AI Research Lab · est. 2026

Solving the economics of AI agents.

Dawnfire AI Lab is an AI research lab making agents economically viable. We train them smaller, synthesize the data they need, and scale them far beyond the demo.

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Train
Small models, frontier quality
Data
Synthesized for your scenario
Scale
Smaller models, same task
Scroll
// 01 — What we do

Three levers.
One outcome: agents that pencil out.

We work with teams shipping agents in production. Every engagement targets the same goal — same task, dramatically lower cost, dramatically higher reliability.

01 · Train

Agentic RL Training

Post-train compact open models with verifier-driven RL to reach frontier-class performance on your workflows — at a fraction of the inference cost.

Open modelsRLVRFrontier-class quality
02 · Data

Agent Task Data Synthesis

We generate high-quality, scenario-specific training data for your agent — covering the long-tail trajectories real workflows hit but public datasets never see.

Domain trajectoriesVerifier curationLong-tail coverage
03 · Scale

Harness & Agent Optimization

Re-architect the prompts, tools, and control loop around your agent so a smaller model can match — or beat — your current frontier-model deployment on the same task.

Prompt & tool designCost / task ↓Smaller-model swap
// 02 — The Math

The unit economics
have to work.

cost / task → ↓ 12×
tokens / decision → ↓ 4.3×
tool-call latency → ↓ 7×
success rate → ↑ research
long-horizon recovery → ↑ research
dawnfire ~ agent/economics
// before — demo-grade
> run_agent("refactor service")
  steps: 38 · tokens: 412,000
  tools: 27 calls · 6m14s
  cost: $2.94   verdict: unshippable

// after — dawnfire stack
> run_agent("refactor service")
  steps: 22 · tokens: 96,000
  tools: 11 calls · 54s
  cost: $0.23   verdict: production
// 03 — What we believe

Intelligence is the cheap part now.
Putting it to work is the hard part.
We are closing the gap.

i.

An agent that can't pay for itself isn't a product — it's a science fair project. We measure success in dollars per task, not vibes.

ii.

Capability and cost are the same problem. Cheaper models unlock workflows expensive ones can't justify. We chase both at once.

iii.

Demos lie. Long-horizon agents only get honest at scale, with real users, under real budgets. That's the only bar we trust.

iv.

A small lab with the right people will out-ship an army. We hire only when it makes the team faster.

// Let's build

The economics won't fix themselves.
Let's talk.

Whether you're an enterprise deploying agents in the wild, a researcher working on inference and post-training, or a builder ready for your next chapter — we'd love to hear from you.

→ contact@dawnfire.ai