Development of an AI agent for contract analysis (based on Claude)

Budget: 2000 UAH Deadline: 7 days

Hello, I worked on an AI system for analyzing medical documents that processed over 500 documents a day with 94% accuracy - this is very similar to your project with contracts!

I am curious how you plan to address the issue of chunking legal documents to maintain context between related clauses of the contract?

I suggest we get in touch, I will consult you for free on the technical side and we will create a development plan + I will tell you about my team! ⚖️

Budget: 2000 UAH Deadline: 1 day

Hello!

We are implementing an AI agent for contract analysis in Ukrainian:

Stack: Claude API (analysis + recommendations), RAG on ChromaDB/Pinecone, FastAPI backend, support for PDF/DOCX/TXT.

Phase 1 MVP:
— Uploading the contract, breaking it into logical blocks
— Classifying the type of contract
— Risk analysis by points (🔴🟡🟢) linked to the norms of the Civil Code/Commercial Code
— RAG on legislation — only specific references, no hallucinations
— Recommended revision of each point

Phase 2:
— Judicial practice as argumentation
— Comparison of two versions of the contract
— Risk checklist + summary

We understand critical nuances: quality chunking, validation of references, control of hallucinations through RAG-only responses.

Message me privately — we will discuss the budget and start. Expand

Budget: 27000 UAH Deadline: 27 days

Good day. I am very interested in your project. I offer quality execution within a moderate timeframe. We can discuss the details of the work and start the implementation.

Budget: 27000 UAH Deadline: 14 days

Good day.
I am ready to take your project into work.
I will be able to develop such an assistant for you using n8n.
Write to me privately, we will discuss all the details and choose the solution that is most suitable for you.

Budget: 26990 UAH 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 business. 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: 20000 UAH Deadline: 10 days

Good day, we are ready to implement an AI agent for contract analysis with a focus on accuracy and legal justification - we have 4 years of experience in IT and have worked with AI and RAG systems in our portfolio - we offer a solution with an API-first architecture where the LLM processes text and generates conclusions, while the RAG layer through a vector database provides answers based solely on legislation and case law, we are implementing a pipeline for document processing, breaking down into blocks, analyzing clauses, classifying risks, and generating recommendations with references, separately we will establish hallucination control, logging, and source validation, the MVP can be implemented in 2–4 weeks with further expansion of features - we are working in pairs, which guarantees a comprehensive approach.

Budget: 8000 UAH Deadline: 1 day

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

Budget: 20000 UAH Deadline: 15 days

Hello! The task is clear — you need not just a chatbot, but a full-fledged RAG system with a high degree of trust in the sources. I have experience in developing complex systems in Python using LLM (Claude, GPT) and integrating external data through APIs and vector databases. My experience in developing terminals with risk management and complex logical bots allows me to implement an architecture where the Reasoning model Claude will be supported by strict filtering through a knowledge base. How I will implement the MVP (Phase 1): 1. Document processing: I will set up quality parsing of PDF/DOCX with proper segmentation (chunking) into logical blocks to avoid losing the context of the points. 2. RAG system: I will use Pinecone or Chroma DB to store the legislative base. This will allow the agent not to "hallucinate," but to provide specific references to articles of the Civil Code/Commercial Code. 3. Risk analysis: I will set up a scoring system (🔴/🟡/🟢), based on the patterns we will embed in the prompts and knowledge base. 4. Result: You will receive an API-first solution that produces a structured report comparing "was/has become." I have a question regarding the sources: do you already have a compiled database of legislation in text format, or do we need to set up parsing from open registers in the first stage?

Budget: 18500 UAH Deadline: 18 days

Good day!

I have carefully reviewed the technical specifications — the task is complex and interesting, and I see where most performers make mistakes at the start.

The main thing that distinguishes quality implementation is solving three specific problems that are usually overlooked:

1. Chunking contracts with non-standard structure. Regex parsers do not work on real documents. I use Claude in the first step to structure the document in JSON (section → subsection → text), and only then do I index chunks with metadata. This costs an additional $0.01–0.02 per document, but provides precise references to a specific clause, rather than "somewhere in section 3."

2. Validation of references to legal norms. Each reference in the report has a flag retrieved: true/false — the agent physically cannot invent an article that is not in the retrieved context. If the norm is not found — it simply states: "norm not verified, we recommend consulting a lawyer." This is the foundation of trust in the product.

3. Relevance of the legislative base. I index codes with metadata last_verified_date, monitoring changes through parsing the Verkhovna Rada. The report always shows the verification date of the norm.

What is included in Phase 1 (MVP):
— Uploading PDF / DOCX / TXT
— Claude-based chunking and classification of contract type
— Basic RAG with legislation (Civil Code, Commercial Code, Labor Code)
— Risk analysis by levels
— Verification of references
— JSON → HTML report

Phase 2 — (14 days)
Connecting judicial practice (Unified Register of Court Decisions), improved risk classification, report UI with filters.
Estimated cost 14,000

Phase 3 — (18 days)
Comparison of two versions of the contract, integration with Google Docs, automation.
Estimated cost 18,000

Stack: Python + FastAPI, Claude API, ChromaDB, Next.js.

The cost in the response is indicated for Phase 1: 18,000 UAH.

I have experience in developing AI agents and RAG systems, and I understand where the real complexities of such projects lie.

I am ready to discuss the details and answer technical questions. Expand

Budget: 25000 UAH Deadline: 10 days

Hello, I can develop an MVP version for your product. I have experience in creating complex turnkey products. I suggest we connect in private messages.

Have a good day, Pavlo

Budget: 20000 UAH Deadline: 5 days

I have experience building complex RAG systems and understand that in the legal field, "hallucination" of the model is an unacceptable error. I will explain how I implement this so that you choose me.

My technical implementation strategy:
1. RAG architecture and Chunking (Key Stage)
The most challenging aspect of legal RAG is the correct segmentation of documents.

Legislation: I implement hierarchical chunking (Article -> Part -> Paragraph) to ensure references to legal norms are precise, not general.

Case law: We will use a vector database (Pinecone or Chroma) to store semantic vectors of court decisions. This will allow us to find not just similar words, but similar legal situations.

2. Critical Thinking Workflow
Instead of a single query to the LLM, I will implement an Agentic Workflow:

Analyst: Breaks down the contract and identifies the essence of obligations.

Retriever: Searches for relevant norms in the Civil Code/Commercial Code and relevant cases in the case law database.

Legal Reviewer: Matches the contract clause with the obtained data, identifies risks, and formulates justifications.

Editor: Checks references for validity and formulates recommendations in "human language."

3. Technical Stack
Backend: Python (FastAPI) — the gold standard for AI projects.

Orchestration: LangGraph (from LangChain) for creating complex cyclical agents.

OCR: Unstructured.io or AWS Textract for perfect PDF reading.

Frontend: Next.js for creating an interactive report where one can click on a risk and see the text of the law in the adjacent window.

I specify the price and deadlines for phase 1.

My GitHub: https://github.com/onyx144 Expand

Budget: 22000 UAH Deadline: 30 days

Доброго дня. Стек:

— Python 3.13 + FastAPI (API-first архітектура)
— Claude 3.7 Sonnet (Anthropic API) — найкраща з reasoning по long-context legal docs, я daily-driver
— Embeddings: voyage-law-2 (спеціально для legal) або text-embedding-3-large
— Vector DB: Chroma локально для MVP ($0), Pinecone/Weaviate коли scale >10k договорів
— OCR: Tesseract + pdfplumber для текстових PDF, Azure Document Intelligence для відсканованих
— Smart chunking: LlamaIndex з custom legal-block parser (за блоками "предмет/відповідальність/форс-мажор", не arbitrary 512-token)
— Sources: zakon.rada.gov.ua scraper для ЦКУ/ГКУ/КЗпП + reyestr.court.gov.ua для судової практики
— Docker Compose + GitHub Actions CI

Critical для якості (типові пастки які ламають LegalTech AI):

System prompt який ENFORCE'ує "відповідай тільки з джерел RAG" + refusal якщо нема citation
Кожне reference валідується — Claude перевіряє що нормативний акт існує перед output
Smart chunking з overlap не cuts через legal clauses (інакше context loss)
Confidence score per risk (0-100%) + tier classification (червоний/жовтий/зелений)
Phase 1 MVP (4-5 тижнів, 22000 UAH):

Upload PDF/DOCX/TXT + OCR fallback
Smart chunking + classification типу договору
RAG з ЦКУ + ГКУ + КЗпП embedded
Risk identification + 3-tier + recommended rewrite + "людською мовою" пояснення
API endpoint + admin UI
Анти-hallucination layer
Phase 2 (3 тижні, +18000 UAH): судова практика з reyestr.court.gov.ua, PDF export звіту, comparison режим двох версій

Phase 3 (по запиту): "режим переговорів", автоматизації, Google Docs / CRM інтеграції

Релевантний досвід: Daily Claude API user (Sonnet 3.7), будую local RAG pipelines (Chroma + Postgres pgvector). Парсила Verkhovna Rada open data API для іншого проекту.

Demo: POC RAG over повним ЦКУ + 1 тип договору (купівля-продаж) за 3-4 дні, 5000 UAH окремо як технічна перевірка перед основним контрактом.

Чесні обмеження: Hallucinations не 100% solvable, output потребує review юриста перед production use. Disclaimer "AI не заміна юриста" обов'язковий у фінальному UI.

Готова на ескроу + async chat. Expand

Budget: 1000 UAH Deadline: 1 day

I am among the top 10 developers in the category of "Artificial Intelligence and Machine Learning" among ~2100 specialists on the platform.
I guarantee:
- Fast and quality execution of the task
- Strict adherence to deadlines
- Regular communication throughout the entire process
I would be happy to discuss the details of your project in private messages.

Budget: 27000 UAH Deadline: 27 days

The task is interesting. Approximately 4-5 weeks for MVP, $800-1200. If suitable, we can discuss.

Budget: 5000 UAH Deadline: 10 days

Good day! We have reviewed your project. The task is very strong and promising — essentially a LegalTech AI platform for contract analysis with a focus on Ukrainian law. We have experience in creating AI services, RAG systems, document intelligence solutions, and complex backend products.

We understand the key task: not just a "chat with AI," but a managed AI agent that provides specific legal conclusions with references to legal sources and minimizes hallucinations.

What we can implement:

• Uploading PDF / DOCX / TXT contracts
• OCR for scanned documents
• Smart chunking of the contract into legal blocks
• Determining the type of contract
• Analyzing each clause separately
• Identifying risks: legal / financial / operational
• Risk scoring: high / medium / low
• Rewritten versions of clauses
• Explanations in "human language"
• Document summary

RAG system:
• Integration of Ukrainian legislation
• Codes, acts, regulatory framework
• Searching for relevant legal norms
• Case law as a separate knowledge layer
• Validation of sources and references

Recommended stack:
• Backend: Python (FastAPI)
• AI: Claude API + OpenAI (backup / embeddings)
• Vector DB: Weaviate / Pinecone / pgvector
• PostgreSQL
• OCR: Tesseract / Google Vision / Azure OCR
• Frontend: Next.js / React
• Auth / roles / audit logs

How we would build the MVP (Phase 1):
• Document upload
• Analyzing the structure of the contract
• RAG only based on legislation
• Risk report + recommendations
• API-first architecture for future integrations

What is critical and we will take into account:
• Quality chunking of documents
• Strict grounding only from sources
• Logging of responses
• Checking the relevance of norms
• Transparent architecture for scaling

We can also assist as a technical partner: product roadmap, UX legal dashboard, monetization model, SaaS architecture.

We are ready to discuss the MVP, budget, timelines, and break the work into phases.

Our website: https://benefit-it.net/
Sincerely, Benefit Studio Expand