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

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: 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: 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: 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: 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: 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.

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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: 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: 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.