By industry
AI & Automation for SaaS & Technology
Ship AI features and let us carry the platform
We design around
- DPDP Act 2023
- GDPR
- SOC 2 Type II readiness
- ISO 27001
- EU AI Act transparency obligations
- 8-10 weeks
- Pilot to production
- 30-55%
- Inference cost reduction
- 0
- On-call rotations for product engineers
With quality held on the evaluation suite
What we hear
The problems that bring people to us
If several of these describe your week, there is almost certainly something worth automating.
- An AI feature demo that has not shipped in six months
- Engineers carrying on-call instead of building product
- Inference costs growing faster than revenue
- Enterprise buyers blocking deals on security questionnaires
- No evaluation harness, so nobody can approve a prompt change
What we build
Where automation pays off in saas & technology
Pilot to production
Take an AI prototype that works in a demo and make it a product feature — evaluation harness, guardrails, cost controls, observability, staged rollout. This is the single most common engagement we run for software companies.
Embedded AI features
Build AI capability into your product as a first-class feature with the latency and cost budget of one, not a research prototype bolted on the side.
Platform and on-call handover
We take the pipelines, infrastructure and pager. Your engineers stop being part-time SREs, which is usually the fastest way to increase product throughput without hiring.
Inference unit economics
Model routing, caching and context trimming with quality measured throughout, plus per-tenant cost attribution so pricing decisions have real numbers behind them.
Enterprise readiness
The security, audit logging, data handling and documentation that enterprise procurement asks for — built as engineering rather than assembled under deal pressure.
The pattern we see most
A senior engineer built an AI prototype in a fortnight. It demos well. Leadership is excited. Six months later it has not shipped, because nobody can answer: what is its accuracy, what does it cost per user, what happens when the provider is down, and what stops a user extracting the system prompt.
None of those are research problems. They are engineering problems with known solutions, and they are what Pilot to Production exists to solve.
What we add to a working prototype
Prototype
- Impressive on the demo corpus, unknown at real scale
- Prompt changes argued about in review
- Inference cost discovered on the monthly bill
- One provider outage takes the feature down
- Prompt injection is an unexamined risk
- No audit trail when a customer disputes an output
Product feature
- Accuracy tracked against a labelled set in CI
- Every change scored before it merges
- Cost per request budgeted, alerted, attributed per tenant
- Provider abstraction with automatic fallback
- Injection and PII defences, tested
- Request-level audit trail with model version
The on-call arithmetic
For a team of fifteen to fifty engineers, carrying your own on-call costs more than the rota suggests: interrupted focus, slower feature throughput, and attrition among the two people who end up handling most of it.
Handing the platform and the pager to us is frequently the cheapest available increase in product velocity — no hiring cycle, no ramp, and coverage from the first week. See NoOps for what that covers.
FAQ
Questions we get asked
Talk to someone who has worked in saas & technology
Bring a process that annoys you. In 30 minutes we will tell you whether AI helps, what it would cost, and where it would fail — even if the answer is don't bother.
Or email [email protected] · we reply within 1 business day