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Purpose-Built AI deserves
Purpose-Tuned models.

You've specialized everything — retrieval, evals, your agent graph. Your models shouldn't be the exception.

Purpose-tune for
LLM Judges·Tool Calling·Classification·Extraction·Embeddings·Model Routing
Try it outSee how it works
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How it works

Models shaped for the job.

Emissary shapes model layers, attention, and output heads for the exact job each component does in your agent graph — and then enables you to tune them with feedback over time.

LLM Judge
task · pairwise response quality · 5-class rubric
Input from your graph
Response to evaluate
"The refund policy is 30 days from delivery, not purchase date. You can initiate it from Settings › Orders."
nodeJudge in eval loop
baseLlama 3.1 8B
calls/day~120k
Output head 5-class rubric, calibrated
Excellent (5)
0.78
Good (4)
0.18
Acceptable (3)
0.03
Poor (2)
0.01
Wrong (1)
0.00
Calibration lossECE 0.012 · trained on your rubric
Base LLM as judgePurpose-tuned
5× faster
Latency
Smaller, shape-specialized models run inference in a fraction of the time.
80% cheaper
Cost per call
Fewer params doing exactly the right thing, not a generalist doing everything.
∞
Improves on your signal
Every accepted / rejected response becomes training data. The model gets better as you use it.
The full lifecycle

A complete, drop-in replacement for your model API.

Training, inference, monitoring, and retraining — in one platform. Swap out your current API and get everything you had, plus the parts you've been stitching together yourself.

01

Training

Every technique in the modern tuning stack — plus our purpose-design primitives.

  • SFT · supervised fine-tuning on your data
  • RL · from human & programmatic feedback
  • LoRA · parameter-efficient adapters
  • Purpose Tuning · layers, attention & heads
02

Inference

Deploy how your workload needs — no infra team required.

  • Serverless · pay per token, zero cold starts
  • Dedicated · reserved GPUs, predictable latency
  • Autoscaling · scales to traffic, down to zero
  • OpenAI-compatible · one URL change and you're live
03

Monitoring

Every signal you'd want from a production model — in-product, not in a grafana screenshot.

  • Logs · every request, input & output
  • Uptime · per-endpoint SLOs & alerts
  • Throughput · TPS, p50/p99 latency, cost
  • Drift · quality regressions flagged automatically
04

Retraining

Built-in. Your model learns from its own production signal without you building the pipeline.

  • Scheduled · nightly, weekly, or event-triggered
  • Feedback-native · thumbs, corrections, click-throughs
  • Shadow + promote · validate before routing traffic
  • Rollback · any version, one call away
Built for enterprise

Production scale from day one.

Deployed inside the agent stacks of the fastest-growing AI companies. Capacity, isolation, and reliability you don't have to negotiate for.

17M+
requests per month
40B
tokens per month
99.99%
uptime SLA
0
rate limits
Enterprise-grade data isolation. Dedicated training runs and inference endpoints per tenant.
Your signal never trains anyone else's model.
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Let's talk.

Tell us about your use case and we'll get back to you within one business day.

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