One principle. Three outcomes. One sharp category.
Training efficiency, evaluation intelligence, and trustworthy-AI operations look like three different products. They're three expressions of a single mechanism — which is what keeps Knobs one platform instead of three claims.
Not more data. The right data — chosen, created, and kept current.
Knobs is the data-intelligence layer for enterprise AI. It runs on one principle — find the high-information regions of a space and act on them deliberately — and turns that into three outcomes: domain models trained on far less data, evaluation that measures where models actually break, and accuracy that holds as data patterns drift.
Whether the space is documents, evaluation cases, or decision states, and whether the right move is to select what already exists or synthesize what's missing, Knobs aims labeling, evaluation, and optimization budget at the few points that change the outcome — fewer examples, deliberately chosen. That is how enterprises operationalize trustworthy AI faster: governed, measured, and continuously re-synced to reality.
This resolves the apparent contradiction between "needs less data" and "creates datasets." Knobs minimizes data volume while maximizing information per example — fewer but the right ones, plus the high-value cases that don't exist yet. Reducing and creating are the same move seen from two sides.
Each goal is the same mechanism pointed at a different space.
Goals 1–3 aren't a widening set of promises. They are one capability — target the high-information regions — applied to data points, to evaluation cases, and to the full lifecycle.
Data efficiency
High-performing domain-specific models on significantly less training data — cutting cost, time, and dependence on massive labeled sets.
Evaluation intelligence
Enterprise-grade evaluation datasets that measure accuracy, hallucination risk, reliability, and task performance across models, prompts, and agent workflows.
Lifecycle & optimization
Trustworthy AI faster — governed training and evaluation data, continuous evaluation, and rapid optimization, extending to learning optimal policies from a compressed view of the state space.
One mechanism in the center; three enterprise outcomes around it. The space changes — points, cases, states — the move does not.
What Knobs is — and just as importantly, what it isn't.
Three of the goals can over-reach if the wording drifts. These boundaries keep every claim anchored to the one principle, so the category stays defensible.
Knobs is
It selects, compresses, and synthesizes the high-information data so you reach a target on far less of it.
It creates the cases that surface where a model breaks — including hallucination-prone, sparse, and uncertain regions.
Living gold and fine-tuning sets, re-synced to production as patterns shift.
It sits across whatever you run and measures and improves it from the data side.
Knobs is not
It produces the data that makes training efficient; you bring and own the model. Knobs enables the build — it doesn't replace it. (Goal 1 boundary)
It builds the datasets that expose hallucination; the faithfulness scoring method rides on top of that data, it isn't information geometry by itself. (Goal 2 boundary)
Policy and state-efficiency is the frontier layer built on the same principle — it applies where a state space is defined, not as a blanket guarantee. (Goal 3 boundary)
It is deliberately the opposite — less data, the right data. Volume is the thing it removes.
The test for any future claim: does it reduce to "select, create, or measure high-information data"? If yes, it belongs in the category. If it implies doing the training itself, scoring faithfulness itself, or optimizing policies in the general case, it's an adjacent capability — useful, but label it as one so the core stays sharp.
The data-intelligence layer that finds the few examples that matter.
Train with less, evaluate with rigor, stay accurate as the world drifts — one mechanism, governed and continuous. That's the whole category, and its edges.