Data Product
Design, build, and operate governed data products with AI-assisted discovery, modelling, and delivery.
The Data Product accelerator uses AI to move from raw enterprise data to production-ready data products: AI-assisted discovery and profiling, semantic product design, governed pipelines, and serving layers for agents, analytics, and regulated workflows.
What this accelerator delivers.
Most enterprises have data platforms, not data products. Pipelines exist, but consumers still negotiate field meanings in spreadsheets. Agents fail because the "customer 360" or "policy exposure" product they depend on was never specified, qualified, or owned. The Data Product accelerator closes that gap by using AI at every stage of the product lifecycle: discovering candidate products from usage and lineage signals, profiling quality and drift, drafting semantic contracts and documentation, and accelerating build-out of governed serving layers in your cloud. Forward Deployed Engineers work with domain and data teams to define products around value streams, not source systems. Each product ships with ownership, SLAs, access policies, eval hooks for downstream agents, and operational runbooks. The outcome is a repeatable factory pattern: from idea to published, auditable data product in weeks, not quarters, with AI reducing manual mapping and documentation while humans retain control of definitions, policy, and sign-off in regulated environments.
AI-assisted discovery, profiling, and semantic product design
Data contracts, ownership, and quality SLAs per product
Serving layers built for agents, analytics, and APIs
Repeatable factory pattern from idea to production product
Concrete artefacts, not slide decks.
Product opportunity map
AI-assisted analysis of lineage, usage, and domain interviews to prioritise high-value data products aligned to agent and analytics workloads.
Semantic product specifications
Data contracts, field definitions, ownership model, quality thresholds, and consumption interfaces for each product in scope.
Governed product pipelines
Production pipelines, transformations, and serving endpoints in your VPC with policy controls and lineage captured by default.
Product catalogue and runbooks
Published product registry, documentation, eval fixtures for agent consumers, and operational playbooks for ongoing quality and drift management.
Four phases to production go-live.
Embed & discover
FDEs embed with domain and data teams to identify value-stream data products and define success criteria for each consumer in scope.
Unify context
Profile source systems, lineage, and existing assets with AI-assisted discovery to baseline quality and semantic gaps per product.
Configure & evaluate
Design contracts, build governed pipelines and serving layers, and wire eval hooks for downstream agents and analytics.
Deploy & monitor
Publish to the product catalogue, hand over runbooks, and establish continuous quality monitoring with optional managed AI oversight.
Where this accelerator applies.
Customer and counterparty data products
Publish governed customer, party, and exposure products that KYC, onboarding, and reconciliation agents consume with auditable contracts.
Policy and claims domain products
Build qualified policy, claims, and peril products with semantic models that FNOL, triage, and underwriting agents trust in production.
Regulatory and finance data products
Accelerate reporting, ledger, and control data products with AI-assisted mapping and continuous quality monitoring for compliance workflows.
Enterprise data product factory
Establish a repeatable pattern for ideation, design, build, publish, and operate cycles across multiple domain teams.
Production outcomes, not pilot metrics.
Faster time from product idea to published, governed data product versus manual catalogue-led approaches.
Reduction in manual field mapping and documentation effort via AI-assisted profiling and contract drafting.
Quality SLA adherence for production data products feeding agent workloads.
Related accelerators
Ready for an AI implementation partner?
Book a discovery call and we'll map your highest-value use case — and exactly how we get it into production.
Frequently asked questions
- How is a data product different from a dataset or pipeline?
- A data product includes explicit ownership, semantic contracts, quality SLAs, access policy, documentation, and consumption interfaces. Pipelines alone do not guarantee trustworthy reuse by agents or teams.
- Where does AI help in building data products?
- AI accelerates discovery and profiling, drafts semantic definitions and documentation, suggests mappings and quality checks, and keeps catalogues current. Domain experts and data owners approve definitions and policy before anything reaches production.
- Do we need a data mesh or platform team already in place?
- No. The accelerator works within your existing cloud and data stack and can introduce product thinking incrementally, starting with one or two high-value products tied to agent or analytics outcomes.
- How do data products connect to agentic workflows?
- Each product ships with contracts and eval fixtures that agent workloads consume via MCP, APIs, or graph queries, so agents reason over qualified, governed data rather than ad hoc extracts.
- Who owns products after go-live?
- Ownership stays with your domain and data teams. FDEs transfer runbooks and monitoring patterns during delivery. Managed AI services can provide ongoing quality and drift oversight if required.