Development of an AI agent for lawyers

we already have a practically ready foundation for such an AI solution, we can quickly adapt it and launch it for a law firm, I suggest discussing it here on the marketplace, I am available ))
the benchmark for MVP is from 20,000 dollars and 10-14 weeks, more precisely after a brief clarification of the sources of law, the volume of documents, and the requirements for placement.
We have experience with AI agents, RAG, vector search, roles, cabinets, action logs, reports, and systems for sensitive data.
Your stack option is fine - Python + FastAPI, React + TypeScript, PostgreSQL + pgvector, Docker, RAG through LangChain or LlamaIndex, Claude API or a mixed secure architecture.
I would build the MVP through separate modules - uploading and OCR of documents, extracting the structure of contracts, risk scoring, a database of legislation and case law, a chat with citations, checking the response before showing it to the lawyer.
An important nuance - to minimize hallucinations, references to specific fragments of the document and sources of law are needed, plus a separate layer for response verification, not just a single query to the model.
For confidentiality, I would include encryption, roles of admin-lawyer-assistant, 2FA, action logs, masking personal data before the AI API, and an option to host critical parts within your perimeter.
Question - do you already have sources for legislation and case law that can be indexed, or do they also need to be selected and updated automatically?
Another question - can documents be processed through Claude API after masking, or is a fully self-hosted scheme without sending texts outside required?
… Similar examples:
- https://business.ingello.com/vorfahr - AI/SaaS with automation and production logic.
- https://business.ingello.com/fractal - agent automation of complex workflows.
- https://business.ingello.com/lita - medical product with sensitive data, roles, and cautious access logic.
The main profile of Ingello for FreelanceHunt - https://systems-fl.ingello.com/ua
Overall, it's fine to start with MVP, but it's better not to skimp on the architecture for response verification - in legal AI, accuracy matters more than effectiveness, here the old wisdom works literally - check seven times, show the client once ))

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Hello, Ostap!

Many here suggest building a system on builders (n8n/Make) or changing the backend to ready-made BaaS solutions, but to maintain attorney-client privilege and enable full On-Premise deployment, your choice of stack is the only right one.

I specialize in Python development of AI systems under heightened data isolation requirements and precise recognition (OCR).

How I will address the main challenges of your MVP:

1. Confidentiality and PII masking (Critical point):
… No contract will be sent to the Claude API in raw form. I will implement a local NLP preprocessing layer (via Microsoft Presidio or custom NER models). Names, amounts, addresses, and EDRPOU will be locally replaced with tokens (e.g., [COMPANY_A], [PERSON_1]). The AI analyzes the anonymized text, and the backend returns real data during the generation of the final PDF report.

2. Minimization of hallucinations (Retrieval-Only):
Claude 3 (Opus/Sonnet) works great with Tool Use (Structured Outputs). The model will be strictly limited by the system prompt: it does not generate text freely but returns a Pydantic/JSON schema, where each conclusion or identified risk must contain an array of citations (exact reference to a contract clause or legal paragraph). The rule is: "no citation — no answer."

3. OCR and structure issue:
Standard PDF parsing merges everything into one paragraph. I will use a hybrid approach (pdfplumber for text-based PDFs and Tesseract with ukr-traineddata for scans) with layout analysis algorithms to preserve the legal hierarchy (sections, clauses, subclauses).

Responses to your points:

Experience and technologies: Python, FastAPI, LLM integration (Claude API, OpenAI), Docker, PostgreSQL (pgvector). I have extensive experience with complex OCR pipelines and hardware isolation of data streams.

MVP estimate: About 5–7 weeks of intensive development (backend core, parsers, masking, RAG search, basic admin panel).

Budget: Approximately $2500 - $3500 (or equivalent in UAH). This is a real price for a secure custom backend without "crutches" in the form of low-code.

One clarifying question: Where do you plan to source the legislation database for vector search? Will it be exports from open registers, or is there a plan to integrate with an API (e.g., LIGA:ZAKON)?

I work for results and am ready for revisions to meet strict deadlines.

I am ready to discuss the architecture in private messages!

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The description looks very interesting, especially the emphasis on minimizing hallucinations through RAG and source verification. For legal cases, this is truly critical, as the value of the system directly depends on the accuracy and ability to substantiate each conclusion with references to legislation or case law.

I have practical experience in building AI solutions based on Claude and ChatGPT, creating AI assistants, automating business processes, integrating through APIs, and designing AI workflows using Make.com, Voiceflow, and CRM systems. I have also worked on solutions where AI analyzes input data, makes decisions based on context, and interacts with users through a chat interface.

For such an MVP, I would recommend building the architecture around the RAG approach with a separate vector storage for legislation and case law, mandatory citation of sources in responses, and an additional layer of validation of results before showing them to the user. This significantly reduces the risk of inaccurate legal conclusions.

In terms of functionality, I see the most complex parts as OCR of Ukrainian documents, quality extraction of legal risks, and maintaining an up-to-date database of legislation. These modules require the most attention at the MVP stage.

I would estimate the MVP of this level to take about 8–12 weeks of development after detailed processing of the technical specifications and architecture.
…
I am curious to clarify: are there already prepared sources of legislation and case law for filling the RAG database, or will they also need to be automatically collected and updated?

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Good day. I am ready to complete this project as I have extensive experience in app development.

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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

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Good day! We can assist with the development of an AI web application for a law firm: document analysis, RAG search of legislation, and AI chat for lawyers. The project is clear: it is important to build not just an AI chat, but a secure system with documents, vector search, access roles, logging, and minimization of hallucinations. We can implement: — uploading and processing PDF/DOCX/DOC/TXT — OCR for scanned documents — document type identification — structural analysis of contracts — risk search with classification — RAG search of legislation and case law — AI chat with document context — reports in DOCX/PDF — access roles, JWT/2FA, action logging — masking personal data before AI API — Dockerized architecture. For the stack, we see it logical: Python + FastAPI, React + TypeScript, PostgreSQL + pgvector, Claude API, LangChain/LlamaIndex, Docker. To start, we can propose an MVP: document upload → OCR/parsing → RAG search → risk analysis → AI chat → report with source references. We are ready to discuss architecture, security requirements, the scope of the legislation database, and MVP estimation.

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Hello!

I am a Full-Stack Software Engineer with over 7 years of experience in developing websites, SaaS solutions, complex web platforms, and MVPs for startups - from idea and architecture to production and support.

I work not only as a developer but also with a focus on business logic, scalability, and long-term support of solutions. My portfolio includes examples of implemented projects of varying complexity.

Technology stack:
PHP (Laravel, Symfony, Yii2),
Frontend: JavaScript (Vue.js, React.js), HTML5, CSS3,
… Databases: MySQL, PostgreSQL.

Regarding timelines and costs - at this stage, it is difficult to accurately assess them without clarifying the requirements. To form a reasoned estimate, it is usually necessary to understand:
- project goals;
- key functionality;
- expected load and integrations;
- priorities (quick launch vs scaling).

I suggest starting with a brief clarification of the requirements or a call / correspondence - after that, I will be able to provide a realistic estimate on timelines, budget, and implementation options.

I would be happy to discuss the details.

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Great, I can do everything clearly as requested, well and in a short time.

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I understood the specifications: MVP of a legal assistant with three blocks — analysis of incoming contracts (PDF, DOCX, DOC, TXT, OCR for scans) identifying risks and referencing norms, RAG search through Ukrainian legislation and case law, chat assistant with context from uploaded documents. A strict requirement — minimal hallucinations, each conclusion with a reference to the norm, self-hosted-friendly architecture.

The proposed stack matches what I use for my main AI product. Briefly, here’s how I see the implementation by components.

Document analysis. A parser using pdfplumber for text PDFs, fallback to Tesseract with the ukr-language pack for scans. A classifier for document type and structure extraction (parties, details, deadlines, amounts, penalties, force majeure, termination) goes as structured output from Claude with jsonschema, so we don’t have to parse free prose. The risk scanner itself is a separate stage: the model receives only the necessary clause of the contract and the relevant piece of legislation from RAG, returning {severity, explanation, law_ref, recommendation}. This way, accuracy is much higher than when you ask for all analytics in one prompt across the entire document.

RAG. PostgreSQL and pgvector as you propose — for self-hosted this is the best option, no need for a separate vector-DB. Embeddings — OpenAI text-embedding-3-large or Voyage, chunking by legal structure (article/paragraph), not by characters. Codes and case law are split by a preprocessor with normalization of article numbers, so that semantic search provides not only contextually close results but also exact quotes. LangChain would be suitable here for orchestrating the pipeline, but it’s better to keep the retrieval part on your own Python wrapper over pgvector — LangChain.Vectorstores are too abstracted and complicate tuning.

Security. Persistence in your infrastructure (self-hosted PostgreSQL), documents encrypted at rest (pgcrypto), masking personal data before sending to external LLM API — a separate PII pre-processor (Presidio or your own regex+ML wrapper). JWT with a short lifetime plus refresh-token, RBAC through row-level security in PostgreSQL to differentiate "admin / lawyer / assistant", audit log in a separate table with append-only mode.
…
Anti-hallucinations — this is not a single approach, but several layers. First, retrieval-only mode for responses on legislation: the model receives references from RAG and an explicit instruction "if sources do not cover the question — respond this way". Second, post-hoc verification: a separate validating call with a lower temperature checks if all quotes from the response are present in the retrieved chunks. Third, log r

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Hello.

I am a NodeJS developer. I have experience with AI. I am ready to take on your project. Write to me, and we will discuss.

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Good day, Ostap!

I am ready to create an RAG platform for legal work with documents, where critical aspects are accuracy, source control, and data security.

I have experience in developing AI solutions based on LLM APIs, RAG architectures, and vector search (pgvector / LangChain-like approaches), so I understand well how to build a system where answers are not "fabricated" but tied to documents and legal sources.

From a technical standpoint, I see the MVP as a FastAPI backend with a modular architecture: a separate service for document processing (OCR + parsing + contract structuring), a separate RAG service for legislation and case law, and an AI chat layer with contextual access to uploaded files. An important emphasis is on quality control of responses through source citation and limiting generation outside of context.

Considering security requirements (encryption, access roles, logging, possible self-hosted approach for some components), this is a medium-complexity enterprise AI system, where the biggest risk is not the UI, but the quality of RAG, the stability of data extraction from documents, and minimizing model hallucinations.
…
MVP estimation:
300 hours
8 weeks
3800 USD

It can be broken down into stages:

1. basic architecture + document upload + OCR
2. RAG for legislation + semantic search
3. AI chat with context + risk analysis of documents
4. security layer + roles + logging + stabilization

The stack is completely fine: FastAPI + PostgreSQL + pgvector + Claude API + Docker — this is just the right foundation for such a product.

If needed, I can offer a more detailed architecture (with a data flow diagram and RAG pipeline) at the start before the final estimation.

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Good day, I am ready to perform. Message me privately, we will discuss in more detail.

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Good day.

I have reviewed the specifications. The project is completely clear, and the architecture for the MVP looks adequate and can be implemented without issues.

I have experience working with:
— Python / FastAPI
— integration of LLM (OpenAI, Claude API)
— AI chatbot systems
— RAG architecture and vector search
… — PostgreSQL / pgvector
— Dockerized backend services
— OCR and document processing
— integration of external APIs and AI workflows

I have also worked with AI systems where the following are critically important:
— minimization of hallucinations
— retrieval-based responses
— logging
— context control
— secure architecture

How I would implement the MVP:

Backend:
Python + FastAPI
PostgreSQL + pgvector
LangChain / LlamaIndex for RAG pipeline
Claude API as the main LLM

Document processing:
— OCR for scanned PDF (Ukrainian support)
— parser for DOCX/PDF/TXT
— chunking + embeddings
— semantic indexing

RAG:
— separate pipeline for legislation
— separate pipeline for case law
— citation-based responses
— retrieval-only strategy to reduce hallucinations

Security:
— JWT auth
— role-based access
— encrypted storage
— audit logs
— possible masking of PII before sending to LLM
— Docker isolated services

Frontend:
React + TypeScript
Ukrainian interface
REST API + Swagger documentation

What I also recommend:
— adding a confidence score for AI responses
— verification layer before generating the final legal conclusion
— fallback retriever for source verification
— async queue for OCR and heavy AI tasks

MVP assessment:
Timeline:
approximately 4–8 weeks depending on the depth of AI analysis and the volume of legislation/case law database.

Cost estimate:
from $4000–9000 for the MVP depending on:
— complexity of AI logic
— number of integrations
— level of security
— load
— necessity of self-hosted AI infrastructure

I can also help with:
— designing AI architecture
— optimizing RAG
— planning scaling
— preparing Docker deployment
— building secure AI pipeline

I am ready to discuss the details and propose a technical architecture for your case.

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