Your AI features fail quietly. We find it before your users do.
Clan Labs is a reliability studio for teams shipping AI features. We find what breaks in production, silent quality drift, provider outages, untested prompts, and build the fixes before your users feel them.
Most AI features break in ways nothing alerts you to.
A green build means the code runs. It says nothing about whether the model still gives good answers, whether your provider is up, or whether last week's prompt change made things worse. These failures cost you users and are brutal to trace. Most teams have no gate that catches them.
Quality drifts silently
A prompt tweak, a model update, shifting inputs, and output slowly gets worse. Nothing errors, nothing alerts. You find out when support tickets spike and users leave, by which point the cause is weeks buried.
Providers go down with no fallback
When your provider has an outage, your feature goes down with it. Without automatic failover to a backup, someone else's incident becomes yours, live in front of every user.
Prompt changes ship on hope
With no safe way to test prompts first, teams edit them straight into production code. Every change is a blind bet, and regressions only surface once users hit them.
No one owns reliability
Small teams can't spare an engineer to build monitoring, evals, and governance from scratch, so it never gets built. The gaps stay invisible until something breaks in front of a customer.
Gartner predicts over 40% of agentic AI projects will be cancelled by the end of 2027, citing escalating costs, unclear business value, and inadequate risk controls. Source. We catch and fix the reliability gaps while they are still cheap.
We find where your AI is exposed, and build the fix.
We assess your stack across four areas, then build the failover, monitoring, and testing that close the gaps, or hand your team a plan to run with. Diagnosis and the fix, not a forty-page report nobody opens.
Provider resilience & failover
What actually happens the moment your model provider goes down? We check whether you can detect the outage in seconds and switch to a backup model automatically, so the failure stays invisible to the people using your product.
Output-quality monitoring
How would you even know if quality slipped? We assess whether you are measuring output quality continuously and catching regressions automatically, instead of finding out from a frustrated customer weeks later.
Prompt testing & benchmarking
Can your team change a prompt with confidence? We look at how prompts are tested, compared, and benchmarked, so every change ships on evidence and a clear before-and-after, never on a hope and a deploy.
Governance & data handling
Where are you exposed on data handling and oversight? We review how AI actions are logged, controlled, and kept auditable, an expectation that is fast becoming non-negotiable for UK and EU teams under tightening AI rules.
How it works.
Light to start, clear at every step. You always know what you are buying and what you will walk away with.
Intro call
A short call on your stack, your AI features, and where you suspect the weak points are.
Assessment
We work through all four reliability areas against your real setup and document where you are exposed.
Findings readout
A prioritised report: what is fragile, what each gap costs you, and the order to fix it in.
We build the fix
We build the failover, monitoring, and testing the audit surfaces. Or hand it to your team, your call.
SaaS teams
Teams adding AI features to live products and needing production AI reliability before usage grows.
AI product teams
Teams shipping LLM workflows that need prompt testing, LLM evals, and output-quality monitoring.
Startup teams
Small teams that need AI reliability consulting without hiring a full internal reliability function.
Enterprise pilots
Teams piloting agentic AI that need AI risk controls, governance, and provider failover.
What you get.
A clear picture of your risk, a prioritised plan, and the working systems to fix it, failover, monitoring, and prompt testing in production.
Exposure map
Where your stack is fragile across failover, quality, prompt testing, and governance, each finding rated by severity.
Prioritised fixes
The gaps to close first, ordered by risk against effort, so you know where the next sprint goes.
What it would cost you
For each gap, the plain-language cost of leaving it, an outage, a quality regression, a compliance miss.
The build
We build the failover, monitoring, and prompt testing that close the gaps, wired into your stack.
Example audit output
A practical slice of the readout: what is exposed, how severe it is, and the first fix we would put in motion.
| Area | Finding | Severity | First fix |
|---|---|---|---|
| Provider failover | No backup model route if the primary provider is down. | High | Add a tested fallback route with health checks. |
| Prompt testing | Prompt changes ship without a regression benchmark. | High | Build a golden test set and compare outputs before deploy. |
| Quality monitoring | Output drift is not measured after release. | High | Add sampled evaluations and alerts for quality drops. |
Built by people who've shipped reliability infrastructure for production AI.
Clan Labs is a reliability studio led by its founder, with a small team behind it. The work is hands-on: provider failover, output-quality monitoring, and prompt testing, the systems that keep AI dependable in production.
Led by a founder who has shipped AI products used by large consumer and enterprise audiences, with hands-on experience across product, reliability, and production AI systems.
We're taking on a small number of founding clients.
Early engagements run at a reduced rate, in exchange for honest feedback and, if you are happy, a case study and an intro to teams like yours. You get the work for less, and we build the track record together.
Apply as a founding client- Reduced founding-client rate
- Full audit, plus the build
- Direct access to senior delivery
- Optional implementation follow-on
Questions, answered.
The things teams usually want to know before booking.
How long does an audit take?
What do you need from us?
We already use an eval or observability tool. Do we still need this?
Who actually does the work?
What does it cost?
Find out where your AI is fragile.
Book a short intro call. We will talk through your stack and tell you honestly whether we can help. If we can't, we will say so.
Free, no-obligation — 30 minutes.
Not ready for a call yet? Email us your stack and we will tell you the first thing we would check, no obligation.