Your company knows more than it can find.
We make that knowledge reachable, with sources attached.
Reports, procedures, contracts, decision history: your organisation's real expertise sits scattered across documents nobody has time to search. We connect AI directly to that knowledge, so every answer is grounded in what you actually know.
Opens an email, no forms or calendar links.
Every team and sector that lives by their documents
Your confidential documents must not end up on the web.
Employees copy-paste contracts, procedures and client data into ChatGPT or other public AI tools more often than most companies realise. But locking everything down isn't the answer either: in a typical organisation, only a fraction of documents, contracts, HR files, strategic plans, are genuinely sensitive. We build a hybrid RAG: sensitive material stays on infrastructure you control, while the rest runs wherever it's fastest. You get security where it matters, and a faster, more reliable system everywhere else.
Classify first
Every document is classified by sensitivity before it ever reaches an index, typically around 10% confidential, 90% not.
Sensitive data stays local
Contracts, HR records and strategic plans are processed and stored on infrastructure you control. Nothing sensitive leaves your perimeter.
Everything else runs efficiently
The remaining documents run wherever retrieval is fastest and most cost-effective, without touching the sensitive fraction.
Your documents, your data, your company culture, finally usable.
Here is what happens between your documents and an answer. The method has a name: retrieval-augmented generation, or RAG.
"Did we ever test a lower-viscosity variant of the polymer coating, and why did we abandon the original approach?"
Once, in an early feasibility trial that was never followed up. The original approach was shelved in 2022 after inconsistent adhesion at scale, not a problem with the coating chemistry itself: a detail the project lead remembered but had never written down.
People's know-how is implicit. RAG can only put it to work once it has been captured, written down, recorded, or walked through in an interview.
A closer look at the mechanics
The system retrieves relevant passages across your reports, procedures, contracts and internal databases.
→These extracts are injected as context, with your business terminology, your formats and your organisational logic.
→The AI generates a response that reflects your organisation: precise, sourced and verifiable.
Structuring this knowledge today makes your organisation machine-readable, the baseline every company will need for what comes next.
3 phases. Commit at your own pace.
Start with a discovery workshop
Before any audit or estimate, we sit down with your team for 90 minutes, free of charge, to understand your documents and where information gets stuck. You leave with a clear picture of what we'd propose, and why. No commitment.
Audit
We assess the maturity of your documents in terms of structure, consistency, quality and RAG exploitability. You receive a score and a clear action roadmap.
Data Preparation
We clean, structure and normalise your data to build a corpus ready for AI transition, without technical debt.
RAG Pipeline
We build and deploy the complete AI solution, from ingestion to generation with cited sources.
What you receive. Per phase. Transparent.
Audit
Document maturity score
RAG exploitability audit
Prioritised action roadmap
Data Preparation
Cleaning & normalisation
Data structuring
Corpus ready for AI indexing
RAG Pipeline
Complete RAG pipeline with cited sources
Vector index + API
Deployment + documentation + source code
Evaluation test suite included
The final price depends on two factors: the number of documents and the density per document (pages per file). A corpus of 500 three-page PDFs is not the same effort as 500 two-hundred-page reports. Every delivered pipeline includes an evaluation test suite: you know exactly what your system returns before going live. Contact us and we'll send an estimate within 24h.
Not sure what it'll cost?
The free 90-minute workshop is the fastest way to find out. No commitment.
No agency. You talk to the people who build.
Yavuz Sarikaya
LinkedIn ↗Strategy & Project Architecture
Before building, you need to understand. Yavuz frames the business challenge, maps documentary risks and translates your constraints into an actionable RAG architecture. He defines what goes into the system, what comes out and why.
Applied R&D · European Innovation Programs · Proof of Concept
Ilker Makine
LinkedIn ↗AI Engineering & Build
Once the architecture is set, Ilker builds it. He designs and deploys the complete RAG pipeline in production: ingestion, chunking, vector index, retrieval, generation. His nuclear engineering background taught him one thing: in critical systems, rigour is not optional.
AI Engineering · Nuclear Engineering · UC Berkeley · NLP