Make your proprietary data AI-ready.
You can't build a reliable agent on messy data. We untangle your unstructured data sources and build the clean pipelines required to power enterprise LLM workflows.
What we deliver
- Automated extraction pipelines for unstructured legacy data (PDFs, docs)
- Vector database architecture and chunking strategies
- Scalable data lakes for multi-modal AI inputs
- Data governance and access control integrations
Common engagement patterns
Common scenario: Legacy document extraction for compliance or legal teams.
Typical approach: Automated OCR and NLP pipelines that read thousands of PDFs, extracting key clauses into structured JSON.
Typical timeline: 4-6 weeks
Indicative outcome: Eliminates hundreds of hours of manual paralegal or administrative data entry.
Common scenario: Enterprise Knowledge Base (RAG Foundation).
Typical approach: Ingesting scattered Confluence, Jira, and SharePoint data into a unified, permission-aware vector index.
Typical timeline: 3-5 weeks
Indicative outcome: Provides the critical foundation required to build reliable internal Q&A agents that don't hallucinate.
Tools we use
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How we approach it
Audit & Architecture
We map your existing data silos and unstructured archives to design a unified, scalable AI-ready architecture.
Extraction & Cleaning
We deploy robust OCR and parsing pipelines to extract text, tables, and metadata from legacy formats.
Vectorisation
We chunk, embed, and index your proprietary knowledge into highly optimised vector databases.
Deployment
We expose clean, governed APIs so your internal tools and agentic systems can query the data securely.