The Kaia Intelligence Engine
Leaderboards show models improving.
We show your product improving.
Every published trend line in this market tracks a foundation model. None tracks a product — release over release, with the corrections that moved it. The engine beneath every Kaia vertical closes that gap: pre-trained where it learns, deterministic where it counts, improved by your experts’ corrections, measured on a frozen evaluation set, shipped with the evidence.
Product accuracy, release over release
illustrative · targetsSame frozen, versioned evaluation set every release. Every delta reproducible from a stored evidence packet — the run, the inputs, the grader output, the correction that changed it. Targets labeled as targets until measured; no numbers publish until the eval gate clears — then this chart becomes real, release over release.
How it learns
Five learning layers. Every correction routed to the right one.
Not every correction should retrain a model. The triage engine classifies each one and routes it — fast and automatic where safe, human-gated where it matters.
Context augmentation
Client-specific factual fixes, live in context.
auto · ≥90% conf.Knowledge update
Universal facts and context, batched overnight.
auto · ≥90% conf.Model adaptation
Repeated patterns (50+ in 30 days) become tuning candidates.
human gateReward model
Format, style, and preference patterns, weekly.
human gateArchitecture
Fundamental capability gaps, monthly review.
human gateThree Layers, One Intelligence
Named After My Daughter — Because Learning Never Stops
Each layer builds on the one below. Product truth comes first, then the surrounding ecosystem compounds delivery, implementation, and new vertical creation.
All three layers feed the same Closed-Loop Intelligence Engine
Every correction, every interaction, every deployment makes the entire platform smarter. Transparent benchmarks track it.
Confidence Thresholds
Different industries demand different standards.
eDiscovery, document classification, and entity extraction with governed review thresholds.
Clinical data processing, milestone tracking, and provider integration designed for HIPAA-regulated work.
Healthcare payer claims triage and review with STP optimization and fraud detection.
Why it compounds for you
The model may be rented. The intelligence you create is yours.
Capture
Every correction recorded with provenance — who, what, why, under which model and policy — inside your tenant.
Evaluate
Candidate improvements run against the frozen eval set. Nothing silently retrained, ever.
Release
Approved deltas ship versioned, rollback armed. Structural learnings transfer across the ecosystem — with consent, never PHI/PII.
Compound
Each release starts from accumulated intelligence. Per-vertical confidence floors: 0.90 regulated, 0.85 general.
The Continuous Learning Advantage
Every accepted correction becomes a governed signal that compounds across the Intelligence Engine. Kaia improves routing, evidence handling, and review guidance — with the improvement published as measured, release over release — while authorized teams retain final regulated authority and Kaia makes no customer-data baseline-training claim.
Transparent Benchmarks
RRES: Routing & Resolution Efficiency Score.
Routing Accuracy
% of corrections routed to the correct learning layer after review or verification
Improvement Verification Rate
% of applied corrections that produce verified output-quality improvement
Correction-to-Improvement Latency
Time from correction submission to measured improvement in the governed loop
Cost Per Improvement
Human, compute, and infrastructure cost per verified improvement
Human Override Rate
% of reviewed corrections where humans change the automated routing decision
Sample Composite RRES Score
This sample score illustrates how the five metrics roll into a governed benchmark readout. Public RRES publication remains gated until production baselines are ready.
Why this isn’t a marketing chart
The evidence standard behind every number.
Frozen, versioned eval sets
The evaluation set is fixed and versioned before any number publishes — improvement can't be gamed by moving the goalposts.
Reproducible evidence packets
Every quality figure resolves to a stored packet: run, inputs, grader output, the correction that changed it — per tenant, per release.
Path to independent attestation
Designed so a third party can recompute our metrics on a sample basis (AICPA AT-C §215 agreed-upon procedures) — making "auditable" literal, not rhetorical.
Privacy by Design
Your data stays yours. The process intelligence compounds.
Tenant Isolation
Every client's data is completely isolated. No cross-contamination. No shared model weights. Federated architecture from day one.
Federated Learning
Corrections improve governed routing and process posture without turning customer data into baseline training. Tenant data remains scoped while product intelligence compounds through approved patterns.
Compliance Ready
Built for HIPAA, SOC 2, and state-level regulatory requirements. Audit trails for every classification, correction, and model update.
See It in Action
The Intelligence Engine powers every vertical.
Six further regulated verticals — Health, Pharma, Real Estate, HR, Auto Loan, and Government — run on the same Intelligence Engine.
See your own corrections move the number.
Design partners run one bounded workflow on the engine — and watch the time series start with their data.
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