> ## Documentation Index
> Fetch the complete documentation index at: https://docs.kaireonai.com/llms.txt
> Use this file to discover all available pages before exploring further.

# Capability Matrix

> An index of every shipped decisioning capability — algorithms, governance, fairness, observability, supply chain — with links to deep-dive pages

This page indexes every algorithmic surface KaireonAI ships today. Each
section names what the capability does, points at the file that backs
it, and links the deep-dive page where one exists.

## Adaptive Models

Champion / challenger model lifecycle with auto-binning, target
encoding, drift detection, and auto-rollback. Weight-of-Evidence
binning (Siddiqi 2005) groups numeric features into monotone bins
classified as useless / weak / medium / strong / suspicious by
information value. Target encoding (Micci-Barreca 2001) handles
high-cardinality categoricals with smoothed posteriors and optional
training-time gaussian noise. Auto-rollback trips on relative AUC drop,
feature PSI, or scoring-error rate. The model registry is a
status-machine with transactional demotion and approvals trail. Backed
by `lib/ml/auto-binning.ts`, `lib/ml/target-encoding.ts`,
`lib/ml/auto-rollback.ts`, `lib/ml/registry.ts`, `lib/ml/drift.ts`.

## Counterfactual Training

Decision-boundary data augmentation for the gradient-boosted trainer.
Scores every training row with the current model, identifies marginal
rows (predicted probability within `marginalBand` of 0.5), generates K
synthetic neighbors per marginal row by perturbing numeric features
with gaussian noise scaled to feature standard deviation, and appends
to the training set. Backed by `lib/ml/counterfactual-trainer.ts`.
Deep-dive: [Counterfactual Training](/ai-ml/counterfactual-training).

## Explainability

Per-decision explanations across four methods. **TreeSHAP** (Lundberg
2018 Algorithm 2) returns exact Shapley values for `gradient_boosted`
models. **KernelSHAP** (Lundberg-Lee 2017) covers `neural_cf` models.
**Counterfactuals** binary-search the minimum per-feature nudge that
flips the decision. **LIME** (Ribeiro et al. 2016) fits a local linear
approximation by weighted least squares, and global feature importance
aggregates LIME coefficients across instances. Backed by
`lib/scoring/tree-shap.ts`, `lib/scoring/neural-cf-shap.ts`,
`lib/explanations/counterfactual.ts`, `lib/explanations/lime.ts`. Deep-dive:
[SHAP](/ai-ml/shap).

## Multi-Language Narratives

Regulator / agent / customer audience narratives in 12 languages (en,
es, fr, de, pt, it, nl, ja, zh, ko, hi, ar) with deterministic quality
scoring (0-100 + A-F grade). The grader penalizes hedging, missing
cited features, and length excursions. Backed by
`lib/explanations/multi-language.ts`, `lib/explanations/quality-score.ts`. Deep-dive: [Multi-language Narratives](/ai-ml/multi-language-narratives).

## Fairness

Five core metrics — demographic parity, disparate-impact ratio
(four-fifths rule), equal opportunity, equalized odds, per-group
TPR / FPR. Plus advanced metrics: counterfactual fairness (flip the
protected attribute and measure decision-change rate), individual
fairness (Lipschitz ratio scan), intersectional analysis (per-cell
disparate-impact ratios), Gini coefficient, DeLong paired-AUC test, and
two-sample Kolmogorov-Smirnov. Export targets are CSV and EU AI Act
Annex IV-ready HTML. Backed by `lib/fairness/core.ts`,
`lib/fairness/advanced.ts`. Deep-dive: [Fairness & Drift](/ai-ml/fairness-drift)
and [Advanced Fairness](/ai-ml/fairness-advanced).

## Decision Simulator

Shadow-score engine with bootstrap 95% CI for "what if I changed this
weight?" analysis. Multi-scenario compare emits pairwise bootstrap
p-values for "is B significantly better than A?" Weekly seasonality
decomposition produces trend, per-weekday, and residual std with
forward forecast bands. Five tabs ship at `/studio/scenarios`: Run,
Compare, Outcome, Optimization Sweep, Seasonality. Backed by
`lib/scenario/`, `app/api/v1/scenarios/`. Deep-dive:
[Decision Simulator](/ai-ml/decision-simulator).

## Governance

Approval workflow engine enforces four-eyes (requester ≠ approver),
multi-stage chains (`all-of` vs `any-of`), CODEOWNERS-style policy
resolution, and auto-expiry. Multi-stage approvals persist as
ordered approval-request stage rows with sequenced state transitions.
Signed audit-log export uses HMAC-SHA256 on canonicalized payloads
with tamper-evident content hash and DSAR-ready format. Deep-dive:
[Governance four-eyes](/governance-security/governance-four-eyes).

## Decision Provenance

Canonicalized decision-bundle export per `/api/v1/decisions/:id/provenance`.
Bundles ship the request inputs, model + score path, qualification-rule
cascade trace, audit chain rows, and a Sigstore-formatted signature
payload. Operators feed the predicate to `cosign attest --predicate`
to produce SLSA v1 attestations. Backed by
`app/api/v1/decisions/[id]/provenance/route.ts`, `lib/supply-chain/cosign-metadata.ts`, `lib/supply-chain/cosign-sign-blob.ts`. Deep-dive:
[Decision Provenance](/decisioning/decision-provenance) and
[Provenance Cosign](/governance-security/provenance-cosign).

## Durable Pipeline

Resumable DAG executor with checkpoint store (in-memory + raw-SQL
Postgres). On restart the executor skips already-completed nodes and
marks runs terminal on failure. Retry uses exponential backoff with
deterministic jitter. The circuit breaker is tri-state (closed / open
/ half-open) with a typed dead-letter queue sink interface. Backed by
`lib/pipeline/checkpoint.ts`, `lib/pipeline/retry-dlq.ts`,
`lib/pipeline/circuit-breaker.ts`.

## Ranking

Weighted composite scoring across multi-objective weights with hard
constraint filters (budget / inventory / frequency). Lagrangian
relaxation handles soft multi-constraint optimization via dual
sub-gradient. EXP3-IX online bandit tunes weight vectors per context.
Budget pacing supports flat and daytime curves with behind / ahead
multipliers. Goal-seek runs a proportional controller for "hit \$X by
end-of-day" targets. Backed by `lib/ranking/`,
`lib/ranking/lagrangian.ts`, `lib/ranking/online-weights.ts`. Deep-dives:
[Lagrangian](/decisioning/ranking-lagrangian),
[EXP3-IX](/decisioning/ranking-exp3ix),
[Budget Pacing](/decisioning/ranking-budget-pacing),
[Goal-Seek](/decisioning/ranking-goal-seek).

## Decisioning Gates

Four-stage rule taxonomy — Eligibility, Fit Filters, Match Scoring,
Ranking — that decides which offers reach each customer. At decision
time, Eligibility and Fit run as hard filters (a failing offer is
dropped) and Match applies a soft multiplier to each surviving offer's
score; Ranking-stage rules are persisted for authoring but are not yet
applied to ordering. Rule inheritance flows global → category →
subCategory → offer. Conflict detection surfaces priority ties, stage
mismatches, and contradictory thresholds. Time-aware rules support
day-of-week, time-of-day with midnight wrap, date range, blackout
dates, and IANA timezone. Backed by `lib/qualification/`,
`prisma/schema.prisma::QualificationRule`.
Deep-dive: [Decisioning Gates](/decisioning/qualification-rules).

## Negotiation

Shadow-mode runs a 9-violation guardrail validator with full audit
log. Apply mode is gated by a 7-stage pipeline: feature flag, offer
negotiable, tenant + global + auto-error-rate kill switches,
regulator-review cleared, daily apply budget, guardrails. Multi-turn
sessions enforce concession-monotonicity (the agent cannot widen
discount or extend term across turns) and a strict accept / counter /
walk-away state machine. The offline eval harness runs a deterministic
synthetic dataset and emits precision / recall / coverage /
zero-violation-clearance gates. Backed by `lib/negotiation/`,
`lib/negotiation/realtime-apply.ts`. Deep-dives:
[Negotiation Apply-Mode](/decisioning/negotiation-apply-mode),
[Eval Harness](/ai-ml/negotiation-eval-harness).

## GitOps

YAML export and apply for nine resource kinds with three-way merge
(base / ours / theirs) and conflict reporting (Git wins on conflict).
The drift detector classifies each diff as `added_in_prod`,
`missing_in_prod`, or `drift` with dotted-path field diffs. Backed by
`lib/gitops/`, `app/api/v1/gitops/`.

## Supply Chain

CycloneDX 1.5 SBOM emitted from `package-lock.json` with PURL-formatted
components, integrity hashes, and a dependency graph. Cosign SLSA v1
provenance payload builder produces the predicate ready for
`cosign attest --predicate`. Backed by `lib/supply-chain/sbom.ts`,
`lib/supply-chain/slsa.ts`. Deep-dive: [Provenance Cosign](/governance-security/provenance-cosign).

## Connectors

80 registered connector types spanning object
storage (S3, GCS, Azure Blob, SFTP), streaming (Kafka, Confluent
Kafka, Amazon Kinesis), warehouses (Snowflake, Databricks, BigQuery,
Redshift, Snowpipe, Fivetran, Hightouch, Census), databases
(PostgreSQL, MySQL, MongoDB), CRM + support (Salesforce, HubSpot,
HubSpot Marketing, ActiveCampaign, Intercom, Zendesk), CDP + analytics
(Segment, Braze, Iterable, Klaviyo, Amplitude, Mixpanel, PostHog,
Customer.io, MoEngage, CleverTap), messaging (Slack, Microsoft Teams,
WhatsApp Business, Twilio SMS, SendGrid, Postmark, PagerDuty), commerce

* billing (Shopify, Stripe, Mailchimp, Adyen, Recurly, Zuora,
  Chargebee), and workflow (Webhook, REST API, Zapier, n8n, Typeform).
  Catalog is curated as a single registry. Deep-dive:
  [Connectors Expanded](/data/connectors/connectors-expanded).

## Industry Accelerators

Eight vertical packs (Banking, Telco, Retail, Insurance, Healthcare,
Hospitality, Utilities, Media & Streaming), each with 30 offers, 20
decisioning gates, 3 decision flows, and 30 creatives — 240 offers,
160 rules, 24 flows, and 240 creatives in total. Every entity is
regulator-safe wording and cross-referenced for integrity. Backed by
`lib/accelerators/`. Deep-dive:
[Industry Accelerators](/operations-reporting/industry-accelerators).