For product
A capability you can ship, measure, and put on the pricing page.
- Scoped to one workflow first
- Eval set you can show the board
- Lives inside the product, not beside it
In-product AI layer
Ship an AI capability inside your own product
In-app assistants, retrieval over your data, and support automation — shipped inside the product you already sell.
Built for Product-led SaaS teams who need AI in the product, not a bolted-on widget.
Scope this with usThe problem
Your customers expect AI features. Building the retrieval, evaluation and guardrail layer well is a real engineering project that competes with your roadmap.
In practice
A B2B analytics platform adds an in-app assistant that answers questions about a customer's own dashboards, citing the underlying metrics — built in weeks, not a quarter.
Live
In your product
A capability customers can actually use
Cited
Answers
Grounded in the customer's own data
Weeks
First capability
One workflow, instrumented
Shipped
In production
Eval set, cost, and quality from day one
The shift
From a chatbot ticket to a product capability
From this
- ✕A roadmap item that keeps losing to core product.
- ✕A third-party widget that cannot see tenant data safely.
- ✕No evaluation set — just a demo that looks clever.
- ✕Cost and quality invisible until the invoice arrives.
- ✕Legal asking who owns the prompts and the logs.
- ✕Support still answering the same in-app questions.
To this
- ✓Retrieval designed around your data model and tenancy.
- ✓The assistant ships inside the product your customers already use.
- ✓An eval set from real tenant questions, not a happy path.
- ✓Quality, cost and usage on a dashboard from day one.
- ✓A capability you can put on the pricing page — not a widget parked beside the product.
- ✓Support deflection that cites the product the customer actually has.
What's included
The layer we assemble inside the product
Retrieval pipeline
Index, chunk, and fetch against your data model — with tenant isolation that would survive a security review.
Answer + citation
Responses that point at the metric, doc or record they used. Plausible is not enough.
Guardrails
What it may say, what it must confirm, what it must never attempt — per plan and per role.
Evaluation harness
A living set of real questions. We tune against it weekly, not against a launch demo.
Cost and latency
Budgets you can actually operate: tokens, p95, and a kill switch.
In-app surfaces
The assistant lives where the work is — a dashboard, a ticket, a page — not a parallel chat island.
Handoff to humans
When the corpus does not have it, a human gets the thread and the tenant context.
Observability
Traces, thumbs, and the examples your PM will want in the next sprint review.
Every role connected
Built around how each desk actually works
For engineering
Fits how you already ship. Clear service boundaries. Docs operators can follow.
- Fits the existing service map
- No mystery sidecar with admin on prod
- Docs a new hire can follow
For support
The in-app answer cites the same docs you already maintain.
- Escalations arrive with tenant context
- You see what the model said, and why
Go-live
Implementation without a year-long project
- 1
Week 1
Corpus and tenancy
What data, which tenants, which questions are in-bounds.
- 2
Week 2–3
Pipeline and eval
Retrieval, citations, a first eval set from real tickets or dashboards.
- 3
Week 4
In-app surface
Ship behind a flag to a slice of tenants.
- 4
Week 5+
Tune in production
Quality, cost, the cases it must never attempt. The product stays live.
Ideal for
SaaS teams who need AI inside the product they already sell
Deployment
Inside your product, on your tenancy model
Commercials
A scoped build — not a seat tax on your customers
How we approach it
- 1
We design the retrieval and evaluation pipeline around your data model and latency budget.
- 2
We ship it inside your product, with the model picked for the task.
- 3
We instrument it so you can see answer quality, cost and usage from day one.
- 4
The capability lives where your customers already work — a dashboard, a ticket, a page.
FAQ
Questions about saas & technology
Will this lock us into your hosted assistant?
It lives inside your product, on your tenancy model. We build the capability; your customers use it as part of what you already sell.
Can it see a customer's data?
Only inside that tenant, the way your product already does. Retrieval is designed around your tenancy model, not bolted on.
How do we know it is any good?
An evaluation set from real questions, instrumented from day one. We do not ship on a demo transcript.
Want AI inside the product — not beside it?
Bring the workflow and the data model. You leave with a written plan for the first in-app capability, including what it must never say.
We’ll tell you if a platform already fits better.
