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.
Inference economics, concretely
Cost per request stops being an accounting curiosity the moment your AI feature is in the pricing page, because it sets the floor on your gross margin. The order below is roughly by return on effort.
Attribute cost per tenant before optimising anything. Almost every team we work with discovers the distribution is far more skewed than they assumed — a handful of accounts generating a disproportionate share of spend, sometimes on a plan that does not cover it. That finding often changes pricing before it changes engineering.
Cache the stable prefix. Most production prompts carry a large, unchanging instruction block and a small variable part. Structuring the request so the stable part can be cached is frequently the single largest saving available, and it is a refactor rather than a quality trade-off.
Trim what accreted. Prompts grow. Few-shot examples get added to fix a case and never removed once the model improves. Measuring which examples still earn their tokens against the evaluation set usually recovers a meaningful fraction.
Route by difficulty, with quality measured. Cheap model for the easy majority, capable model for the rest, and the routing decision itself validated against labelled cases. This is the step that goes wrong when done first and without measurement.
Fix the retry behaviour. Silent retry storms on transient provider errors are a common and invisible cost line, and they show up as latency complaints long before anyone traces them to the bill.
What breaks after launch
Shipping the feature is not the end of the work, and the failure modes are predictable enough to design for.
Provider models get deprecated and replacement versions behave differently on your edge cases — which is why the evaluation suite is the asset, not the prompt. Retrieval quality decays as the corpus grows and the chunking that worked at five thousand documents stops working at fifty thousand. Usage patterns shift as you move upmarket and the drift shows up as rising escalations before it shows up in any dashboard.
None of this is exotic. It is LLMOps, and it is the part that distinguishes a feature you can maintain from a demo that shipped.
Where the work overlaps other sectors
The retrieval and modelling layer under most product AI features is data platform buildout. If your product includes a support surface, the same engineering as support automation applies — with the difference that in your case it is a feature your customers pay for rather than a cost centre.
FAQ
Questions we get asked
Why not just hire for this?
Sometimes you should, and we will say so. But a platform hire takes three to six months to find and does not give you 24x7 coverage on day one. Bringing us in to build and run it while you hire — or instead of hiring, if the scope does not justify a permanent role — is usually the better arithmetic.
Our AI feature works in the demo but we are scared to ship it. Is that normal?
Extremely, and it is the most common reason software companies call us. The gap is almost always evaluation, cost controls, injection defences and graceful degradation — not model quality. Those are tractable in weeks.
Can you help with our SOC 2?
The engineering half — controls, evidence collection, logging, hardening — working alongside your auditor or compliance platform. We are not an audit firm and the certificate does not come from us.
Will you work as an extension of our team?
That is the AI Ops Partner model — a named pod working in your repositories, your standups and your review process. It suits companies with an ongoing roadmap rather than a single project.
What happens to the code and the pager if we stop working with you?
You keep everything, and the handover is defined at the start rather than negotiated at the end. Code in your repositories from day one, infrastructure as code in your accounts, runbooks and the evaluation suite written for someone who was not there. We run a handover exercise where your engineer takes an incident with us watching rather than the other way round. A retainer that would collapse if we left is a retainer built to trap you, and it is worth checking any provider on this point including us.
We are seed stage with eight engineers. Are we too early?
For the platform retainer, often yes — at that size your infrastructure should be simple enough that a managed platform and a couple of well-chosen defaults carry you, and paying us to run it is premature. For pilot-to-production on a specific AI feature, no. That work is scoped and finite, and getting the evaluation and cost controls right early is cheaper than retrofitting them at Series B. We will tell you which side of that line you are on.
Our engineers will resist an outside team touching the platform.
Reasonably so, and it is worth taking seriously rather than managing around. What works is starting with the work they actively do not want — the pager, the dependency upgrades, the compliance evidence — rather than the architecture decisions they care about. Those stay with your team, with us implementing. Engagements that begin by overruling the existing engineers do not go well, and we have declined ones scoped that way.
How much of the inference cost reduction is just switching to a cheaper model?
Less than half, usually. Model routing helps, but the larger wins are typically caching repeated context, trimming prompts that grew by accretion, and stopping the retry storms nobody noticed. We measure quality on your evaluation suite throughout, because a cost reduction that quietly degrades output is a price cut you did not agree to. If the only available saving is a quality trade-off, we present it as that and let you decide.
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.
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