We fix the gap between proof of concept and real customers.
Only 1 in 9 companies have AI agents running in production today. Most pilots die in the gap between demo and reality — hallucinations at scale, token costs that compound, model drift, and reliability gaps nobody tested for. KUMO has shipped 25+ production systems in 13 years, including the engineering for Volopay (YC S20, $31M raised) and our own SaaS, CampaignHQ. We do the unglamorous parts.
Most teams build a working demo, get sign-off, and stall. Not because the model is wrong — because the production layer underneath was never built. Here is what kills AI in the wild.
Ten test queries pass. Ten thousand real queries surface edge cases nobody anticipated — wrong data formats, ambiguous instructions, adversarial inputs. Without an evaluation harness and regression suite, you find out from your users.
Demo cost $50 over a week of testing. Production cost $50K in the first month because nobody routed cheap queries to cheap models, cached repeats, or capped context windows. The finance team finds out before the engineering team does.
Accuracy was 94% at launch. Six months later it is 78% — but no one is measuring. Customer complaints start arriving as feature requests, then as churn. Drift detection should ship with the model, not after.
Concurrency. Rate limits. Provider outages. Timeout cascades. None of this shows up in the staging environment because staging never had 10,000 concurrent users hitting the same endpoint at 9 AM Monday.
Picking a model is the easy part. Production AI needs an entire layer below it — the parts that catch failures, control costs, monitor drift, and keep the system reliable when the load is real.
Test sets that cover edge cases, regression detection on every model or prompt change, automated checks for hallucinations and policy violations. Catch failures before users do.
Per-request traces, token usage by endpoint, accuracy and drift dashboards, alert thresholds on cost, latency, and error rates. The same operational discipline you have on the rest of your stack — applied to AI.
Model routing (cheap models for cheap queries), prompt caching, response caching, context window management, batch inference where it makes sense. The difference between a $50K/month bill and a $5K/month bill is engineering, not pricing.
Retry logic, fallback models, graceful degradation, rate limit handling, circuit breakers, multi-provider failover. The boring infrastructure that turns an AI demo into a system you can put your name on.
KUMO are our go-to consultants when it comes to solving deep fintech technical architecture problems.
Pilot that never reached production? AI in production that drifts, breaks, or burns budget? Demo that impressed the board and nobody since? A senior member of our team will be in touch personally — no automated sequences, no junior triage.
If it makes sense to move forward, we will walk through exactly what an engagement looks like and what it takes to get started. If it does not, we will tell you that too.
We will be in touch within one business day. Or email us at enquiry@kumohq.co
No pitch decks. A senior team member will be in touch within one business day.
