Internal docs01
Internal knowledge assistants
One assistant that answers across Notion, Drive, Confluence and your wiki — citing the page, not paraphrasing it.
RAG & knowledge systems
Citation-first responsesPer-tenant isolationEval-gated rolloutsYou own the code
Hybrid
Vector + keyword retrieval
Hybrid
Vector + keyword retrieval
Cited
Source-grounded answers
5.0
Google rating
24h
Reply within
Where it fits
Support02
Customer support copilots
Suggest grounded replies in Zendesk / Intercom, drafted from your KB, prior tickets and product changelog.
Sales03
Sales enablement search
Reps ask in Slack and get the right case study, pricing slide, or objection answer — pulled from decks and CRM.
Compliance04
Policy & compliance Q&A
Answer regulatory and policy questions with strict source-only synthesis — audit log on every retrieval and generation.
Product05
Product knowledge agents
User-facing assistants over your help center, changelog and API reference — with deep-links back to docs.
Research06
Research & analyst assistants
Brief generation over reports, transcripts and warehouse data — with cited sources and an export-ready format.
The pipeline
01
Ingest
Loaders for Notion, Drive, Confluence, Linear, S3, PDFs, tickets and your warehouse — with deduping, OCR and structured-metadata extraction.
- 20+ source connectors
- OCR + table extraction
- Permission inheritance
02
Embed
Semantic chunking sized to your domain, dense + sparse embeddings, and a re-index pipeline that re-runs when models or content change.
- Domain-aware chunking
- Dense + BM25 hybrid
- Scheduled re-indexing
03
Retrieve
Vector + keyword + filters, then a reranker for relevance — so the top-k passed into the LLM is actually the right top-k.
- Hybrid + metadata filters
- Cross-encoder rerank
- Recall@k benchmarks
04
Synthesize
Answers that cite their chunks, refuse politely when the knowledge isn't there, and never quietly hallucinate over missing context.
- Inline citations
- Refusal templates
- Faithfulness evals
What you get
8 items, none of them extra
Source connectors with permission inheritance baked in
Domain-aware chunking strategy + scheduled re-index pipeline
Hybrid retrieval (vector + BM25) with cross-encoder rerank
Citation-rendering UI: file, page, link back to the source
Golden eval set + live faithfulness regression dashboards
Per-tenant data isolation and configurable PII redaction
Latency, cost and recall dashboards in Looker Studio
30-day post-launch tuning window — prompts, evals, chunking
How it runs
- 01Week 1
Source & access mapping
What knowledge lives where, who can read it, and what we're explicitly never allowed to surface.
Source map · ACL model
- 02Week 1
Eval set first
Before a single chunk is embedded, we write the golden Q&A set. Retrieval and answer quality ship behind it.
Golden eval set
- 03Weeks 2–3
Ingest & embed
Loaders, deduping, OCR, semantic chunking and a re-index schedule sized to how often content changes.
Indexer · Re-index plan
- 04Weeks 3–4
Retrieval tuning
Hybrid retrieval, filters, reranker. We tune against Recall@k and citation accuracy — not against a demo.
Retrieval benchmarks
- 05Weeks 4–6
Synthesis & surfaces
Grounded generation, citation rendering, refusal handling — plumbed into Slack, Zendesk, in-product widgets.
Surfaces · Eval pass
- 06Ongoing
Observability & evolve
Live latency, cost and faithfulness dashboards. Quarterly re-eval as content and models shift.
Dashboards · Retro
The stack
Anthropic ClaudeSynthesis
OpenAIEmbeddings
LangChainOrchestration
SupabaseVector store
PostgreSQLpgvector
Vercel AI SDKRuntime
NotionSource
SlackSurface
FAQ
- 01How is this different from a generic AI chatbot?
- A generic chatbot guesses from training data. A RAG system retrieves the relevant chunks from your knowledge first, then generates an answer that cites those chunks — and refuses politely when the knowledge isn't there. The work is in the retrieval, not the prompt.
- 02Where does our data actually live?
- By default on your cloud (AWS, GCP, Azure) or in a managed Postgres / Supabase instance you control. Per-tenant isolation, configurable PII redaction, and the option to keep embeddings on-prem. We sign DPAs and align to your data-residency rules.
- 03What stops it from hallucinating?
- Three things: retrieval quality (we benchmark Recall@k and citation accuracy before launch), grounded synthesis (the model only answers from retrieved chunks), and a refusal template when the knowledge isn't there. Live faithfulness evals catch regressions as content drifts.
- 04How fresh is the knowledge?
- Depends on the source. Notion, Slack, Drive and your warehouse can be near-real-time via webhooks. Larger document corpora ship with a re-index schedule you set — typically hourly, daily or on-change.
