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Specialists, Adapters
Self-specializing inference
Inference That Gets Smarter And Cheaper The More You Use It.
One Giant Model For Every Call.
frontier · every task · $$$ per call
Your App Asks The Same Questions All Day.
// the problem
Frontier models are built to do everything. Your app runs many agents, each doing only a few things, thousands of times a day. Every one of those calls is billed like it might need to do everything.
// the move
Verseta is self-learning. It watches your traffic, finds the jobs your agents keep doing, and trains a specialist for each one on your own data. A specialist takes a job only when it beats your original model at it: the same quality for a fraction of the cost. Then it keeps learning as your app changes.
// zero labels
Nobody annotates anything. Your traffic is the dataset.
// zero rewrites
Same SDK, same prompts, same tools.
// always a way back
Unmatched or unproven calls go to your original model.
┌──────────────────────────────┐
your app ─────▶ │ frontier model $$$/call │ ─────▶ every call, forever
└──────────────┬───────────────┘
│ observe
▼
┌─────────┬──────── verseta learns your app ────────┬─────────┐
│ │ │ │ │ │ │
▼ ▼ ▼ ▼ ▼ ▼ ▼
order warranty returns repair review prod_q ticket
live ✓ live ✓ live ✓ live ✓ live ✓ learning drift ↺
│ │ │ │ │ │ │
└─────────┴─────────┬─────────┴─────────┘ └────┬────┘
▼ ▼
served by its specialist your original model
↻ watched, judged, relearned. continuously.
EXP-007, Unedited.
// the run
We pointed Verseta at a retail app with seven agents on Claude Sonnet 5.5 and Haiku 5.5. It found all seven jobs by itself. Five now run on specialists that match the original model's quality at a fraction of its cost. One isn't good enough yet. One was handed back when its prompt changed. One consumer GPU. Every result is below.
// shipped
5 / 7
agent jobs taken over by a specialist
// median step
0.67–1.09s
per agent step on one consumer GPU
// projected cost
29× less
than Claude Sonnet 5.5. $0.08 per million tokens, blended in and out, on one rented consumer GPU. Under Haiku-class pricing.
paired checks vs. original
// never off the hook
Shipping isn't the finish line. Verseta keeps judging every specialist against your original model on live traffic. When EXP-007's ticket router got a new prompt, its score fell from 0.95 to 0.80, and Verseta handed the job back within 20 checks.
It Learns Your App.
Then It Takes The Load.
0x01 // observe
Verseta sits between your agents and their model and sees every call. Your users notice nothing.
0x02 // understand
It finds the jobs your app does over and over. No labels, no setup.
0x03 // learn
It trains a specialist for each job on your own traffic, then keeps training it.
0x04 // prove
Every specialist goes head to head with your original model on real traffic. It takes the job only when it wins: same quality, far lower cost.
0x05 // take over
Proven jobs move to their specialist. Everything else still goes to your model.
0x06 // keep learning
When your app changes, Verseta notices, steps back, and learns the new version.
// two lines
Keep the SDK your agents already use. Change where it points. Anthropic- and OpenAI-compatible endpoints, tool calls included.
- learns from
- your production traffic
- labels
- none
- setup
- two lines
- quality bar
- set by your own model
- fallback
- your original model
- works with
- Anthropic · OpenAI SDKs
guest@verseta-labs ~ $ verseta request-access --design-partner
» 2 slots · teams running agents in production · we bring the receipts
[ RUN ] request access ▍