We run a managed quality loop on your production AI agents. You get a score you can defend, the fixes that move it, and smart API routing that typically cuts the model bill by half. You approve, we operate.
One managed loop on your production AI. We install the measurement, get paid to improve what it exposes, and keep the loop running so you never go blind again.
Your first real quality number, on your real traffic. The platform stands this up out of the box.
platformWhich checks fail, for which customers, and the root cause. Surfaced automatically.
platformOur engineers fix the prompt, retrieval or context, and backtest it offline before it ships.
managed serviceWe show the lift on the same number and roll it out with your team.
managed serviceYour system keeps changing. We keep it measured and routed, every month.
managed serviceAll the numbers below come from one coherent worked example: Aria, a customer-support agent measured on a public benchmark. The interactive demo walks the same story.
A test suite of 60 business-language checks built from your policies and graded on real conversations, every run. Aria scores 78 of 100 and climbed there over twelve weeks. When a model update quietly dropped quality in week 5, the suite caught it within days, before customers did.
A handful of low-effort changes, each backtested against the suite before it ships. For Aria that meant quality 78 to 89 and wrong answers 9% to 3%, roughly 12,000 fewer bad answers a month at 200k conversations. The current model stayed. The gains came from how the agent is built.
Several routes lead to a lower bill: a cheaper backtested default, caching, token tuning. The biggest is managed API routing: classify every request in milliseconds and send it to the cheapest model that still passes your quality bar. For Aria the blended cost fell from $0.021 to $0.010 per conversation, a 54% cut with quality held on every tier.
A routing and caching layer we design, run, and tune for you. Quality held is the constraint; the savings are what's left. Most teams see the model bill cut by half or more.
| Request type | Share | Routed to | Cost |
|---|---|---|---|
| Simple lookups | 50% | Light model | $0.004 |
| Standard answers | 30% | Mid model | $0.012 |
| Complex / sensitive | 20% | Frontier model | $0.020 |
Everything here can be built in-house. The comparison is what it takes to keep it running while you also ship product.
| Doing it in-house | With Synessa | |
|---|---|---|
| Writing and maintaining eval checks | A backlog item that loses to features, every sprint | Built from your policies and kept current. 60 active in the worked example. |
| Catching silent regressions | Whoever happens to notice, often a customer | Every change scored, alert within days. |
| Shipping improvements | Scoped, planned, maybe next quarter | Backtested offline on your real traffic. You approve, we ship. |
| The cost-routing layer | A multi-month build plus permanent upkeep | Operated as a service, quality guardrails included. |
| Your team's involvement | An engineer or two, indefinitely | An approval and a monthly report. |
No integration project. We read from where your logs already live: S3, BigQuery, LangSmith or Langfuse exports, or a plain webhook.
A four-minute guided walkthrough of the fleet view, the quality story, and the routing economics. No signup, no sales call attached.