AI Systems
Enterprise Knowledge & AI Context Platform
Knowledge-retrieval (RAG) infrastructure connecting internal sources, conversations, tasks and repositories into AI workflows.
Context
Company knowledge was spread across documents, conversations, trackers and repositories; finding an answer consumed a meaningful share of working time.
Problem
High search friction: the answer depended on who searched and where, and identical questions were solved from scratch each time.
Constraints
Index freshness, source access control, mandatory relevance evaluation — the answer must rely on sources, not the model’s memory.
Architecture
Source normalization and versioning → hybrid retrieval with re-ranking → supplying the model the minimum sufficient context.
AI layer
Re-ranking of what was retrieved and a context-budget cap; the answer carries a link to the source it relies on.
Event model
A source change becomes an event that rebuilds the relevant part of the index — with no deferred nightly updates.
Integrations
Documents, conversations, tasks, repositories and CRM connected through a single context layer that respects access rights.
Automation flows
Supplying relevant context into support, operations and decision workflows — where manual search used to be.
Infrastructure
Event-driven indexing, caching, isolation by source access policy.
Observability
Relevance and groundedness metrics, regression sets of questions with known answers, fragment-influence tracing.
Results
Search friction dropped, decisions became faster and more reproducible.
Lessons
RAG breaks on context, not the model; without quality evaluation the degradation is invisible and undebuggable.