A copilot that reads only this tenant's data.

• 01

  In-product answers over customer data

  Users ask about their own workspace and get a cited answer, with retrieval filtered by tenant and by the user's own permissions — not just the tenant's.

• 02

  Docs and support deflection

  Grounded answers over documentation and past tickets, embedded in-product where the question is actually being asked.

• 03

  Onboarding and migration assistance

  Guides a new customer through setup using their actual configuration state, which is the difference between a checklist and a copilot.

• 01

  Tenant isolation belongs in retrieval

  Filters at the vector-store level plus row-level security underneath, tested with an adversarial suite that actively tries to retrieve across tenants. This is the finding that ends procurement conversations.

• 02

  Cost per user is a product decision

  We model cost per active user against your pricing before writing the feature, then enforce it with caching, model routing and hard ceilings so the unit economics survive adoption.

• 03

  Evals are the release gate

  A versioned eval set in the repo, run in CI, so upgrading a model is a measurement rather than a gamble.

1. 1

   Data inventory

   What docs / databases / APIs / feeds matter? What's PII vs public? What changes daily vs quarterly? We map it before writing code.

2. 2

   Retrieval architecture

   Hybrid (BM25 + vector) retrieval, chunking strategy, reranking, citations. Tuned for your domain not a generic benchmark.

3. 3

   Eval suite

   Real-world question set with golden answers. Faithfulness, context precision, latency. So you know when changes ship a regression.

4. 4

   Guardrails

   Prompt-injection defense, PII scrubbing, refusal patterns, output schemas. Especially critical for customer-facing deployments.

5. 5

   Production + iterate

   Cost-effective inference (caching, fallback models), monitoring, weekly eval reports. Fine-tune when the data justifies it.

• Postgres with row-level security
• pgvector, Pinecone & Qdrant
• Clerk & Auth.js
• Stripe Billing & metering
• Vercel, Fly & Railway
• Sentry, PostHog & OpenTelemetry
• Segment
• GitHub Actions

FAQ

RAG for SaaS products: your questions.

• How do you guarantee one tenant can't see another's data?

  Isolation is enforced at three layers — the query filter, the store's own partitioning, and row-level security in Postgres — with an adversarial test suite in CI that tries to break all three on every commit. No single layer is treated as sufficient.

• Can customers bring their own model or key?

  Yes, and enterprise buyers increasingly require it. We keep the provider behind an interface so BYO-key, a self-hosted open-weight model and the default path are the same code.

• Should I use RAG or fine-tuning?

  RAG for facts that change. Fine-tuning for style, format, or domain reasoning. Most production systems use both — and we'll tell you which mix is right after a 1-week discovery.

• Can I use my own LLM (open-weights)?

  Yes. We work with Llama, Mistral, Qwen, and similar — useful for data residency, cost predictability, and air-gapped environments.

• SaaS prototype to productionMulti-tenancy, real auth, billing, migrations and CI added to a working prototype — plus the security posture your first enterprise buyer will audit.Read
• In-product AI agents for SaaSAgents that execute inside your product — configuring, importing, drafting and fixing — with the same permissions as the user who asked.Read
• AI growth marketing for SaaSContent and lifecycle systems that produce pages worth indexing, with attribution joined to activation and revenue rather than form fills.Read

• RAG for banking and financial servicesRetrieval over circulars, product terms and internal procedure — returning the clause, the revision date, and a link to the page it came from.Read
• RAG for healthcare and clinical documentsRetrieval over clinical guidelines, payer policy and internal SOPs — with the citation, the version, and an explicit refusal when the library doesn't cover the question.Read
• AI product search and catalogue RAGSemantic search and grounded product Q&A over your catalogue, reviews and size guides — with stock and price read live rather than remembered.Read