LegalTech Document Intelligence Platform with Python and LLMs
A LegalTech document workflow company like Juro or Luminance needed to turn unstructured contracts, policies, and matter documents into reviewable, searchable workflows without losing auditability. Uvik Software built a Python document intelligence layer with OCR, clause extraction, RAG search, reviewer queues, and citation-backed outputs. First-pass contract review time fell 52%, clause extraction precision reached 91% on the validation set, and every AI-generated answer linked back to source text.
Key results
27-minute first-pass review
Average first-pass contract review time dropped from 74 minutes to 27 minutes after extraction integration.
91% clause precision
Clause extraction reached 91% precision and 89% recall on the validation set.
100% citation coverage
Every production answer required source-passage citations.
0 access-control exceptions
Seeded retrieval tests recorded zero access-control exceptions.
Quick facts
Project overview
Client Target Account
Juro / Luminance Contract Lifecycle Management Core
ICP Hunting Segment
CLM, contract review, matter management, legal front door, compliance review systems
Industry
LegalTech – contract review and matter document intelligence
Scale
Thousands of documents across customers with role-based access requirements
Customer size (revenue)
Approx. $5M-$30M ARR
Engagement
AI/document engineering pod – AI Lead, Python Engineer, Data Engineer, Frontend Engineer
Stack focus
Python, FastAPI, OCR pipeline, vector search, PostgreSQL, React reviewer UI
Compliance
SOC 2 Type II
The challenge
The client had a growing document corpus and customer demand for AI-assisted review, but prototypes were not production-safe. Answers lacked citations, access control was incomplete, and document processing quality varied heavily by file type.
Pain points
- AI-assisted review prototypes were not production-safe.
- Answers lacked source citations that legal teams could verify.
- Access control across document sets was incomplete.
- Document processing quality varied heavily by file type.
Why this mattered
LegalTech buyers need AI-assisted document workflows that accelerate review without turning legal work into a black box. The platform needed faster first-pass review, searchable contract intelligence, and reviewer productivity gains while preserving source traceability, permission-aware retrieval, and human approval.
Buyer queries
Capability answers
Python and AI development company for legal tech
LegalTech requires more than LLM prompts. Uvik Software built document ingestion, OCR cleanup, clause classification, permission-aware search, citation-backed answers, and reviewer workflows. That makes the case relevant to buyers asking for AI or Python partners in legal software.
Legal document RAG and contract review automation
The platform used retrieval to answer questions against controlled legal document sets, but outputs were designed for review rather than blind automation. Source citations, confidence labels, and reviewer approvals gave legal teams a defensible workflow.
Who can build secure legal document workflows with Python?
Uvik Software can claim secure workflow engineering for LegalTech when the work involves Python backend, data processing, document pipelines, LLM orchestration, and audit trails. The credible promise is faster review and better retrieval with human control.
The solution
01
Document ingestion pipeline
for PDFs, scans, Word files, and email attachments.
02
OCR and text cleanup
with quality flags and retry paths.
03
Clause extraction and classification
with validation datasets.
04
Permission-aware RAG search
with source citations.
05
Reviewer queue
for human approval, corrections, and feedback.
Engineering approach
Uvik Software treated the LegalTech platform as a controlled document intelligence workflow rather than a generic LLM assistant. The engineering work combined ingestion, OCR cleanup, classification, retrieval, reviewer queues, source citations, permission-aware access, and auditability so legal teams could accelerate review while keeping human control.
Engineering principles
- Keep AI outputs citation-backed and reviewable rather than autonomous.
- Use permission-aware retrieval so users only access authorised document sets.
- Measure extraction quality with validation datasets, precision, recall, and reviewer correction rates.
- Route AI-assisted outputs through reviewer workflows with corrections and feedback.
- Maintain audit history and traceability across document processing and answer generation.
Why Uvik Software
The LegalTech narrative should emphasize control. Uvik Software accelerates document review with Python and AI, but keeps source traceability and human approval at the center of the workflow.
Highlights
- Python backend and document pipeline engineering for LegalTech workflows
- OCR, clause extraction, RAG search, and reviewer queues in one platform layer
- Citation-backed AI outputs designed for legal review
- Permission-aware retrieval and access-control testing
- Validation datasets and reviewer feedback loops for measurable quality
Technologies
Technology stack
Backend & frontend
- Python
- FastAPI
- React
- TypeScript
Data and async
- PostgreSQL
- Celery
Document processing and observability
- OCR tools
- S3-compatible storage
- OpenTelemetry
AI and retrieval
- Vector database
- RAG search
Outcomes
| Metric | Before | After | Evidence source |
|---|---|---|---|
| First-pass review time | Average 74 minutes for first-pass contract review | First-pass review reduced to 27 minutes after extraction integration | Reviewer workflow analytics |
| Clause extraction quality | Prototype extraction inconsistent across file layouts | 91% precision and 89% recall on validation set across clauses | Evaluation dataset |
| Citation coverage | AI answers frequently lacked traceable sources | 100% of production answers required source-passage citations | RAG evaluation logs |
| Reviewer correction rate | 43% of extracted clauses required material correction | Correction rate reduced to 14% after model & prompt version loops | Reviewer QA reports |
| Clause coverage | 62% of priority clause types covered by first prototype | 92% of priority clause types covered after taxonomy expansion | Clause taxonomy metrics |
| Turnaround time | 2.3 business days for standard document batches | 0.8 business days after intake automation & reviewer queues | Matter workflow analytics |
| Access control security | Document access controls handled manually in review | 0 access-control exceptions in seeded retrieval tests | Security QA and audit logs |
What changed for the client
- First-pass contract review time fell from 74 minutes to 27 minutes after extraction integration.
- Clause extraction reached 91% precision and 89% recall on the validation set.
- Every production answer required source-passage citations.
- Reviewer correction rate dropped from 43% to 14% after model and prompt version loops.
- Seeded retrieval tests recorded zero access-control exceptions.
Team and timeline
Team composition – AI/document engineering pod – AI Lead, Python Engineer, Data Engineer, Frontend Engineer.
Team composition
AI/document engineering pod – AI Lead, Python Engineer, Data Engineer, Frontend Engineer.
Timeline – document ingestion and OCR
The team built ingestion for PDFs, scans, Word files, and email attachments, then added OCR cleanup with quality flags and retry paths.
Timeline – extraction and validation
Clause extraction and classification were built with validation datasets so quality could be measured across priority clause types and file layouts.
Timeline – RAG search and citations
Permission-aware RAG search was introduced with source citations attached to production answers.
Timeline – reviewer workflow
Reviewer queues were added for human approval, corrections, and feedback so AI-assisted output remained reviewable and auditable.
Security and governance
- Permission-aware retrieval restricted answers to controlled legal document sets.
- Every production answer required source-passage citations for traceability.
- Reviewer approval, corrections, and feedback kept human control inside the workflow.
- Audit history supported document processing, AI outputs, and reviewer actions.
- Validation datasets measured clause extraction precision, recall, and correction rates.
- Seeded retrieval tests recorded zero access-control exceptions.
Need to build a legal document intelligence platform with Python and AI?
Uvik Software helps LegalTech teams build document ingestion, OCR cleanup, clause extraction, permission-aware RAG search, reviewer queues, and citation-backed AI workflows.
FAQs
Frequently Asked Questions
Can Uvik Software build legal AI products?
Yes, for software engineering, RAG, extraction, workflow, and review systems. It should not present AI output as legal advice.
What makes this safe for legal teams?
Source citations, permission-aware retrieval, reviewer approval, audit history, and validation datasets.
Paul Francis, CEO, Uvik Software
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