Your AI is silently failing in production.
We find and fix the failures before your customers do.
Get a failure report on your production AI in under a week. No integration required.
Evaluations > Run #2847"...recommend starting lisinopril 40mg daily alongside lifestyle modifications..."
Medication not discussed in consultation. No evidence in transcript.
"Patient reported intermittent chest pain on exertion - absent from clinical note"
Clinically significant symptom mentioned at 04:32 but not documented.
"...consistent with chronic migraine pattern..."
Patient described "occasional headaches" - diagnostic leap to chronic migraine unsupported.
Trusted by leading AI teams
The problem
You don't know what your AI is getting wrong right now.
Your test suite was accurate the week you wrote it. Your LLM-as-judge gives the same scores on day 100 as day 1.
Your AI is handling things differently than you expect - and nobody notices until a customer complains.
Every team we've worked with discovers failure patterns in week one they had no idea existed. Not generic "hallucination" - specific failures that matter for your domain.
What happens
What the first four weeks look like.
Week 1
The failure report
We connect to your production traces and run our engine. You get a failure report - every failure categorised by type, severity, and frequency. This is usually the "oh shit" moment.
Weeks 2-3
Your experts calibrate
Your domain experts review what we flagged and correct where we're wrong. Every correction makes the system smarter - similar cases improve automatically. We build out guardrails for the worst patterns.
Week 4
Handover
You own everything. The evaluation criteria, the failure taxonomy, the guardrail rules, all correction data. The system works without us.
Ongoing
It gets smarter
Platform maintenance, upgrades, and tuning as your product evolves. Optional - the system works without us.
Your team commits ~10 hours over 4 weeks. We handle everything else. You own everything at the end.
How it works
How we catch failures your evals miss
Find
Connect to your production traces. We surface failures your team doesn't know about - categorised by type, severity, and frequency. Not generic "hallucination" but the specific ways your AI fails that matter for your domain.
Learn
Your domain experts correct where we're wrong. Every correction compounds - fix one case, similar cases improve automatically. The system adapts to your evolving standards. Day 30 catches things day 1 missed.
Fix
Confirmed failure patterns become guardrails that block bad outputs at runtime. Sub-second latency. 100x cheaper than frontier models - runs on every output, not just a sample. Your quality standards enforced automatically.
See it in action
See what we find in a real clinical AI output
See how Composo evaluates a real clinical AI output - with analysis, source citations, and expert corrections that compound over time.
What we replace
The alternative takes 6 months. We deploy in 2 weeks.
Without Composo
Your best ML engineer spends 3-6 months building evaluation infrastructure
The scoring logic is frozen the week it was written
Nobody wants to maintain it
When the model changes, the evals break
When the domain expert leaves, the knowledge leaves with them
Failures still slip through
With Composo
Deployed in 2-4 weeks
Your team spends ~10 hours total
The system gets smarter every week from expert corrections
You own everything at the end
Guardrails block and fix bad outputs before customers see them
Under the hood
Four things that took us 30 deployments to get right
Custom failure taxonomy for your domain
We build a failure taxonomy specific to your use case - learnt from your traces and your experts. Day one is already informed by patterns from 30+ deployments across healthcare, fintech, CX, legal, and multi-agent systems.
Learns from your traces and experts
Your production traces and expert corrections build a memory of what quality means for your domain. Month-1 corrections still improve month-6 evaluations. The system gets smarter every week without retraining.
Dynamic ensemble of agents
Multiple specialised agents work together - blending fast and deep evaluation intelligently. Beats any single model alone. Fast enough to block, cheap enough to run on everything.
Runs on 100% of outputs, sub-second
100x cheaper than frontier models. Fast enough to block bad outputs before they reach customers. Cheap enough to run on everything - not just a sample.
The failure taxonomy
The failure taxonomy
Every engagement adds to a structured library of AI failure patterns - categorised by type, severity, and domain. Hallucinated medications in healthcare. Unsupported conclusions in legal. Confident wrong answers in customer support.
30+ deployments means we've seen failure modes your team hasn't encountered yet. When we deploy into your stack, the engine already knows what to look for. Your expert corrections make it specific to your domain. The taxonomy grows with every engagement - anonymised, cross-customer, compounding.
This is the thing that takes 6 months to build internally and starts from zero every time. No eval tool or observability platform ships with one. It's the difference between configuring a tool and deploying an engine that's already seen your type of failure.
See it on your data
Send us a handful of production traces. We'll deliver scored results with a failure report - what's going wrong, how often, and how severe. Takes under a week. Your team reviews it, and if it doesn't match their judgment, you've lost nothing.
30+ deployments. Every team discovers 3-5 critical failure patterns they didn't know about.
30+
AI teams across healthcare, fintech, CX, legal, and multi-agent systems
3-5
critical failure patterns found in week one that teams didn't know existed
2-4 wks
to deploy vs 3-6 months to build internally
90%+
agreement with domain experts on flagged failures
From our customers
Trusted by teams where quality isn't optional
We embedded Composo into our AI Workers from day one - best decision we've made on testing. As an early stage start-up, we can't afford to waste time on manual evals or debugging. They provide peace of mind for us and our customers. No brainer.
Fehmi Sener
CTO, 5u.ai
We cut our QA cycle time by 70%. Instead of relying purely on human review, now we instantly know which prompts are failing and why.
Head of AI Engineering
Enterprise SaaS platform
For the first time, we can ship with complete confidence knowing exactly what our AI quality looks like at scale.
Senior Software Engineer
Instrumentl
LLM as a Judge was far too unreliable. Composo gave us the deterministic scoring we needed to actually track improvements.
Senior ML Engineer
Fortune 500 Financial Services
First failure report delivered in under a week. Most teams discover 3-5 critical patterns they had no idea about.
50 expert corrections is the typical turning point. By month 2, the system catches failure types it missed in month 1.
Guardrails running in production, blocking bad outputs before users see them. Not sampling - every output, evaluated.
Backed by an ablation study on RewardBench 2 (1,753 examples). Vanilla LLM-as-judge: 72.1%. Our combined techniques: 85.4%. Full study on GitHub.
What you get
Your system. Your data. Your rules.
You own everything. Calibrated to your domain, running in your stack.
Calibrated evaluators
Specific to your domain and use case
Dynamic failure taxonomy
Every pattern categorised and severity-ranked
Guardrail rules and thresholds
Running in your stack, sub-second latency
All annotation data
From your domain experts, compounding over time
Deployment runbook
Complete documentation and configuration
Self-hosted option
Deploy in your Azure, AWS, or GCP. SOC 2 Type II certified.
Your AI is failing in production right now. Let's find out how.
Get a failure report on your production AI in under a week. No integration required.