Recursive Self
Improvement
at the core.
Dialogus is the reward-function company. We turn production traces into verifiers and replayable environments, then train open-weight models to beat frontier APIs on each agent's own evaluation — at one-tenth the cost.
Production traces become the reward function.
Real deployments contain the hard cases: failures, successful trajectories, tool outcomes, policy decisions, and human judgment. Skylines converts that evidence into an executable verifier and an environment where the agent can be replayed, evaluated, and trained.
From production trace to specialized model.
Build the verifier
Translate outcomes, policies, and human decisions into executable binary and graded rewards.
Reconstruct the environment
Replay full trajectories — model output, tool calls, state transitions, and external results — under controlled conditions.
Train the specialist
Optimize an open-weight model on the operation's real task distribution, not a generic benchmark.
Promote on evidence
Ship only when held-out evaluation shows a higher score and lower cost than the frontier baseline.
Every production cycle sharpens the verifier.
A better verifier creates a stronger training signal. A better model produces new traces. Those traces expose harder failures and extend the environment. Skylines runs that cycle under held-out evaluation, policy constraints, and versioned release gates.
Customer-specific objective
The verifier scores what the operation actually values, including outcomes, constraints, and edge cases.
Reproducible trajectories
The environment reproduces complete agent behavior and its consequences, not isolated prompts.
Open-weight economics
Specialized models concentrate capacity on the real task distribution, reducing inference cost without sacrificing evaluated performance.
Beat the frontier on your own eval.