Xong AI
Product

Model Adaptation

Increase accuracy with planned training intervals using verified sessions, corrected outputs, and human rewards with evaluation gates to prevent regressions.

Model adaptation workflow illustration

What it does

Xong adapts open-source and customer-deployed models to your language, terminology, and edge cases powered by session traces and human feedback.

Interval training
Train on a cadence (weekly/biweekly/monthly) using verified session outputs and hard cases.
Human rewards
Correct/incorrect signals, field edits, and preference choices become reward data.
Evaluation gates
Regression suites and golden sets prevent accuracy drops across model or prompt changes.

How it works

The adaptation loop

Turn real operations into measurable model improvements without losing control.

  1. 1) Capture sessions (inputs, sources, tool calls, outputs)
  2. 2) Collect HITL corrections and reward signals
  3. 3) Build datasets (golden sets + hard cases)
  4. 4) Train / fine-tune on a defined interval
  5. 5) Evaluate + regression tests
  6. 6) Roll out new model versions with monitoring
Result: accuracy increases as the system learns your real formats and edge cases.

Feature highlights

Accuracy improves continuously

Interval training
Train on a cadence (weekly/biweekly/monthly) using verified session outputs and hard cases.
Human rewards
Correct/incorrect signals, field edits, and preference choices become reward data.
Evaluation gates
Regression suites and golden sets prevent accuracy drops across model or prompt changes.
Cost & latency tuning
Use SLMs for routine steps and reasoner LLMs for complex decisions; distill where needed.
Tool-aware learning
Train on tool usage patterns and structured outputs to reduce operational errors.
Governed lifecycle
Model registry, versioning, rollout policies, and audit logs for each deployment.

Model improvement signals

Track accuracy gains without regressions.

Model Adaptation tracks evaluation packs, rollout gates, and cost/latency trade-offs across versions.

  • Golden set coverage and regression status per release.
  • Reward signal quality and volume from HITL.
  • Cost and latency deltas per model version.
Adaptation signal map
Illustrative signals across data, evaluation, and rollout stages.
Accuracy gains
Measured
Evaluation packs track deltas.
Regression guard
Gate-driven
Block releases that fail tests.
Training cadence
Planned
Weekly/biweekly intervals.
Cost profile
Optimized
Tune SLM vs LLM usage.

Proof

Measurable gains without regressions

Production teams improve accuracy with evaluation packs, rewards, and safe rollouts.

Continuous improvement network illustration
Evaluation pack

Want to start with a measurable accuracy baseline?

We will build an evaluation pack (golden set) from your real documents and workflows, then track improvements across versions.

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