Search should feel like asking the best person in the room — not digging through folders with better branding.
A retrieval-first AI search engine for teams that need grounded answers from their own knowledge base.
RAG AI search engine
SeekEngine
Grounded answer
For teams that want AI search to be accountable, not theatrical.
SeekEngine is built around document ingestion, semantic retrieval, answer grounding, source trails, workspace memory, and team-ready search experiences.
Audience
Service teams, product teams, research teams, and operators with scattered documents, decisions, and institutional knowledge.
Problem
Most internal search is either too literal or too magical. Teams need answers that cite sources, respect context, and hold up under real work pressure.
RAG search across documents and workspace knowledge
Source-backed answers with visible citations
Collections for projects, departments, or clients
Search sessions that become reusable briefs
Admin controls for knowledge hygiene
Built like a real product track, not a portfolio prop.
The roadmap is deliberately staged so the product can validate usefulness before chasing scale.
Private prototype
Workspace ingestion tests
Answer quality evaluation
Team pilot
Public waitlist
Ready to start?
Need brand, product, and launch to finally sit in one room?
That's exactly what we built Zocav for. One studio, visible scope, clean communication, and delivery tied to a real business outcome.