AI Platform Engineering
AI built into your SaaS product, whether new or already in market.
We build AI into SaaS products, new or already in market: a copilot that knows each customer's account, answers from their own data, drafting inside the workflow, and the tenant isolation, cost controls and release tests that let you ship it with confidence.
Each feature runs on your tenancy model, permissions and billing. Before a customer sees a change, it has passed cases drawn from real customer work.
Which of our accounts renewed late this quarter, and why?
Renewal notes
From your account records
Support threads
Cited in the answer
Billing history
Late payments flagged
Account health report
Updated this morning
A user asks in plain words and gets an answer from their own account's records, with each source one click away and nothing from any other tenant.
It knows the screen the user is on and what they are allowed to see, drafts and explains inside the product, and asks before it changes anything.
Admins can now export a whole workspace in one step and get an email when the file is ready.
Answers for customers and release notes drafted by AI wait for a person before anyone outside your team sees them.
Prompt, model and retrieval changes run against your evaluation set before release, quality and cost are then tracked for each tenant, and rolling back to the last good release takes one step.
The pieces SaaS teams ask us for most, each built on your tenancy, permissions and billing and measured against cases from your own product.
A copilot that sees the screen, the record and the user's role, explains what is in front of them, drafts the next step and hands over to your support team when it should.
Semantic and keyword search combined, with answers grounded in each customer's own records and documents. Your product's permissions apply to every result, and the source is always one click away.
Summaries of threads, records and meetings, and drafts of replies, reports and release notes, placed where the work already happens and checked by a person before anything leaves the product.
Your product's agent can update records, run workflows and call your APIs for a user, with each action scoped to that user's permissions, logged, and held for approval wherever you choose.
Each customer's data, embeddings, prompts and any fine-tuned weights kept apart, with the tenant's identity attached to each model request and each tool an agent calls. Tests try to cross tenant lines before each release.
One route to every model you use: routing by task, caching, fallbacks, and budgets and rate limits per tenant, so one heavy customer cannot erode your margin or slow everyone else down.
Tokens, tasks and actions counted per tenant, user and feature and passed to your billing system, so you can price AI as seats, credits or usage and see what each plan costs to serve.
An evaluation set built from real customer work runs on every release. In production, quality, latency and cost are traced per tenant, so a regression shows on a dashboard before it arrives as a ticket.
The controls your customers' security teams ask for: single sign-on, roles, audit logs, data retention, a choice of region, model allow-lists and a switch that turns AI features off for a workspace.
Agent access
Your customers are starting to work through AI assistants and agents of their own, and they expect those agents to reach the tools they already pay for. The Model Context Protocol, an open standard, gives agents a common way in.
We build an MCP server over your product's API, with scoped access for each customer's agents: they see only that tenant's data, act only with the permissions an admin grants, stay within rate limits and leave the same audit trail as any other user.
SaaS teams usually begin with a single feature on a single plan. It is built inside your stack, proven with real tenants and handed to your engineers.
A short readiness sprint in your product, data model and support history to pick the first feature, decide how success will be measured and set what it may cost per tenant.
Our engineers join your codebase and cloud account and wire the feature into the tenancy, permissions, billing and model gateway you already run, not around them.
Cases from your own customers are tested before launch. The feature then goes to a few design-partner tenants behind a flag while quality and cost are watched, and a rollback is one switch away.
Your engineers take it over with runbooks, dashboards and the evaluation set. The code, prompts and any fine-tuned weights are yours; we can keep running it under an agreed scope if you prefer.
Describe your product, the people who use it and the AI feature you are weighing, and an engineer will reply with a first sketch of how we would build it.