Get my agent

Assistants that search your docs, wikis, tickets and databases, answer with citations, respect who can see what, and say "I don't know" instead of inventing. Quality is measured, not assumed.

Get my agent now What we build ↓
Week 1
Prototype on your data
Day 30
Production pilot
100%
Yours: code & weights
Ch. 01The problem

Scanned PDFs, stale wikis, tables, permissions and contradictory versions break naive retrieval. We engineer ingestion, chunking, hybrid search, reranking and access control, and we prove answer quality with an eval set built from your real questions.

Ch. 02What we build

  • 01

    Internal knowledge assistant

    Policies, processes and product docs answered in seconds, with the source paragraph linked.

  • 02

    Support deflection

    Customers get correct answers from your help center and past tickets; agents get suggested replies.

  • 03

    Contracts & compliance search

    Find clauses, obligations and deadlines across thousands of documents.

  • 04

    Analytics in plain language

    Text-to-SQL over your warehouse with guardrails, so the answer comes from the database, not a guess.

Ch. 03What you get

  1. 01Ingestion pipeline: PDFs, Office, Notion, Drive, Confluence, databases
  2. 02Hybrid search (vector + keyword) with reranking
  3. 03Document-level permissions mirrored from your sources
  4. 04Answer-quality evals: faithfulness, citation accuracy, refusal rate
  5. 05Chat UI, Slack/Teams bot or API, your call
Stack
  • pgvector
  • Postgres
  • Elasticsearch
  • Qdrant
  • Embeddings
  • Rerankers
  • Claude
  • GPT
  • Gemini
  • Next.js

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Ch. 04FAQ

Will it hallucinate?

We design it to cite or refuse. Every answer links its sources, and the eval suite tracks faithfulness and refusal rate so regressions are caught before release.

Can it respect document permissions?

Yes. Retrieval filters by the asking user's access, mirrored from your source systems, so nobody gets an answer built from a document they can't open.

Do we need a vector database?

Often Postgres with pgvector is enough and keeps your stack simple. We only add infrastructure when scale demands it.

Where does our data go?

It stays in your cloud. Embeddings and indexes live in your accounts, and we can use models with zero-retention terms or self-hosted ones.

Other capabilities
Ch. 05Now

Tell us the pain. We answer in one business day.

  • ✓Reply in 1 business day
  • ✓Fixed price before we start
  • ✓NDA on request