Industry

AI Development for SaaS Companies

Ship the AI layer your roadmap keeps deferring

SaaS teams rarely lack AI ideas. They lack the specialist capacity to build them properly alongside an existing roadmap — retrieval that is actually accurate, agents that are safe to let loose in a customer account, and inference costs that do not quietly destroy gross margin.

Not prototyped
ShippedNot prototyped
Per-tenant cost
MeteredPer-tenant cost
Before release
EvaluatedBefore release
Where it hurts

What slows SaaS companies down

  • The AI feature on the roadmap keeps slipping behind committed work
  • A prototype exists but nobody is confident enough to put it in front of customers
  • Inference cost per account is unmodelled, so pricing is guesswork
  • Support volume grows with the customer base and headcount follows
  • Onboarding is manual and time-to-value is the leading churn driver
FAQ

SaaS questions we get asked

Can you work inside our existing codebase and process?
Yes. We work in your repo, your review process, and your deployment pipeline. The output is code your team owns and can maintain, not a black box we have to be retained to operate.
How do we price an AI feature when costs are variable?
Instrument first. We meter usage per tenant and per feature so you can see real unit economics, then choose between seat pricing with fair-use caps, credit packs, or usage tiers based on actual distribution rather than a guess.
How do you stop AI features regressing?
An evaluation suite in CI. Every prompt or model change runs against recorded cases with expected outcomes, so a regression fails a build rather than reaching customers.

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