AI Application Engineer / Full-Stack Backend Engineer for Claude API Document Validation Workflow

Budget: 5500 USD Deadline: 15 days

Hi Darlene.
My name is Hiroshi, a backend engineer who builds production AI systems.
A compliance report has an income field marked for redaction, the validation workflow masks it neatly in the stored record, and the audit trail looks clean, except the raw unmasked text already went out over the wire to the model before anything was tokenized, so the exposure already happened and no log entry shows it.
I understand what this role is really about.
You are not looking for prompt engineering, you need a real backend system where Claude does semantic judgment and your code owns validation, sensitive data handling, audit integrity and the human approval loop around it.
What I would build is that full workflow.
I pull submissions through your platform API, run semantic audits for contradictions, missing information and unsupported claims, cross check structured answers against narrative text, apply your editorial and business rules, and route each document to auto approval, human review, or back to the submitter, all backed by Postgres, job queues and webhooks.
There is a point worth deciding before the first document is processed.
Masking has to happen before the raw text ever leaves your infrastructure toward the model, not just before storage, because the API call itself is the exposure point, and the audit trail must persist the masked version only, never the original, or the trail becomes the biggest leak surface in the system. The same care applies to confidence thresholds, since a model's stated confidence is not a calibrated probability, so auto approval needs to be tuned against a human labeled sample rather than trusted at face value.
I acted as CTO and auditor at MoveSave, a fintech platform built on an immutable ledger with full traceability and strict credential and sensitive data handling, the same audit and trust problem this workflow needs, and Claude and other LLM APIs are already in production in my work.
I hope we can talk here about the details of the project, and one question would sharpen my plan.
What does the source platform's API look like for writing the approved content back, one endpoint per document type or a shared schema.
The value here is not the rewrite, it is a decision trail a human can trust without re-reading everything, and that is what I would build. Expand

Budget: 6000 USD Deadline: 30 days

Good day!
My name is Oleg, I am the project manager of Business Atlas. We develop and implement autonomous AI ecosystems (over 50 successful cases in the UA/EU markets).
What we will implement for your project:
• Orchestration and Claude API: Building logic through n8n. Our tech specialist Lavr will transform complex semantic checks into stable workflows that will detect conflicts in texts via the Anthropic API.
• PII Masking and Security: Creating a tokenization stage for sensitive data using regular expressions and local scripts before sending the text to the LLM.
• Human-in-the-loop (HITL) Interface: Developing a custom interface (via Retool or similar low-code UI tools), where the moderator will see version comparisons, an AI comment log, and an "Approve" button, with automatic data recording in CRM/external platform.
• Full audit trail: Logging every step (Original -> Masked -> AI-refined -> Human-edited) into a database (PostgreSQL/Airtable).
Why Business Atlas:
We have already built similar "content factories" and AI audit systems.
Estimated terms:
• Price: $4500 – $8500 (final after agreeing on the validation rules matrix).
• Timeline: 4–8 weeks (from MVP development to platform release).
Message me privately — we will discuss the details of your task, and I will suggest the optimal architecture option for your stack! Expand

Budget: 5000 USD Deadline: 15 days

Hi — this maps closely to what I actually build: production Claude API pipelines with human-in-the-loop review, not prompt scripts. I've built document-QA workflows that pull submissions via API, run a structured semantic audit (contradictions, missing/vague/unsupported sections, structured-vs-narrative consistency), apply a rule layer, then either auto-approve on a confidence threshold or route to a human editor — with everything written to an append-only audit trail and pushed back to the source platform.

A few points I'd handle deliberately, since they're where these systems usually break:
- PII/sensitive data: detected and tokenized/masked BEFORE anything reaches the model, the unmask map kept out of the LLM path, and every mask/unmask event logged.
- Rewriting: grammar/clarity/tone improved while preserving the author's original meaning and observations — style standardized without flattening every report into the same generic voice.
- Confidence + routing: explicit scoring so auto-approve, human-review, and return-to-submitter are each traceable to why.
- Editor view: side-by-side original vs AI-revised, accept/reject per change, final decision logged.

Rather than commit to the full ruleset blind, I'd start with a paid first milestone on your real data: pull documents from your platform, run the semantic audit + PII masking, produce an approve/route decision with a working audit log and write-back. That proves the architecture and the output quality on YOUR content before we scale to the full editorial ruleset, queues, roles and thresholds. The rest we lock into milestones from there.

Two things that let me scope precisely:
1. Which platform are documents pulled from and written back to (API docs or the system type)?
2. Could you share one sample document (sanitized is fine) so I calibrate the audit and rewrite rules to your real format?

Examples of work: Freelancehunt · https://guardlabs.online

I can start this week. First working milestone: about 3 weeks (15 working days) to a demonstrable pull -> audit + PII masking -> approve/route + audit-log + write-back on your data; the full build is scoped into milestones from there.

Proposed budget: $5000, structured across milestones as above — the first working milestone first. Timeline: 15 working days to the first milestone. Expand

Budget: 5000 USD Deadline: 30 days

Hello. i have been working with Node.js and Python for more than 9+ years.I'm ready to cooperate.

Budget: 5000 USD Deadline: 21 days

Hi Darlene! I've built this kind of system before — a Claude-based backend that ingests documents via API, runs structured semantic validation against your business rules, masks PII before anything reaches the model, and auto-approves on confidence thresholds while routing edge cases to a human reviewer with full audit logging. The part most people get wrong is the rewrite step — improving clarity and tone without flattening the author's original meaning; I handle that with constrained prompts plus a verification pass. One question that shapes the architecture: does your platform push completed documents to a webhook, or should the service pull them on its own? Can start this week.

Budget: 5000 USD Deadline: 25 days

Darlene, your workflow needs more than prompt tuning — it needs a reliable AI backend. I can design the document pipeline end to end: API ingestion, structured validation, semantic checks, PII masking, confidence scoring, human review, and audit trails. I’ve spent 7 years building web services and leading a dev team, so I can turn this into a production system with clean architecture, secure handling, and clear reviewer actions. Happy to discuss the best setup for your flow.

Budget: 5000 USD Deadline: 27 days

Darlene, this is exactly the kind of system I build: not just AI prompts, but a reliable backend workflow. I’ve worked with Python, FastAPI, PostgreSQL, API integrations, async job processing, and AI-assisted text handling, so I can design the document pipeline, validation logic, audit trail, and human review flow end to end. I also pay close attention to security, access control, and clean data handling. If you’d like, I can outline the architecture and review flow right away.

Budget: 5000 USD Deadline: 25 days

IF the source platform API is documented and accessible, I can take this as a first production MVP for USD 5,000 and about 25 business days. For the full hardened prodction version with deeper compliance controls, larger scale queues, advanced role model and several integrations, I would split delivery into a second phase after we validate volume, API limits and security requirements.

The nuance here is that this should be built as an application, not as a prompt wrapper. I would design a backend pipeline with document ingestion, PII masking before Claude API where required, deterministic rule checks, structured AI outputs, confidence thresholds, reviewer decisions, audit trail, and writeback to the source platform API. The editor should let reviewers compare original and revised text, accept or reject AI notes, edit manually, and finalize the approved document.

Two questions before a precise plan:
> Which external platform will documents be pulled from and written back to?
> What document volume and PII level should we assume - low internal reports, regulated personal data, or security sensitive content?

Relevant Ingello examples:
> https://business.ingello.com/fractal - AI agents and repeatable decision logic for business processes
> https://business.ingello.com/vorfahr - AI and SaaS engineering with production backend logic
> https://business.ingello.com/platforma - related corporate platform architecture with roles and process control

FLH landing:
> https://systems-fl.ingello.com

No magic here - just careful engineering, auditability, and a human review layer where the model should not decide alone =) Expand

Budget: 5000 USD Deadline: 21 days

Hi. Strong fit: Python/FastAPI, Claude API, structured validation workflows, retries, audit logs and deployment. I can deliver the document-validation pipeline with deterministic checks around the LLM and a tested API. Price is negotiable.

Budget: 5500 USD Deadline: 28 days

Hi! Document-validation workflows on the Claude API are exactly what I do. I've
built an n8n content pipeline with a human-in-the-loop approval gate, and I work
with the Anthropic API daily (structured outputs, tool use, PII-safe prompting).

How I'd approach it:
• Ingestion + structured parsing of each submission into a schema (answers,
choices, free-text)
• Validation layer: semantic audits (contradictions, missing/unsupported claims),
configurable business rules, PII detection/masking before the model sees data
• Decision engine: auto-approve above a confidence threshold, else route to a human reviewer; full audit trail (original → tokenized → AI findings → human edits → final)
• Reviewer editor UI + write-back to your source platform via API

Backend on Node/Fastify or Python, Postgres for the audit log, deployed to your
infra with logging/monitoring.

I'd start with a working validation slice on ONE document type so you see quality
fast, then scale the rules. Rough estimate: $5000, ~3–4 weeks in milestones.
Portfolio and a short approach doc on request.
— Mykola Expand

Budget: 5000 USD Deadline: 30 days

I am ready to complete your order with high quality, adhering to all requirements and deadlines.

Budget: 5000 USD Deadline: 21 days

Hi Darlene!

This is close to what I already build: promAI, a FastAPI backend that wraps Google Gemini for batch AI content generation with structured outputs and validation before anything ships (public repo: github.com/slonce70/promAI). And edrsr-ai-server, an async Gemini analysis pipeline with queue/workers and WebSocket delivery. The confidence-threshold pattern you describe - auto-approve above a bar, route to human review below it, log everything - is exactly how I designed the AI-agent logic I've pitched on similar automation projects this week (idempotency keys, correlation IDs, human handoff on low confidence).

What I'd bring: Claude API integration for structured validation output, backend architecture (queues, webhooks, task processing, DB design for audit trails), and rule-based + AI-scored validation layered together rather than trusting the model blindly. PII handling: I've worked with secrets/credential hygiene and CI security scanning (Trivy, pip-audit) on my own infra, but dedicated PII tokenization/masking at the scale you're describing would be new - happy to be upfront about that rather than oversell it.

I'd want a short call to size this properly rather than guess a number blind on a 5-figure-scope project, but as a starting point: an initial working pipeline (Claude integration + structured validation + confidence routing + audit trail for one document type) is realistic in about 3 weeks. Happy to share the promAI/edrsr-ai-server code on a call.

Budget: 5000 USD Deadline: 21 days

Two things I can say that most bids here probably can't: I have a live AI product in production right now (lingovox.online — voice translation bot, paying subscribers, OpenAI Whisper + GPT under the hood), and I've already built the core pattern you need — raw input → structured LLM analysis → confidence-based routing → human review before anything finalizes. That's my lead monitoring pipeline, running 24/7.
For your system:
PII before Claude, not after. Detect and tokenize sensitive fields in a preprocessing layer; Claude only sees masked placeholders. Unmask map stays out of the LLM path. Every mask event is an audit log entry.
Structured outputs, not prose. Claude returns typed JSON per document section — findings, rule violations, confidence score, rewrite suggestion. Approval gate keys on real fields. Auto-approve above threshold, human queue below it, return-to-submitter when info is missing.
Audit trail as a state machine. Original → mask events → AI findings → AI rewrite → human edits → decision → final. Append-only, immutable.
Stack: Python/FastAPI, PostgreSQL, Celery/Redis, Claude API with tool_use, React reviewer UI with side-by-side diff.
One question that shapes the architecture: does your source platform push documents via webhook, or does the system poll/fetch them?
$5,000, phased milestones. First working slice (ingest → PII mask → structured audit → confidence routing + audit log) in ~3 weeks. Ready to start this week.

Budget: 5000 USD Deadline: 30 days

I have experience working with enterprises, specifically debugging the automatic turnover system at a machine-building enterprise, and debugging the sales analysis system at a printing house with a complete restructuring of the product range based on the analyzed data. In addition, I have my own products, particularly the assessment of the quality of call center work with auto-training for managers.

I have created my own applications; here is an example: https://apps.apple.com/ua/app/situ-%D1%8F%D0%B7%D1%8B%D0%BA-%D0%B4%D0%BB%D1%8F-%D0%B6%D0%B8%D0%B7%D0%BD%D0%B8/id6763421487?l=ru

Regarding your task, I am ready to take on the execution according to the requirements and to refine the system logic in the process. Conditions for management and improvement during the system's operation are also possible.

Budget: 5000 USD Deadline: 14 days

Greetings!
Ready to work on your project.
I can build a full document validation automation according to your technical brief.
DM me, we'll discuss all details and nuances, and we'll pick the best soultion for you!

Budget: 5000 USD Deadline: 30 days

Hello. My name is Alexey, and I represent a group of developers – NC-1. For over five years, we have been creating websites, mobile applications, online stores, ERP/CRM systems, and other e-commerce products.
In our team, there is a full stack developer, senior with the necessary experience and knowledge for you.
His stack: Python (3.x), Django, FastAPI, Flask, Pyramid. API and data: RESTful API, web scraping (Requests, BeautifulSoup, Selenium, Scrapy), Pandas, NumPy, asyncio (aiohttp). Databases: PostgreSQL, MySQL. DevOps and tools: Docker, Git/GitLab, GitLab CI/CD, Celery, APScheduler, Pytest, Logging.
Sincerely, Alexey M.

Budget: 5000 USD Deadline: 10 days

Hello!

I am interested in your project. I have over 7 years of commercial experience in Python development and designing complex backend systems. In recent years, I have been actively involved in developing AI applications, automating processes, and building production-ready services using LLM.

I have practical experience in developing systems with:

Python (FastAPI, asyncio, Celery);
PostgreSQL, Redis;
Docker, Linux, Nginx, CI/CD;
integration of external REST APIs and Webhooks;
task queues and asynchronous processing;
building scalable backend architecture;
integration of modern LLMs (Claude/OpenAI), structured response processing, and automation of business processes.

I am particularly close to the architecture of the project itself. I do not view LLM as a "magic function," but rather build a complete pipeline: data acquisition → normalization → validation → application of business rules → AI analysis → confidence scoring → human review → audit → recording the result back via API.

I also understand the importance of:

protecting and masking PII before passing to the model;
a complete audit trail;
separating AI logic and deterministic business rules;
the ability to scale and further develop the system without rewriting the architecture.

I believe that such a project should be implemented immediately as a production service with a clear architecture, testability, and the possibility of expansion, rather than as a set of prompts around the Claude API.

I am ready to discuss the architecture, propose an optimal stack, and a phased implementation plan.

Cost: $5000
Timeline: 5–7 weeks (depends on the volume of the review/editor interface and integrations with the external platform).

I would be happy to discuss the details and get started. Expand

Budget: 5000 USD Deadline: 30 days

Hello! You are absolutely right—this is a serious engineering challenge, not just a prompt design task. Building a production-ready AI validation workflow requires a robust, asynchronous architecture with strict Human-in-the-Loop (HITL) integration, deterministic logic, and absolute data compliance.

As a Python Backend & Infrastructure Engineer, here is how I will architect and deliver this system:

1. Data Ingestion & PII Masking (Security First):
The pipeline will fetch documents via your external API and immediately pass them through a sanitization layer. Using NLP tools, PII and sensitive data will be tokenized (e.g., [PERSON_1]) and securely mapped in a PostgreSQL database. The Anthropic (Claude) API will only process the anonymized text, ensuring complete data privacy.

2. LLM Orchestration & Validation Engine:
I will build an asynchronous backend using FastAPI to handle the processing queues. Claude 3.5 Sonnet will be utilized via strict structured outputs (JSON schemas) to return semantic audits, confidence scores, and identified logical conflicts. Deterministic business rules will be evaluated via Python, keeping the AI focused on semantic quality while code handles rigid logic.

3. Audit Trail & Human-in-the-Loop (HITL) Editor:
Every mutation will be logged: original text -> tokenization -> AI recommendations -> human edits. I will build a dedicated reviewer interface where human editors can see a visual "diff" of the AI's changes, review flagged inconsistencies, and approve/reject revisions. Once approved, the system will re-inject the PII and push the final version back to the external platform.

4. Reliability & Infrastructure:
The entire ecosystem will be containerized with Docker for seamless deployment. My background includes building heavy ETL data pipelines (such as processing complex FAA aeronautical navigation datasets), so handling massive, structured document flows with absolute data integrity and asynchronous stability is my core expertise.

I am ready to review the API documentation of your external platform and sketch out the initial database schema. Let's connect and discuss the exact technical requirements! Expand

Budget: 4990 USD Deadline: 29 days

Good day!
My name is Valentin, and I represent Arctic Web Agency. We are a team that specializes in creating modern and effective solutions for businesses. I can provide examples of our similar work in personal messages. We are ready to take your project to work!

Sincerely,
Arctic Web Team
Freelancehunt

Budget: 5150 USD Deadline: 35 days

If the source platform API is documented and accessible, I can deliver this as a first production MVP for USD 5,000 and approximately 25 business days. For the fully hardened production version—with deeper compliance controls, larger-scale queues, an advanced role model, and multiple integrations—I would propose splitting delivery into a second phase. This would occur after we validate volume, API limits, and security requirements.
The subtlety here is that rather than being a prompt wrapper, this should be developed as an application. Document intake, PII masking prior to the Claude API when necessary, deterministic rule checks, structured AI outputs, confidence thresholds, reviewer choices, audit trails, and writeback to the source platform API are all features of the backend pipeline I would provide. The editor should allow reviewers to make manual edits, accept or reject AI notes, compare the original and altered text, and complete the authorized document.
Prior to a detailed plan, two questions:
- Which external platform will be used to pull and write documents?
- Which PII Level and document volume--low internal reports, regulated personal data, or security--sensitive content should we assume?

Budget: 5000 USD Deadline: 1 day

Good day!

Your project is right at the intersection of AI and backend engineering, and this is the type of tasks I enjoy working on the most.

I am not just involved in integrating LLMs, but in building production-ready AI workflows: integrating models via API, structured processing pipelines, rule-based validation, working with queues, logging, change auditing, integrating with external services, and building robust server logic.

From your description, I envision an architecture like this:
obtaining documents via API/Webhooks;
preprocessing and masking sensitive data;
AI analysis through the Claude API;
rule-based and semantic validation;
confidence scoring;
automatic decision-making or Human-in-the-loop review;
a complete audit trail of all changes;
writing the final version back to the external system.

I primarily work with Python, FastAPI, REST API, PostgreSQL, Docker, AI service integrations, and a modern stack for building AI applications.

I also have experience creating my own AI products and automations, so I understand the difference between a "demo with ChatGPT" and a system that needs to operate reliably in production (you can check the reviews).

If you're interested, I can propose a solution architecture even before development begins to align the entire workflow, validation logic, and integration.

I would be happy to discuss the details. Expand

Budget: 5000 USD Deadline: 1 day

Good day, I am writing on behalf of the company Devoxen. We specialize in the development of AI solutions and complex backend systems. We have extensive experience in creating production-ready AI applications with LLM integration via API, building multi-stage workflows, validation systems, auditing, human-in-the-loop processes, secure data processing, task queues, and integrations with external services. We implement scalable architecture with a clear separation of AI logic, business rules, and API layers, ensuring the possibility of further system expansion without complete redesign. We will do this without unnecessary questions and time costs. We also provide a guarantee and, if desired, further support. We can start working on your project immediately after discussing the technical specifications.

I suggest moving to private messages for a more detailed dialogue.

Budget: 4800 USD Deadline: 30 days

Darlene, this is exactly the kind of AI system I build: not just prompts, but a secure backend workflow with validation logic, audit trails, and human review. I’ve built Python/DRF systems with JWT/OAuth, complex API integrations, and secure data handling for sensitive web apps. I can design the Claude-powered review pipeline, PII masking, confidence-based routing, and editor workflow so documents are analyzed, improved, approved, or escalated reliably. Happy to discuss the architecture.

Budget: 5000 USD Deadline: 30 days

Building an AI quality-assurance layer that audits documents, catches contradictions and gaps, rewrites for clarity without flattening the author's voice, and routes low-confidence cases to a human — with a full audit trail — is exactly the kind of AI application I build. And I build them as multi-agent systems with specialized roles and independent review stages, not single-prompt calls.

Two production systems that map directly to yours:

— A multi-agent document validation/refinement pipeline (~17.5K LOC) on Claude, genuinely multi-agent: specialized agents orchestrated as a pipeline — a generator, an INDEPENDENT evaluator that scores against a fixed rubric (the author never grades its own work), and a verifier that fact-checks every claim against the source. Plus RAG over a knowledge base, rule-based lint gates, and human-in-the-loop confirmation before anything ships. That's structurally your workflow: validate → refine → score → auto-approve or route to a human → audit trail — with the reliability coming from the role separation, not the prompt.

— A multi-agent LLM content pipeline (Claude + a second model behind one unified llm_client): strict typed schemas (Pydantic), LLM-driven classification/validation, structured persisted output — ~5K+ LOC Python with pytest and an architecture doc.

Also relevant: a ~10K-LOC async multi-LLM orchestration backend (FastAPI, streaming, multi-backend) for the queues/latency side; card tokenization + encryption for PII masking/secure-handling; and a bank-style typed-transaction ledger (before/after on every event) — the same shape as your audit trail.

Stack-wise I'm strongest as the AI-application + backend architect (multi-agent orchestration, Claude integration, structured outputs, rule-based validation, queues, secure data, audit trails). The React reviewer UI I can build or pair with a frontend dev — your call.

Two questions: which external platform do you pull/write documents from (the API)? And do you already have the editorial rules + confidence thresholds, or is designing those part of the scope? Expand

Budget: 5000 USD Deadline: 1 day

Hi,

Your project is exactly the type of AI application I specialize in.

I recently earned the official **Claude Certified Architect – Foundations** certification from Anthropic, which focuses on designing production-ready AI systems rather than just prompt engineering. The certification covers enterprise architecture, Claude API integration, Prompt Caching, Model Context Protocol (MCP), Tool Use, large-context optimization, structured AI workflows, and building secure, scalable AI applications.

Based on your requirements, I can design and implement the complete solution, including:

* Claude API integration and AI orchestration
* Document validation and semantic consistency checks
* Rule-based validation with confidence scoring
* Human-in-the-loop review and approval workflows
* Secure handling of sensitive data (PII masking/tokenization)
* Audit trails and document versioning
* Integration with external APIs and document platforms
* Production-ready backend architecture and scalable processing pipelines

I understand that this project is much more than prompt engineering—it requires building a reliable enterprise AI system with robust backend architecture, security, and workflow automation. That is exactly the type of work I enjoy.

I'd be happy to discuss your current architecture, answer any technical questions, and explain how I would approach the implementation.

Looking forward to hearing from you.

My cert: https://www.credly.com/badges/3df97b6b-e468-42b9-9bd6-c1f78aa309fc/linked_in?t=thmz48

Best regards,
Ihor Expand

Budget: 5000 USD Deadline: 15 days

Hi,

The line that matters most in your spec is "not someone who only writes prompts" — because everything hard here lives downstream of the prompt. Getting Claude to spot a contradiction is the easy 20%. The other 80% is making a non-deterministic model safe enough to auto-approve documents in production, with an audit trail that holds up when someone later asks "why was this one approved?"

Three parts I'd treat as the real engineering, not afterthoughts:

— PII never reaches the model in the clear. Detection and tokenization happen before the Claude call, and the masking itself is a logged audit event — so sensitive data is out of the payload and you can prove it was. Detokenization only on the safe side, after processing.

— Claude returns a structured contract, not prose. Per-section findings, rule violations, and confidence scores come back as validated JSON (tool-forced output), so the approval gate keys on real fields — auto-approve only above threshold, everything borderline routed to a human. The model never silently approves what it's unsure about.

— The rewrite is constrained and diffed, not blind. It fixes grammar, clarity and consistency while preserving the original meaning and observations, and every change is shown side-by-side so a reviewer accepts or rejects per finding. That's how you standardize tone without over-normalizing every report into the same generic voice.

Around that: queue/job processing for the document pulls, idempotent write-back so a retry never double-posts an approval, and an append-only event log capturing every state — submission, mask events, AI findings, AI rewrite, human edits, decision, final version.

This is the stack I actually build on — Anthropic API in production with structured outputs and a guard layer that stops the model inventing facts, plus durable queues and idempotency so nothing is lost or double-processed. Example of that reliability discipline: github.com/novellocaitlin13-ui/melody-platform.

Two things that shape the architecture: does the source platform push events (webhooks) or do we poll it for completed submissions? And what PII classes are in scope — names/contacts, or regulated/security-sensitive data that needs stricter handling? That decides a lot. Happy to go deeper in private. Expand

Budget: 5000 USD Deadline: 30 days

Hello.

This is exactly the type of tasks I work on: not just LLM integration, but building full-fledged AI systems with a reliable backend architecture. I have experience in developing AI agents, complex API integrations, workflows involving humans (Human-in-the-Loop), validation systems, logging, task queues, and secure data processing.

I would build a solution with a clear pipeline: document retrieval → masking of sensitive data → multi-stage AI validation and application of business rules → confidence assessment → review interface → complete audit of changes → recording the result back via API.

I work with Claude API, OpenAI, FastAPI, Go, PostgreSQL, Redis, Docker, and modern AI orchestrators. I am ready to propose an architecture, estimate timelines, and implement a production-ready system.

Budget: 5000 USD Deadline: 34 days

Hello Darlene.
The real risk is trust: reviewers must see what was changed, why it was changed, what was masked, who approved it, and what final version was written back.
I would build this as a document state machine, not a one-shot AI call:
original submission - sensitive data detection/masking - rule-based validation - Claude structured review - AI rewrite suggestions - human review - approval/rejection/clarification - API writeback.
Key parts I would implement:
External API intake: fetch completed documents or receive them via webhook, normalize the structure, store the original submission and metadata.
Validation layer: deterministic business rules plus Claude analysis for contradictions, missing sections, weak or unsupported claims, formatting/tone rules and consistency between structured answers and narrative text.
PII/sensitive data handling: detect and mask/tokenize sensitive fields before Claude where needed. Each masking event is stored in the audit log.
Claude layer: structured output, not free-form text. Findings, reasons, confidence, rewrite suggestions, clarification requests and approval recommendations should come back as typed data.
Reviewer UI: side-by-side view of original vs AI-revised content, accept/reject suggestions, manual edits, final approval, rejection or return for clarification.
Audit trail: original document, masking events, AI findings, AI rewrites, human edits, final decision and final approved version stored separately.
API writeback: approved content is sent back to the source platform.
Relevant experience: I have built backend-first operational systems with documents, roles, statuses, audit trail, AI workspace, structured AI outputs, queues, controlled tool/function calls and human-in-the-loop logic. I treat AI as one controlled part of the workflow, not as the whole system.
Stack: Python/FastAPI or Node/NestJS, PostgreSQL, Redis/queues, Claude API, React/TypeScript reviewer UI, Docker.
I attached relevant project examples. The closest patterns are AI Operations / CRM Platform and AI Content / Automation Platform: roles, documents, audit trail, structured AI outputs, human review and safe tool/function calls.
One question that affects architecture: does the source platform send completed documents via webhook, or should the system poll/fetch them through API? Expand

Budget: 5000 USD Deadline: 60 days

Hello, Darlene!

I've analyzed the requirements in detail. The scope breaks into 4 core roles:

Business Logic Architect — validation rules, editorial guidelines, confidence thresholds, a document scoring system for human reviewers (if needed), plus a hybrid RAG layer with regulatory/legal data

AI/Prompt Engineer/Reviewer — designing Claude's behavior, structured outputs, semantic audit logic

Fullstack Developer — API integrations, queues, database design, audit trail, reviewer UI

Security/PII Controller — masking sensitive data before it reaches Claude's API

To protect sensitive data, I'd propose a two-model setup: a lightweight local model masks user IDs and other sensitive info before it reaches Claude, then restores it after processing. This model doesn't need to be highly capable — just accurate NER/tokenization — so it adds little overhead. Semantic analysis, auditing, and rewriting stay with Claude as the top-tier model.

I can cover all 4 roles myself using AI agents, but tight timelines risk gaps in the model pipeline, architecture, or confidentiality controls.

If timeline matters most, the optimal setup is 2 experts with dual roles:

Expert 1: Business Logic Architect + AI/Prompt Engineer/Reviewer
I'd take this myself — nearly 2 years as a trainer/reviewer for top-tier LLMs at Scale AI and Meta AI (RLHF, safety evaluation).

Expert 2: Fullstack Developer + Security Controller
I'd suggest selecting this candidate together — key is covering the local masking pipeline in front of Claude's API.

The ideal setup is 4 separate experts, each fully dedicated to their domain.

A few questions:
— Document domain (real estate, finance, other)?
— Confidentiality level required (NDA, GDPR, other)?
— What testing depth do you need for modules and the full product?

Let's discuss details in a private chat on the platform.

P.S. Rate and timeline on this bid are placeholders required by the platform. Actual terms will be set after we agree on project details. Expand

Budget: 5500 USD Deadline: 30 days

Good day!

Thank you for the detailed project description. I see that this is not a task at the level of ordinary prompt engineering, but a full-fledged AI-powered document validation and approval workflow with backend architecture, Claude API, human-in-the-loop review, auditing, sensitive data processing, and business rules.

I can take on the AI workflow architecture and validation layer of the project:

— designing the document validation logic;
— the structure of the AI audit and validation criteria;
— Claude prompt system and structured output logic;
— confidence scoring schemes;
— rules for identifying contradictions, omissions, unverified claims, and inconsistencies;
— workflow for human review: approve / edit / reject / send back;
— requirements for PII masking / sensitive data handling;
— technical specifications for backend/full-stack developers;
— testing the logic on document examples.

It is important to clarify: a full production backend, secure database, queues, deployment, encryption, access control, and frontend reviewer interface should be handled by a full-stack/backend developer. I can either prepare a detailed technical assignment for them or work alongside the developer as the AI workflow / product architecture lead.

For the first stage, I would suggest creating an MVP specification + AI validation prototype:

1. describe the workflow architecture;
2. create a validation rules matrix;
3. prepare the Claude prompt system;
4. develop a structured output schema;
5. test the logic on several document examples;
6. prepare the human review flow;
7. hand over the technical specifications for backend application development.

The cost of this stage: $5000.

After this, we will be able to more accurately estimate the production development. Expand

Budget: 5000 USD Deadline: 21 days

Hi there,
I am a Senior Full-Stack Engineer specializing in building production-ready AI applications with robust backend architectures. Seeing my recent successful AI-restaurant SaaS project deployment attached in your job description confirms that my stack and engineering standards perfectly align with your vision.
Here is my concise architectural proposal for your Claude-powered validation workflow:
* Robust Queue Architecture: I will implement a resilient job processing layer using Redis and BullMQ to handle webhook ingestion, document pulling, and upstream/downstream API syncing safely without data loss.
* Structured AI Outputs: Using Anthropic's native Tool Calling (JSON mode via Zod/Pydantic schemas), Claude will return strict validation data—extracting logical contradictions, missing data, and generating a dynamic confidence score.
* PII Masking & Strict Audit Trails: Before hitting the Anthropic API, a local regex/NER module tokenizes sensitive data (e.g., [PII_MASKED]). Every single transformation, AI recommendation, and human edit will be logged in PostgreSQL for a secure audit trail.
* Human-in-the-Loop Editor UI: Built with Next.js and React, the interface will feature a side-by-side diff view (Original vs. AI-refined text), allowing editors to instantly accept changes, override AI flags, or trigger automatic revision requests back to the source platform.
Tech Stack:
* Backend: Node.js (NestJS/Express) or Python (FastAPI), Redis, PostgreSQL (Prisma/SQLAlchemy).
* Frontend: Next.js, Tailwind CSS.
* AI Ecosystem: Anthropic SDK (Claude 3.5 Sonnet).
Timeline & Budget:
* Estimated Turnaround: 3-4 weeks.
* Pricing: Fully open to alignment within your $5,000 budget based on the specific API endpoint requirements.
You can inspect my code quality and architecture style at https://mcarrow.online.
Let's hop on a brief 10-minute call to map out the incoming document structure (JSON/PDF) and define your exact confidence threshold rules!
Best regards,
Serhii Makarov Expand

Budget: 5000 USD Deadline: 21 days

Hello! My name is Nina, I am the manager of the technical team. Your task is a classic LLM-driven Agentic Workflow with Human-in-the-Loop, and we thoroughly understand how to design such a system so that it does not fall apart in production. We do not engage in "prompt engineering in a vacuum," but build a fault-tolerant backend architecture.

How we will implement your project:
1. Security and PII (Personally Identifiable Information):
Before sending text to the Anthropic API, we deploy a local layer in Python (based on custom NER models / Presidio) for detecting, tokenizing, and reversibly masking PII. Claude will receive anonymized text.
2. Semantic audit and validation:
Instead of one giant prompt, we will break the pipeline into a chain of agents (LangGraph / FastAPI + Celery for asynchronous queues). The first agent checks for logical conflicts, the second checks formatting and tone, the third generates structured JSON with a confidence score through Anthropic Tool Calling.
3. Backend and Audit log:
We design a strict relational database (PostgreSQL) for versioning storage: Original -> Masked -> AI-Suggested -> Human-Edited -> Approved. A complete deterministic audit trail.
4. Human-in-the-Loop interface:
We will create a lightweight, reactive frontend (React), where the validator sees a side-by-side UI (comparing the original text and AI edits with highlighted changes, like in Git diffs) and can apply, reject, or add comments with one click.
5. Integration:
We will implement reliable handling of webhooks from the third-party platform with a retry policy mechanism in case of API failures or Claude's rate limits.

Our stack for this task:
Backend: Python (FastAPI), Celery/Redis (heavy task queues), PostgreSQL (version history and audit).
AI Layer: Claude 3.6Sonnet/4.8 Opus API (Structured Outputs, Prompt Caching for budget savings), custom local masking scripts.
Frontend: React (intuitive moderator panel).

We are ready to start with an architectural call to approve the data flow and reviewer interface. When would it be convenient for you to discuss the details?

Best regards,
Nina, development team manager. Expand

Budget: 5000 USD Deadline: 33 days

I can help with this

In chat I will give you a clear plan, show relevant experience so we’re sure it’s a match, then get it done without the usual freelancer back-and-forth.

Budget: 4900 USD Deadline: 1 day

Hello! Your task is a classic challenge for an AI/Backend Engineer, not for prompt engineers. I have about 3 years of experience in backend architecture development and creating LLM services (Agentic Workflows) ready for production.

My competencies for your project:
Working with Claude API (Anthropic): I actively use Claude (especially the Sonnet family) due to its best understanding of context and logic. I set up a clear Structured Output (JSON Mode/Tool Calling) for semantic auditing and confidence score evaluation.

Backend and data processing: I design robust logic in Python (FastAPI/Microservices) with task queues (Celery/Redis) for asynchronous analysis of heavy documents.

Security and PII: I implemented personal data masking before sending to LLM using regular expressions and local models (for example, using libraries like Presidio).

Human-in-the-loop: I understand how to build an architecture where a document, after AI validation, is marked with a status based on scoring and goes either to a webhook for auto-approval or to a queue for human verification (with a complete audit log of changes).

I create not just a bot, but a stable system with logging, document versioning, and flexible business rules.

Feel free to write - let's discuss! Expand

Budget: 5000 USD Deadline: 30 days

Hi Darlene,

You drew the right line: production system, not a prompt. So here's how I'd actually build it, not a restatement of your list.

A document moves through a state machine, not one AI call. It's pulled from the source platform into a queue. Before anything reaches Claude, PII and sensitive data are detected and tokenized, so the model only ever sees masked placeholders while the real values sit in a separate vault, re-inserted only in the final approved output. Confidential data stays out of the API by design.

The audit stage runs Claude with structured tool-use outputs rather than free text, so every result is a typed object: findings, the exact rule or contradiction each maps to, a confidence score, and a rewrite that keeps the author's meaning intact. That structure is what makes routing reliable instead of parsing prose. Confidence decides the path: auto-approve when high, human editor when uncertain, back to the submitter when something's missing, with generated notes explaining why.

Every step, original, masking events, AI findings, rewrite, human edits, final decision, lands in an append-only audit log, so the trail is immutable. The reviewer works in a diff view, accepting or rejecting each change before it's written back to source.

This is my core work: Claude and OpenAI APIs, Python and FastAPI, structured outputs, queues, secure integrations. I build the system around the model, which is exactly the distinction you're screening for.

I'd suggest Phase 1 delivering the spine, pull, mask, structured audit, confidence routing, audit log, as a working testable core, then Phase 2 for the editor UI, approval workflow, and write-back. Overall in line with your 5000 USD, phased so you see the engine run before the full build. Track record: 36 projects, all completed, 10/10.

One question: which platform are documents pulled from and written back to, and does it expose a REST API with webhooks, or polling only?

Revaz Expand

Budget: 5000 USD Deadline: 50 days

Hello!

You have an interesting project and I am definitely the one who can do it qualitatively, quickly and without errors.

You have a detailed description of the project, but we should discuss all the tasks in detail before starting and decide where we will start and what final result you expect, it is also worth understanding in which environment to embed the entire project, it is possible to perform additional integrations so that the user of the system would be happy to use it.

As for my experience, it is all described on my website: https://synvolve.solutions/cases/

When is it convenient for you to discuss the project in more detail?

Budget: 5000 USD Deadline: 45 days

Hello! I can complete your order as I have experience in designing production-ready corporate AI applications, building complex backend architectures, secure data masking systems (PII), and deep integration with the Anthropic Claude API (including JSON output logic / Structured Outputs and Prompt Chaining).

Before responding to the vacancy: a brief overview of my other AI projects (Agent Database and Fairy Tales)
Automated AI Agent Database (SMM & Management): I developed complex agent architectures where an orchestrator (based on LangGraph / CrewAI) coordinates the work of several specialized agents. One agent monitors trends and collects analytics, another generates content in the company's Tone of Voice, a third manages publication queues and cross-posting on social media, while the management agent controls KPIs and closes transactional tasks in the CRM.

Application for generating children's fairy tales: I created a mobile/web application where AI generates personalized therapeutic fairy tales for children. The user selects the child's name, favorite characters, and a moral theme (for example, "how to stop being afraid of the dark"). The system generates a unique plot through sequential prompts, breaks it down into scenes, automatically creates prompts for generating illustrations (Midjourney/Flux), and compiles a finished interactive audiobook (with text-to-speech narration).

Architectural approach to your validation and quality control system
To create a robust B2B document processing system "without hallucinations," I propose the following architecture:

Backend platform and queues: Python (FastAPI) as the fastest asynchronous framework for working with AI pipelines, text processing, and data masking. For background processing of long documents (parsing, chapter analysis), Celery + Redis/RabbitMQ is used, ensuring isolated task execution without API hanging.

Security and PII masking (Zero Trust Data): Before sending text to the Claude API, the document goes through a local pipeline based on the Microsoft Presidio library or custom regular expressions. Names, addresses, license numbers, phone numbers, and sensitive security data are automatically tokenized (for example, replaced with [PERSON_1], [ID_NUMBER_2]). De-masking (returning real data) occurs exclusively within your database before the final record to the output platform.

my Git: https://github.com/onyx144 Expand

Budget: 5000 USD Deadline: 50 days

Hello ⭐️! I am a highly qualified web developer with over ✅ 7 years of experience in development and modern web technologies.

Recent projects:
✔️https://homenly.com
✔️https://confidence-tech.com
✔️https://homexcrm.com
✔️https://omgfirms.com
✔️https://skyhigh-lviv.com/
✔️https://sweet-sdpearls.de/
✔️https://novobudova.pro
✔️https://aquahub.org.ua
✔️https://boostylabs.com
✔️https://ol.zp.ua
✔️https://emporio-sports.cz

My stack includes:
✅ Frontend: HTML, CSS, JavaScript, React.js, Vue.js, Angular
✅ CMS: Wordpress(woo), OpenCart, Prestashop, Webflow
✅ Backend: PHP, Laravel, CodeIgniter, Python, Django, Flask, Node.js, Next.js
✅ Databases: MySQL, PostgreSQL
✅ Other skills: Git, API integration

Why choose me?
✔️ Fast and quality execution: Your tasks will be completed on time and with impeccable quality.
✔️ API integration: Need seamless integration of third-party APIs? I will take care of that.
✔️ Custom solutions: I take your ideas and turn them into successful web solutions tailored to your business needs.

Let's collaborate and create something amazing together! Expand

Budget: 5000 USD Deadline: 28 days

I am a Full-Stack AI Application Engineer, and this project describes exactly the kind of strict, production-ready AI architecture I specialize in. I completely agree with your approach: relying solely on prompt engineering is not enough for enterprise-grade validation. You need deterministic backend logic, structured LLM outputs, middleware for data sanitization, and a robust Human-in-the-Loop (HITL) UI.

Here is how my technical background aligns with your architecture requirements:

Strict AI Validation & HITL Workflows: I recently built a B2B AI agent for a construction/insulation company where hallucinations were strictly unacceptable. The system had to cross-reference user requests with strict technical documents and math formulas, ultimately generating a "legally clean" draft that was routed to a human technologist for final approval. I know how to build the exact routing logic (Auto-Approve vs. Human Review vs. Reject) you are looking for.

Claude API & Structured Outputs: I have extensive experience integrating advanced LLMs (Claude, Gemini, OpenAI). For this document validation system, I will utilize Claude's API with strict JSON schema enforcement to ensure the model returns discrete data points (e.g., confidence_score, flagged_issues, revised_text) rather than just raw conversational text.

PII Masking & Secure Data Handling: I design AI pipelines where sensitive data never touches the LLM blindly. I can build a preprocessing middleware layer (using regex pipelines or local NLP tokenizers) to identify, mask, and replace PII with tokens (e.g., [USER_NAME_1]) before the payload is sent to the Claude API, and unmask it upon return.

Full-Stack Capabilities (The Editor Workflow): I don't just build the backend. Using Next.js and React, I can build the complete final-decision dashboard for your human reviewers. This includes side-by-side diff viewers (Original vs. AI-Revised), audit trail logs, and one-click approve/reject/edit functionality.

Backend & Audit Trails: I build robust backends (Python, Node.js) heavily utilizing PostgreSQL/Supabase. Every single state change—from the original API pull, the PII masking event, the Claude response, to the human edit—will be securely logged in a relational database to ensure a 100% transparent audit trail.

I am highly experienced with webhooks, job queues, and API integrations for pulling/pushing data from external platforms. I prefer building systems that run 24/7 autonomously and fail gracefully. Expand

Budget: 5000 USD Deadline: 30 days

Hi, Dalene
This project is a great fit for my experience building production AI applications where LLMs are combined with backend workflows instead of being used as standalone chat tools.
I would build the system using Next.js, NestJS, PostgreSQL, Redis, Claude API, and a queue-based architecture with structured AI outputs, confidence scoring, audit trails, and human approval workflows.
The workflow would handle document ingestion, PII masking, semantic validation, business rule enforcement, AI rewriting, approval routing, version history, and synchronization back to your source platform through APIs.
The review interface would let editors compare original and AI-generated content, accept or reject changes, make manual edits, and finalize documents before approval.
The architecture keeps validation rules, prompts, and approval logic modular so the platform can evolve without major code changes.
This is the type of AI system I enjoy building because success depends on reliable engineering, secure data handling, and workflow design rather than prompt engineering alone.
I'd be happy to help build it end to end.

Budget: 5000 USD Deadline: 30 days

Good day. I can design and develop an application for work processes. I have all the necessary skills. I will write in the Go language. Everything will work quickly and efficiently.

Budget: 4800 USD Deadline: 25 days

This is squarely my area — I build production AI backends around the Claude API, not just prompts.

Here's how I'd architect your validation workflow:
• Ingest completed documents via API/webhook → queue for processing
• PII layer first: detect, tokenize/mask sensitive data before it reaches the model
• Claude semantic audit: structured JSON output — contradictions, missing sections, unsupported claims, structured-vs-narrative mismatches, confidence scores per issue
• Rule engine: apply your editorial/business rules on top of the AI pass (deterministic, auditable)
• Decision router: auto-approve above a confidence threshold, else route to human review or return to submitter
• Human-in-the-loop editor: reviewers compare original vs revised, accept/reject AI edits, finalize
• Full audit trail: original → masked events → AI findings → AI rewrite → human edits → decision → final version, written back to your platform via API

My proof: I've built and deployed AI-integrated backends (Claude API, structured outputs, API integrations, PostgreSQL, auth/roles) end-to-end.

To de-risk this for you, I'd propose starting with a paid proof-of-concept: one document type, the ingest → PII-mask → Claude audit → confidence-scored findings slice. You see real output on your data before committing to the full build.

Questions: which external platform are we integrating with (API docs?), what document types, and do you have the validation rules defined or should we formalize them together? Expand

Budget: 5200 USD Deadline: 30 days

Hello. I will do everything you require. You can write to me in private messages for further discussions. I am one of the developers of the LLM model GPT, specifically I worked at OpenAI and helped in training the AI. I have a good understanding of machine learning. As for coding, I know many languages like Python, C#, C++, Java at a high level. I can also handle front-end development.

Budget: 4800 USD Deadline: 30 days

Hello! This project is the exact definition of my core expertise. I am an AI Systems Engineer specializing in building production-ready LLM applications, Python backends, and robust Human-in-the-loop (HITL) workflows.

I completely agree with your approach: reliable AI applications require strong backend architecture, not just prompt engineering. Here is how I plan to architect and build your Document Validation Workflow using Python (FastAPI + Celery/Redis) + React + PostgreSQL:
Secure Ingestion & PII Masking: An asynchronous worker (FastAPI + Celery) pulls documents via Webhooks/API. Before sending payloads to the Anthropic API, a Python pipeline (using custom Regex/SpaCy NLP models) detects and tokenizes PII/sensitive data, replacing them with secure masks (e.g., [CONFIDENTIAL_NAME_1]) to ensure strict compliance.
Claude API Orchestration & Semantic Audit: I will utilize Claude 3.5 Sonnet with strict Structured Outputs (JSON mode). The prompt architecture will enforce a multi-step evaluation: Logical Consistency check, Quality Scoring, and Markdown Rewrite. Claude will return a structured JSON containing the enhanced narrative, a confidence threshold score, and specific validation flags.
Automated Routing & RLS Database Design: In PostgreSQL, documents are state-managed (Pending, Auto-Approved, Flagged for Review, Rejected). If the AI confidence score drops below your business threshold or high-risk conflicts are flagged, the system routes the entity to the React review queue.
React Editor & Audit Trail Interface: I will build a lightweight, high-performance React dashboard for human editors. It will feature a side-by-side Diff-Viewer (Original vs. AI-Enhanced Narrative), interactive validation notes, and single-click approval actions that write tokenized events back to the secure database audit trail.
Downstream API Sync: Once finalized by a human or auto-approved, a background worker handles the reverse tokenization (restoring masked data securely) and pushes the verified payload back to your source platform.

I have deep experience with Anthropic's API ecosystem, asynchronous task processing, and database transaction tracking. I am ready to design a scalable, enterprise-grade workflow for you.

Let's connect in PM, I would love to share my architectural thoughts on confidence scoring and start working on the MVP! Expand

Budget: 18000 USD Deadline: 45 days

we already have a practically ready similar AI document validation workflow that can be adapted and launched quickly for this case...
i am here and can discuss the scope on the platform now =)

for the budget - 5000 USD is realistic only for a narrow proof of concept with Claude API checks and a basic review screen.
for a production MVP with API intake, rule validation, PII masking, audit trail, reviewer interface, approval logic, and writeback to the source platform, i would estimate from 18000 USD and about 45 working days.

WE would build it as a backend workflow with document ingestion, queue based processing, deterministic validation rules, Claude structured outputs, confidence scoring, secure storage, and a human review panel.
the main thing is not to let the model become the whole system - the AI should be one controlled part of a clear approval pipeline.
сmall joke from engineering life - the prompt is not architecture, even if it looks very persuasive at 2 am =)

relevant examples from Ingello
- https://business.ingello.com/vorfahr - AI and automation logic around business workflows
- https://business.ingello.com/fractal - agent based automation and structured process design
- https://business.ingello.com/platforma - complex platform architecture with roles, workflows, and business logic

main Ingello Systems page for FLH
- https://systems-fl.ingello.com

before finalizing the estimate, i would clarify 2 things.
- which external platform should we pull and write documents to, and does it already have stable API and webhooks
- what volume and sensitivity level should we design for - number of documents per day, PII types, encryption requirements, and retention rules

if needed, we can start with a short technical design plus clickable reviewer prototype, then move into implementation without overcomplicating the first stage. Expand

Budget: 5000 USD Deadline: 2 days

Hello. My approach to this project will focus on developing a fault-tolerant microservices architecture that ensures effective integration of the Claude API for intelligent validation and processing of documents with structured outputs. Special attention will be given to implementing comprehensive data security mechanisms, including PII tokenization, as well as developing an intuitive interface for human-in-the-loop verification and a full audit system for all changes and decisions. I have successful experience in deploying similar AI-driven solutions, which will allow the use of ready-made architectural templates and developments to significantly accelerate the project and ensure high quality. I propose to discuss all implementation details, final budget, and timelines in private messages.

Budget: 4900 USD Deadline: 30 days

aDarlene, it sounds like you need a robust AI-powered system to automate the validation, refinement, and approval of documents, acting as a smart QA layer before human review. The core challenge is building a production-ready backend that intelligently processes submissions, applies business rules, and ensures data quality using Claude API.

I'll design a workflow to pull documents via API, where Claude analyzes content for issues, applies your specific business rules, and improves quality. Then, it will route documents for automatic approval or human review based on predefined criteria. This will include strong backend logic, secure data handling, and an audit trail to track all AI actions and human interactions.

Could you share more about the 'external platform' where documents are pulled from, specifically its API capabilities?

Budget: 5000 USD Deadline: 14 days

You need a production-grade AI pipeline that validates, refines, and routes documents through approval — not a chatbot wrapper, but a real backend system with audit trails, PII handling, and human-in-the-loop logic.

Here's how I'd build it: First, a document ingestion layer that pulls submissions via external API, runs a PII detection pass (presidio or a custom regex+NER layer), tokenizes sensitive fields before anything touches Claude. Second, a structured validation engine using Claude's API with tool_use — each validation rule (semantic consistency, missing sections, tone, business logic) maps to a discrete check that returns structured JSON with confidence scores and specific failure reasons, not freeform text. Third, a state machine for routing: auto-approve above threshold, queue for human review otherwise, or generate a revision request back to the submitter — with every state transition logged immutably (Postgres + event log table) for the full audit trail.