Shipping an AI feature is a product problem.

Why now

Every SaaS roadmap now has an AI feature on it, and most of them stall between the demo that impressed the board and the version that can be billed. The hard parts are unglamorous: isolating tenant data in retrieval, keeping inference cost below the seat price, and proving quality didn't regress when the model changed under you.

• 01

  The demo doesn't survive multi-tenancy

  A copilot built against one customer's data has no isolation story, and retrieval is exactly where a cross-tenant leak becomes a breach notification.

• 02

  Inference cost outruns the seat price

  Nobody modelled cost per active user, so the feature is a margin problem the moment it succeeds.

• 03

  Nobody can tell if it got worse

  Without an eval suite, a model upgrade is a coin flip and support tickets are your regression test.

• 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.

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

• 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
• RAG for SaaS productsRetrieval over your docs and your customers' workspace content, with isolation enforced in the index and tested adversarially.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

1. Step 1 · Validate

   30-day Sprint

   Prove the use case before you commit. Working prototype on real data, eval scores, and an honest signal in 30 days. Fixed scope, fixed fee.

   $4,500 fixed

   Learn more
2. Most teams land here

   Step 2 · Build

   Prototype → Production

   Turn the validated prototype into a real product. Auth, DB, payments, tests, monitoring, deployed. Sprint fee credits toward this engagement.

   from $6,000

   Learn more

3. Step 3 · Scale

   Managed Retainer

   Ongoing operation, eval cycles, model iteration, and cost guards. We keep the system improving so your team can focus on growth.

   from $750/mo

   Learn more

FAQ

AI in SaaS: the questions we get.

• Can you build this as a feature inside our existing product?

  Yes — that's the usual shape. We work inside your repo, your conventions and your review process, and leave behind code your team can own rather than a service only we understand.

• How do we price an AI feature?

  Start from measured cost per active user, then decide between seat uplift, usage credits or a higher tier. We instrument the usage first so the pricing conversation has numbers in it, because guessing here is how AI features become loss-leaders.

• Which model should we build on?

  Whichever survives your eval set at acceptable cost — and the architecture should let you change your mind. We keep the provider behind an interface and route per task, so a price change or deprecation is a config edit rather than a quarter of work.

• AI for Fintech & BankingMost banking AI pilots die in review, not in testing. We build the boring parts first — decision logs, evals, human approval gates — so the model has somewhere safe to be useful.Read
• AI for HealthcareHealthcare AI earns trust by being narrow. We build systems that retrieve, draft and route — and leave every clinical judgement with a person who signs it.Read
• AI for Retail & E-commerceA bot that says 'let me check that for you' and can't is worse than no bot. We connect agents to the order, the inventory and the returns policy so they can finish the job.Read