Data Flywheels that Slash Inference Spend

Generative AI gets expensive fast. The antidote is a data flywheel that keeps accuracy up while routing traffic to the cheapest acceptable path. In one deployment, this cut inference costs by 98.6%.

1. Capture Feedback Signals

Blend explicit ratings, click-through, document opens, and downstream task completion. Each signal earns a weight so we can prioritise the most trusted feedback.

2. Route Intelligently

We route traffic based on difficulty and sensitivity. Easy queries hit distilled models; complex ones escalate to high-parameter LLMs or human review. Routing rules update weekly as new eval data arrives.

3. Distil on High-Signal Data

Using NVIDIA NeMo and LoRA adapters we retrain task-specific models on the highest performing examples. Each iteration replaces a chunk of expensive calls with cheaper, faster responses.

4. Automate Evaluation

Guardrails monitor factual accuracy, citation coverage, tone, and leakage. TruLens and custom judges flag regressions before users notice.

flywheel.loop({
  ingestFeedback()
    .rank(by=[signalStrength, recency])
    .select(top=5000)
    .distil(model="enterprise-small")
    .eval(metrics=["factual", "compliance", "cost"])
    .promote(onSuccess)
})

The magic isn't the model trick. It's giving the business a predictable way to trade accuracy, latency, and spend without waiting for a quarterly review.