Repeated questions
Teams answer the same questions because decisions and explanations are buried across systems.
Private Company Knowledge
Give your team reliable AI access to company knowledge without losing control of the data.
Company knowledge rarely lives in one clean repository. It is distributed across documents, project systems, meeting transcripts, specifications, policies, tickets, and decisions that were never written down properly.
I design and build knowledge systems that help people and AI agents find the right information while respecting source boundaries, permissions, freshness, and the limits of what retrieval can answer.
The short answer
Private AI is a data and retrieval problem before it is a model problem.
A capable model cannot recover knowledge that was excluded, processed badly, retrieved without context, or exposed to the wrong user. The system must first decide which sources matter, how each document type should be processed, who may see it, how freshness is handled, and how an answer points back to evidence.
Signs this is your situation
The knowledge exists, but people and tools cannot use it reliably.
Weak or unattributed answers
Search misses exact business terms, or an AI answer gives no trustworthy source.
Access boundaries
Teams need different permissions, project isolation, or private-data restrictions.
Proof of concept limits
A RAG demo worked on a tidy sample but fails against real document types, history, and edge cases.
Architecture decisions
Retrieval quality depends on the system around the model.
01
Document processing
Profile source types and preserve useful structure instead of forcing every file through one chunking rule.
02
Hybrid retrieval
Combine semantic search with exact-term retrieval, metadata filters, reranking, and source attribution.
03
Permissions and isolation
Apply access rules at retrieval time and keep project, tenant, and source boundaries explicit.
04
Evaluation and deployment
Test representative questions and failure cases, then choose managed API, private cloud, local, or hybrid deployment based on the real constraints.
Deterministic boundaries
When RAG is not the answer
Conventional search is often better for known terms. Structured database queries are better for exact records and calculations. Long context can be enough for a small, bounded collection. Deterministic rules should own decisions that can be stated and tested completely. I compare these paths before adding a retrieval pipeline.
How I approach the work
Start with the sources and questions, then build the application around them.
The work can include knowledge-source analysis, retrieval architecture, document profiles, reranking, citations, project and tenant isolation, agent access through APIs or MCP, model selection, evaluation, application development, monitoring, and handover.
Relevant proof
ContextHarbor separates knowledge that changes at different speeds.
I built ContextHarbor around a practical distinction between live repository state, project documents, decisions, reusable prompts, and durable memory. That separation helps people and agents retrieve the right kind of context without pretending that every source is the same.
Explore ContextHarborRelated reading
Decisions behind reliable private knowledge systems
RAG by Document TypeDocument-aware processing, hybrid retrieval, and reranking for company knowledge.Your Coding Agent Has a Context ProblemWhy repository state, project knowledge, decisions, and memory need different treatment.Building an AI Home Lab, Part 4A practical local and private AI architecture.
If your team is evaluating private AI, start with the knowledge architecture.
Bring the sources, users, access constraints, and questions the system must answer. We can compare conventional search, RAG, long context, agent memory, and private deployment options before committing to an implementation.
Discuss your knowledge architecture