Discoverability · Trust · Traceability

An engagement to make your data and AI assets findable, understandable, and auditable. We implement a pragmatic metadata strategy, stand up (or fix) your catalog, and wire end-to-end lineage across data pipelines, features, vector stores, models/LLMs, and prompts—so teams can ship faster with confidence.

• Organizations scaling AI/RAG/ML and struggling to find or trust datasets, features, models, or prompts
   
• Teams with multiple platforms (cloud/data lake/warehouse, feature stores, vector DBs) and inconsistent documentation
   
• Leaders who need traceability for risk, compliance, and cost control
   

• Enterprise metadata model that covers datasets, data products, features, models, LLMs, prompts, agents, evaluations, and vector indexes
   
• Working catalog with business glossary, ownership, SLAs/SLOs, and automated onboarding workflows
   
• End-to-end lineage (table → column → feature → model/LLM → endpoint/prompt) for impact analysis, RCA, and audits
   
• Adoption playbook so engineers and analysts actually use the catalog and keep it fresh
   
• Executive visibility: dashboards for coverage, quality, ownership, and changes
   

1. Metadata Strategy & Operating Model
    
   • Canonical entities/relationships (datasets, data products, pipelines, features, models, LLMs, prompts, evaluations, vector stores)
      
   • Roles & RACI (owners, stewards, producers/consumers), contribution standards, review workflows
      

1. Catalog & Glossary Foundation
    
   • Information architecture: domains, collections, tags, classifications (PII/PHI/PCI)
      
   • Business glossary with definitions, synonyms, authoritative sources, and approval flow
      
   • Templates for dataset/model cards and prompt/playbook docs
      

1. Lineage Design & Implementation
    
   • Technical lineage ingestion (ELT/ETL, notebooks, orchestration, streaming)
      
   • Feature lineage: from raw tables to features/embeddings to models and endpoints
      
   • LLM lineage: prompts, tools, RAG chains, retrieval scopes, and output policies
      
   • Change impact rules and RCA patterns wired to ticketing/CI
      

1. Automation & Policy-as-Metadata
    
   • Auto-harvest from warehouses, lakes, schedulers, CI/CD, registries, and vector DBs
      
   • Data classifications, retention, access tiers, and masking policies attached as metadata
      
   • Webhooks to enforce contribution quality (owners, SLAs, glossary link, lineage)
      

1. Adoption & Enablement Kit
    
   • “Golden path” onboarding flow for new assets
      
   • Contribution scorecard & gamified nudges
      
   • Training for stewards, engineers, and analysts
      

1. Executive Pack
    
   • Coverage metrics, lineage completeness, ownership health, catalog adoption
      
   • Roadmap for next 2–3 quarters with dependencies and KPIs
      

Week 1: Discover & Align
Stakeholder workshops; current tools review (catalog/lineage/registry); inventory sampling; pain-point map.

Week 2–3: Design & Prototype
Metadata model + glossary design; lineage blueprint; select 1–2 domains for a working pilot; auto-harvest POC.

Week 4–6: Implement & Embed
Stand up catalog IA, glossary workflows, and lineage ingestion; wire CI hooks; publish “golden path”; enable dashboards; run first steward council.

Week 7: Readout & Scale Plan
Finalize artifacts, adoption plan, and scale roadmap (quarterly phases).

(Can compress/expand based on scope and platform readiness.)

• Sources: cloud DW/lake, streaming, notebooks, ETL/ELT/orchestration, BI/semantic layers
   
• AI-specific: feature stores, model registries, LLM gateways, vector databases, prompt stores, evaluation harnesses
   
• Governance: ownership, classifications, DSAR pointers, lineage for audits & safe change control
   
• DevOps: CI checks for metadata completeness; break-glass rules; change impact to issue trackers
   

• Catalog coverage for Tier-1/2 datasets ≥ 90% with owners, SLAs, and glossary links
   
• Column-level lineage coverage in regulated domains ≥ 80%; 100% process lineage for critical pipelines
   
• 100% of models/LLMs registered with cards, datasets, prompts, and evaluation links
   
• Time to perform impact analysis ↓ 60%; time to find authoritative dataset ↓ 50%
   
• Contribution freshness: ≥ 95% of modified assets auto-updated within 24 hours
   

• Read-only access to platforms (warehouse/lake, orchestration, feature/vector stores, registries)
   
• Existing glossaries, taxonomies, and policy standards (if any)
   
• Named stewards/owners for the initial pilot domains
   

• Empty catalog syndrome: automate harvesting and enforce minimum contribution standards
   
• Stale lineage: event-driven ingestion and CI triggers keep it current
   
• Over-engineering: start with pilot domains and a smallest-viable model, then scale
   
• Compliance gaps: attach classifications/policies to assets and expose lineage to auditors
   

• Data product factory (templates, review boards, CI policies)
   
• BI/semantic layer alignment and metric definitions
   
• Cost/efficiency dashboarding (unused tables, orphan models, duplicate prompts)
   
• Vendor selection and migration support
   

We balance rigor with adoption. You’ll get a catalog engineers actually use, lineage you can trust in an audit, and metadata that powers safer, faster AI delivery.

Share your current stack (catalog/lineage tools, warehouses, feature/vector stores, registries) and 1–2 candidate domains. We’ll schedule a 30-minute scoping session and tailor the pilot and scale plan to your environment.