AI Data Management and Governance for Enterprise Generative AI
We design and operate end-to-end AI data management for generative AI: data contracts, lineage, vector stores, prompt and model governance, evaluation, and production monitoring. Your GenAI stays accurate, auditable, and compliant as it scales from first use case to enterprise-wide rollout.
Govern GenAI like a product, not an experiment, with measurable quality and continuous evaluation from day one
- Data contracts, lineage, and access control across structured, unstructured, and vector data
- Retrieval pipelines, embedding stores, and generative AI database design on Databricks, Snowflake, or native cloud
- Prompt, model, and policy registries with approval workflows and audit trails
- Real-time evaluation, drift detection, and generative AI monitoring with SLO dashboards
- GDPR, EU AI Act, and HIPAA-aligned controls for enterprise AI governance
Why is your generative AI stuck in pilot mode without proper data governance?
Most enterprises have shipped GenAI pilots but cannot put them into regulated production. The blocker is rarely the model. It is the absence of AI data management: no data contracts, no lineage for retrieval sources, no evaluation harness, no ownership over prompts, embeddings, or policies. Without governance, every new use case multiplies risk.
Architecture and technical building blocks
Lakehouse on Databricks or Snowflake with Unity Catalog-style lineage across raw, curated, and AI-ready layers.
Generative AI database with tenant isolation, metadata filters, and encryption at rest.
Versioned, approved, and promoted across dev, stage, and prod.
Centralised guardrails for PII, jailbreaks, data residency, and output filtering.
Logs, traces, evaluation scores, and cost metrics wired into one performance monitoring framework.
Review, red-teaming, and feedback loops feeding continuous evaluation.
Reproducible data transformation pipelines with rollback and lineage.
How we deliver: from assessment to governed production
We map current GenAI use cases, data sources, tools, and ownership. Output: maturity scorecard, risk register, gap analysis against EU AI Act and GDPR, and a prioritised roadmap. (2 weeks)
We define the governance framework, reference architecture, tool selection, and RACI across Data, Security, Legal, and Product. Output: signed-off target operating model, platform blueprint, and policy catalogue. (3-4 weeks)
We implement catalogs, registries, retrieval layer, monitoring, and pipelines, then migrate one flagship use case into the governed platform. Output: live GenAI use case with full lineage, evaluation, and audit trail. (6-8 weeks)
We onboard additional use cases, teams, and markets under the same standards. Output: growing portfolio of governed GenAI products, reusable templates, and measurable KPIs. (ongoing)
We operate monitoring, drift detection, re-evaluation, and policy updates as regulations evolve. Output: quarterly governance reports, incident reviews, and continuous quality improvements. (SLA-based)
Benefits of production-grade AI data management and governance
60-80% faster time-to-production for new GenAI use cases via reusable governed templates.
40-70% reduction in hallucination and grounding errors through continuous evaluation.
30-50% lower GenAI infrastructure and token cost via optimised retrieval and caching.
100% auditability of prompts, data sources, and outputs for regulatory review.
Who this service is for
Generative AI governance and AI data management services
We cover the full stack a GenAI program needs to reach regulated production: AI data engineering for pipelines, a governance framework mapped to EU AI Act risk tiers, a retrieval and vector layer, the tooling to run it, and continuous evaluation in production.
Frequently Asked Questions
AI data management for generative AI is the practice of governing every data asset a GenAI system touches: source documents, chunks, embeddings, prompts, fine-tuning sets, and outputs. It covers lineage, access control, quality, versioning, and monitoring, so models stay accurate, compliant, and auditable in production.
An AI data governance framework extends traditional data governance with AI-specific controls: prompt and model registries, evaluation policies, embedding and vector store governance, output logging, red-teaming, and alignment with regulations like the EU AI Act. Traditional governance stops at datasets; AI governance covers the full model and prompt lifecycle.
Yes. We build generative AI Databricks deployments using Unity Catalog for lineage and access control, Delta Lake for governed data, MLflow for model and prompt registries, and Mosaic AI or external LLMs for inference. Databricks is one of our preferred platforms for enterprise AI data engineering at scale.
We track four dimensions: quality (groundedness, factuality, task success), safety (toxicity, PII leakage, jailbreaks), performance (latency, throughput), and cost. Metrics feed a unified performance monitoring framework with SLO-based alerts, automated re-evaluation on drift, and human review queues.
A governed first use case typically goes live in 10-12 weeks: 2 weeks assessment, 3-4 weeks framework and platform design, and 6-8 weeks implementation. Subsequent use cases onboard in 2-4 weeks each, because they reuse the same tools, registries, and policies.
We integrate with the tools your stack already uses: Unity Catalog, Collibra, Alation, Monte Carlo, MLflow, Weights & Biases, LangSmith, Arize, and cloud-native services on AWS, Azure, and GCP. We prefer composable stacks over lock-in, with a clear generative AI database layer and policy engine at the core.
We map each GenAI use case to EU AI Act risk tiers, implement the required transparency, logging, and human oversight controls, and align data handling with GDPR lawful bases, data minimisation, and subject rights. All prompts, outputs, and data accesses are logged with retention policies suitable for regulatory audit.
Make your generative AI enterprise-ready
Stop running GenAI pilots without data contracts, lineage, or monitoring. Get a governed platform that scales safely across teams, markets, and regulations, with measurable quality and cost. Book a 30-minute, no-obligation GenAI governance assessment. You leave with a maturity scorecard, prioritised risks, and a concrete roadmap, whether you work with us or not.
Discovery call
A 30-minute, no-obligation conversation about your GenAI use cases and where governance is missing.
Governance assessment
We map data sources, tools, and ownership against EU AI Act and GDPR requirements.
Roadmap and proposal
You receive a maturity scorecard, prioritised risks, and a concrete roadmap to governed production.