AI, RAG & Vector Store Optimization
Improve AI accuracy, retrieval quality, grounding, auditability, and model reliability. KRYOS reverse-engineers where AI systems fail and rebuilds them on evidence-gated foundations.
Where this pillar plugs into the KRYOS service stack.
18 named deliverables in this pillar.
Each is engageable as a diagnostic, sprint, project, managed program, or board / investor advisory pack.
AI Data Asset Registry
A single catalog of every training set, eval set, corpus, vector store, model, and prompt asset.
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AI Data Asset Registry
A single catalog of every training set, eval set, corpus, vector store, model, and prompt asset.
Model inventory, training data manifests, vector store metadata.
Source discovery → asset classification → utility tagging → governance overlay.
AI Data Asset Registry with ownership and utility class.
Free 10-Asset AI Inventory.
CTOs, CDOs, AI governance leads.
Training Data Valuation
Defensible value of the corpora driving model utility and competitive position.
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Training Data Valuation
Defensible value of the corpora driving model utility and competitive position.
Training data manifests, model performance attribution.
Source map → marginal-contribution analysis → cost / replacement / utility layering → band output.
Training Corpus Valuation Pack with P10/P50/P90.
Evaluation Dataset Valuation
Value the eval sets that determine which models ship - and which fail.
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Evaluation Dataset Valuation
Value the eval sets that determine which models ship - and which fail.
Eval manifests, model leaderboard history.
Coverage map → uniqueness scoring → contribution analysis → valuation band.
Eval-Set Valuation Memo.
Vector Store Valuation
Quantify the asset value embedded in your retrieval index, not just its compute cost.
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Vector Store Valuation
Quantify the asset value embedded in your retrieval index, not just its compute cost.
Vector store metadata, query logs, retrieval performance.
Index audit → query value attribution → reconstruction cost → utility band.
Vector Store Valuation Pack.
RAG Evidence Graph
A live graph of which sources actually ground which answers, with weak links flagged.
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RAG Evidence Graph
A live graph of which sources actually ground which answers, with weak links flagged.
Source corpus, retrieval logs, answer-source citation traces.
Source graph build → answer-to-source binding → weak-evidence flagging.
RAG Evidence Graph with weak-link ledger.
RAG Diagnostic and Failure Audit
Find why your AI system retrieves weak evidence, hallucinates, or misses sources.
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RAG Diagnostic and Failure Audit
Find why your AI system retrieves weak evidence, hallucinates, or misses sources.
Golden queries, model answers, retrieved chunks, vector metadata.
Query classification → retrieval scoring → evidence-gate review → hallucination failure analysis → repair roadmap.
RAG Failure Map and Repair Roadmap.
Free 10-Query RAG Audit.
CTOs, AI leads, compliance-sensitive AI teams.
Chunking and Knowledge Architecture Redesign
Rebuild how source documents are decomposed so retrieval actually grounds answers.
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Chunking and Knowledge Architecture Redesign
Rebuild how source documents are decomposed so retrieval actually grounds answers.
Source corpus sample, existing chunking config, retrieval logs.
Corpus profiling → chunking strategy bake-off → retrieval lift measurement → migration plan.
Knowledge Architecture Blueprint.
Cognitive Middleware API Design
Standardize how your products call models, with evidence, guardrails, and observability built in.
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Cognitive Middleware API Design
Standardize how your products call models, with evidence, guardrails, and observability built in.
Existing AI calling patterns, target product surface.
API surface design → guardrail layer → telemetry hooks → adoption plan.
Cognitive Middleware Spec.
Hard-Mode Compliance Layer
Make AI outputs survive compliance, legal, and regulated-industry scrutiny.
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Hard-Mode Compliance Layer
Make AI outputs survive compliance, legal, and regulated-industry scrutiny.
Use case scope, regulatory regime, current AI outputs.
Risk framing → policy gate design → evidence requirement → human-in-the-loop protocol.
Compliance Gate Spec for AI outputs.
Decision Intelligence Layer
Wrap raw model outputs in the decision context - thresholds, alternatives, and escalation paths.
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Decision Intelligence Layer
Wrap raw model outputs in the decision context - thresholds, alternatives, and escalation paths.
Target decision, current model outputs, decision-owner playbook.
Decision framing → option enumeration → confidence calibration → escalation routing.
Decision Intelligence Schema.
Prompt Library Assetization
Convert your prompt collection into a versioned, governed, evaluatable asset.
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Prompt Library Assetization
Convert your prompt collection into a versioned, governed, evaluatable asset.
Existing prompts, target use cases.
Inventory → taxonomy → eval harness → versioning and ownership model.
Prompt Library Registry with eval scores.
Hallucination Failure Ledger
A continuous ledger of where AI made things up, why, and how it was caught.
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Hallucination Failure Ledger
A continuous ledger of where AI made things up, why, and how it was caught.
Production AI traffic sample, current evaluation harness.
Failure capture → root-cause classification → mitigation routing → trend reporting.
Hallucination Failure Ledger with monthly trend.
Agent Trace Intelligence
Make multi-step agent behavior auditable, reproducible, and debuggable.
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Agent Trace Intelligence
Make multi-step agent behavior auditable, reproducible, and debuggable.
Agent execution traces, tool definitions.
Trace schema → step-level evidence binding → failure-mode classification.
Agent Trace Console.
Model Telemetry Asset Review
Treat production telemetry as an asset, not exhaust - and value it accordingly.
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Model Telemetry Asset Review
Treat production telemetry as an asset, not exhaust - and value it accordingly.
Telemetry inventory, current retention.
Stream classification → utility attribution → retention optimization → valuation band.
Model Telemetry Asset Memo.
RAG Poisoning Risk Review
Find where adversaries could corrupt your retrieval index or its sources.
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RAG Poisoning Risk Review
Find where adversaries could corrupt your retrieval index or its sources.
Source ingestion pipeline, vector store, access logs.
Attack surface map → poisoning scenario tests → mitigation roadmap.
RAG Poisoning Risk Report.
Free Single-Vector Poisoning Scenario.
AI Governance Asset Dossier
A board-ready dossier proving your AI systems are governed, evidenced, and defensible.
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AI Governance Asset Dossier
A board-ready dossier proving your AI systems are governed, evidenced, and defensible.
AI inventory, current governance artifacts.
Inventory → gap analysis → dossier assembly → red-team challenge.
AI Governance Dossier.
Continuous RAG Evaluation
An always-on eval harness that catches retrieval regressions before users do.
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Continuous RAG Evaluation
An always-on eval harness that catches retrieval regressions before users do.
Golden query set, RAG endpoint.
Harness design → metric selection → CI integration → alert routing.
Continuous Eval Console with regression alerts.
AI Benchmark Product Design
Turn your evals into a market-credible benchmark product.
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AI Benchmark Product Design
Turn your evals into a market-credible benchmark product.
Existing eval scope, candidate audience.
Benchmark scoping → integrity controls → publication pack.
AI Benchmark Spec and Launch Pack.
The KRYOS standard.
Evidence-tagged inputs
Every claim carries a provenance and quality tag - no untraceable assertions.
Red-team review
Independent adversarial challenge before delivery - by design, not by exception.
Executive-ready output
Decision packets, not slide decks. Sized for the role that has to act.
No unsupported guarantees
We do not promise valuations, funding, or regulatory outcomes. We deliver evidence.
One Hypercube. Six pillars.
Begin with a forensic data asset assessment.
A focused engagement that maps your data estate, scores assets against KRYOS frameworks, and produces a board-ready brief on what to recognize, value, productize, and capitalize.