Replace the frontier model only when a smaller one beats it on your own eval.
Speedbin Labs turns your production traces into a held-out eval, trains a small open-weight specialist on them, and routes live traffic to it only when it wins on quality, latency and cost. Sub-400 ms turns, weights you own, deployable in Singapore.
Scope a four-week pilot Free for design partners. One workload, your traces, our work. You keep the weights.The loop runs behind the SDK you already use
Point your OpenAI or Anthropic client at our gateway. Nothing else in your stack changes until a specialist has earned its place.
Capture
Traces, tool calls and your reviewers' corrections, redacted at ingest and stored in Singapore.
Evaluate
Accepted runs become a held-out eval for that exact workload: partial credit per check, strict pass when every check holds. The set is frozen before any training.
Train
Prompt and output control first, then a small open-weight specialist fine-tuned on your traces. Every candidate is scored against the same frozen set.
Promote
A candidate takes live traffic only when it beats the incumbent on quality, latency and cost. Shadow traffic, then canary, with the frontier route kept as fallback.
No model is promoted on a demo. It is promoted on a held-out set it has never seen, against thresholds you set.
Most teams swap models the way they'd never swap a database: try it on a few examples, it looks fine, ship it. Then a provider update changes outputs silently and nobody has a regression test that would have caught it.
Speedbin Labs treats a model change like any other deploy. There is a test suite built from your real traffic, a pass threshold you define, a staged rollout, and a rollback path. The specialist earns production the same way a code change does.
Built for teams whose model is in the critical path
Two kinds of workload benefit most. Both run repeated calls at volume, and both pay for every millisecond and every unexplained change.
Voice and real-time agents
The model turn is the long pole in a live call. A hosted frontier model returns a short structured reply in roughly two seconds; a specialist returns it in under 400 ms.
- Intent, slot-fill and policy checks on every turn
- Latency measured per attempt, p50 and p95
- Specialist weights yours to run anywhere
Vendors into regulated finance
MAS's AI Risk Management Guidelines ask for lifecycle controls, evaluation proportionate to risk, and third-party AI oversight. A model your client cannot pin or audit is the gap a risk team will find.
- Task-specific eval with a frozen held-out set
- Every promotion and rollback logged
- Weights and data resident in Singapore, or in the client's own environment