Contracts, invoices, claims and purchase orders arrive as PDFs, scans and email attachments — and turn into a person retyping fields into a system, one document at a time. Then someone asks a reasonable question: which of our contracts auto-renew in Q4, and which of those have a price-escalation clause? The honest answer is usually “give me a week.”
We turn that pile into structured, queryable data. Layout-aware extraction that survives skewed scans, stamps, handwriting, nested tables and 200-page annexes; classification and validation against your business rules; then the results land in two places at once — your database, so the fields power reports and workflows, and a RAG plus knowledge-graph layer, so anyone can ask a question in plain language and get an answer with the exact page and clause cited. This is the sharpest expression of what GK Codelabs does: an AI layer that works because the extraction pipeline beneath it is engineered, tested and monitored like production data infrastructure.
Key Features
Layout-Aware OCR
Handwriting & Stamp Capture
Table & Annex Extraction
Schema-Driven Field Mapping
Clause-Level Citations
Knowledge Graph Linking
Confidence Scoring & Review Queue
Renewal & Obligation Alerts
Key Advantages
Reads What Breaks Standard OCR
Skewed and low-quality scans, stamps and signatures, handwritten annotations, nested tables, and multi-page annexes.
Every Answer Cites Its Source
Responses link back to the exact document, page and clause — so legal and finance can verify in one click instead of trusting the model.
Structured Data, Not Just a Chatbot
Extracted fields land in your database as typed, validated columns — powering renewal alerts, reports and downstream workflows, not only conversation.
It Knows What It Doesn't Know
Low-confidence fields are routed to a human for review rather than quietly written into your system of record.
Relationships, Not Just Text
A knowledge graph links parties, obligations, renewal dates and amendments — so you can trace how a clause changed across three versions of the same contract.
New Document Type in Days
Schema-driven extraction means adding a new form, vendor layout or claim type is a configuration change, not a new project.
Trusted by Innovators Across Industries
The data engineering work happens underneath: pipelines that are clean, tested, and production-grade, so the AI layer on top actually holds up. That's the difference between a chatbot that impresses in a demo and an agent your team can put into a real workflow.
What Our Clients Say
Trusted by global enterprises and fast-growing startups to deliver reliable, scalable, and intelligent data solutions.
Frequently Asked Questions
Here are some of the most common questions we receive from businesses exploring our solutions.
What kind of data do you work with?
We handle structured, semi-structured, and unstructured data from various sources, including databases, APIs, files, IoT devices, and streaming platforms.
Can you work in our cloud environment?
Yes, with our Bring Your Own Cloud (BYOC) model, we build and manage data pipelines securely within your existing cloud infrastructure.
What visualizations or reports can you deliver?
We create interactive dashboards and visual reports using tools like Power BI, Tableau, or custom-built frontends tailored to your KPIs.
Is your service scalable as our data grows?
Yes, all our solutions are cloud-native and built to scale with your data volume, user base, and business complexity.
See Our Work in Action
Watch how we transform raw data pipelines into actionable dashboards, with AI agents enabling real-time insights.
AI Agents & LLM Apps Built to Run in Production
Most AI agents fail quietly - on stale data, broken pipelines, or retrieval that returns the wrong context. GKCodeLabs builds AI agents, RAG systems, and LLM applications on data infrastructure we engineer ourselves - so what you ship holds up under real traffic, not just demos.