Enterprise RAG & knowledge systems
Answers from your own documents. With sources, and the right permissions.
We turn scattered wikis, drives and PDFs into an assistant your team can trust. Every answer cites its source, respects who can see what, and is measured before anyone relies on it.
Is this for you?
An honest check before we build.
A good fit if…
The same questions get answered every day.
Knowledge is spread across too many tools.
A wrong answer carries real risk.
Your data can’t leave your own environment.
Probably not for you if…
Your documents are outdated or don’t exist.
You only need a better search box.
Nobody will own the content after launch.
You need it live before it’s tested.
The service
Retrieve
Ground
Cite
What we build
02
Q&A
Document Q&A with citations
Every answer links to the exact passage it came from.
03
Search
Enterprise search
Hybrid keyword and semantic search across every connected source.
04
Agentic
Agentic RAG
Breaks hard questions into steps, then checks and compares several sources.
Use cases
01
Support
Support answers
Agents and customers get consistent answers from one source of truth.
02
Legal
Legal and contracts
Find clauses, compare versions and flag deviations, with sources.
03
HR
HR and policies
Instant, accurate answers on policies, benefits and onboarding.
04
Engineering
Engineering docs
Specs, runbooks and past incidents, searchable in plain language.
Our method
01
Connect
Connect
Sync sources and permissions.
02
Index
Index
Parse, chunk and build hybrid search.
03
Answer
Answer
Grounded, cited responses.
04
Prove
Prove
Automated faithfulness, retrieval and hallucination tests on every change.
Accuracy
Measured, not guessed.
What we measure
Faithfulness: is every claim backed by a source?
Retrieval hit rate and answer relevance.
Citation accuracy on every answer.
Honest “I don’t know” when the answer isn’t there.
How we test
A gold set of [X] real questions from your team.
Hallucination and adversarial tests before launch.
Regression tests on every change.
Live tracing and user feedback after launch.
Connectors
SharePoint
Google Drive
Confluence
Notion
Jira
Databases
Data security
Your data stays yours.
Where it runs
Your cloud region of choice, private cloud or on-premises.
Open-weight models when data can’t leave your network.
No training on your data. Ever.
Everything runs in your accounts.
Who sees what
Permission-aware retrieval that mirrors existing document access.
Single sign-on with your identity provider.
PII redaction before anything reaches a model.
Full audit logs, aligned with GDPR, HIPAA and SOC 2 practices.
Engagement
A pilot on your documents. Then you decide.
Before we start, we agree on one team, one set of sources, a fixed price and the accuracy targets the pilot has to hit, tested on questions your team writes. Cost depends on how many sources and formats are involved, how messy the documents are, how complex the permissions are and where it needs to be hosted. If the pilot misses its targets, you’ve spent a pilot budget, not a project budget.
From kickoff to a measured pilot.
Fixed pilot price, agreed upfront.
Tech stack
01
Ingestion
Parsing
Docling, Unstructured and LlamaParse for PDFs, tables, scans and slides.
02
Retrieval
Search and ranking
Embeddings, pgvector, Qdrant or Weaviate, hybrid search, and Cohere or BGE rerankers.
03
Models
Orchestration and LLMs
LlamaIndex and LangGraph, with GPT, Claude or Gemini, or private Llama, Mistral and Qwen.
04
Quality
Evals and ops
Ragas, DeepEval and Langfuse, with FastAPI on AWS, Azure or GCP.
Process
Your sources. Your permissions.
Measured before launch.
Case studies
Knowledge systems in production, and what changed.
FAQ
How is RAG different from a chatbot?
Why not just fine-tune a model on our documents?
How do you stop it from making things up?
Will people see documents they shouldn’t?
What happens when our documents change?
Can it run in our own environment?
How much does it cost?
How long does it take?
