A data foundation that compounds, and AI that makes it through the audit.
Most enterprise data programs collapse under their own ambition. We build the foundation in product-shaped pieces — each one usable on the day it ships, each one composable with the next.
Outcomes we are accountable to
- Decision-grade data products across the business
- Production-grade AI use cases with measurable lift
- A defensible governance posture for regulated workloads
Problems we solve
When teams call us in.
- 01A "single source of truth" that takes six months to add a new metric to.
- 02AI pilots that demo well but cannot be run in production because no one owns the data they need.
- 03Lakehouse migrations that landed the storage but never landed the governance, lineage, or quality.
- 04BI dashboards no one trusts because the underlying definitions disagree across teams.
Methodology
The Dezynum Data Product Sequence
- Phase 1
Map
Inventory the data, the consumers, the contracts, and the gaps. Identify the smallest set of data products that would unlock the most decisions.
- Phase 2
Build
Build those data products end-to-end — ingestion, modeling, contracts, quality checks, lineage — with a real owner and a real consumer on day one.
- Phase 3
Govern
Lineage, quality, and access controls designed to be defensible under audit, not theatrical for compliance reviews.
- Phase 4
Apply
Build the AI and analytics use cases on top — only on data products that already pass the governance bar.
Capabilities
What we deliver inside this practice.
Data platform engineering
Lakehouse, warehouse, streaming, and the orchestration layer that makes them usable. Architected so the next ten data products are easier to build than the first.
Analytics and BI modernization
Move from spreadsheet exports and dashboard sprawl to a metrics layer with shared definitions, owned by the team that publishes them.
Applied AI and generative AI
Production AI patterns — retrieval-augmented generation, structured extraction, copilots — built on data the model is allowed to see.
MLOps and model governance
The infrastructure that makes models reproducible, auditable, and replaceable. Bias, drift, and performance monitored by default.
Data governance, quality, and lineage
Lineage and quality designed for the auditor and the consumer at the same time. Not a metadata project.
Typical pod
Who actually shows up to do the work.
- Data architect
- Senior data engineer
- Analytics engineer
- ML engineer
- Data governance lead
Time to value
Realistic timelines, not sales-deck timelines.
First production data product in 8–12 weeks. First production AI use case in 12–20 weeks.
Ecosystem we work with
Technologies and platforms we have engineering depth in.
We do not list logos for partnerships we do not formally hold. The list below reflects platforms we have built and run on, not vendor relationships.
- Snowflake, Databricks, BigQuery, Redshift
- dbt, Airbyte, Fivetran, Dagster, Airflow
- Kafka, Kinesis, Pub/Sub
- OpenLineage, Marquez, Atlan
- PyTorch, TensorFlow, LangChain, OpenAI, Anthropic, AWS Bedrock
- Looker, Tableau, Power BI, Metabase
Industry context
How this service shifts by industry.
Banking & Financial Services
Risk, compliance, and reconciliation drive the data architecture. Lineage is a regulator requirement, not a nice-to-have.
Healthcare & Life Sciences
Patient and clinical data has access controls baked into the data products themselves, not retrofitted via a layer above.
Retail & Consumer Goods
Inventory, demand, and customer 360 data products typically pay for the platform within 3–6 months.
Engagement models
How clients buy this work.
Data platform assessment
Two to four weeks. Output: the smallest set of data products that would unlock the most decisions, plus the platform architecture to support them.
Build the first data products
A 12–20 week engagement that ships 2–4 production data products end-to-end, including governance and consumer onboarding.
Applied AI program
Multi-quarter program that puts AI use cases into production on top of governed data products. Each use case has a measurable business outcome.
Frequently asked
Questions we tend to get.
Where this matters most
Related industries
Bring us the system, the constraints, and the timeline.
Most engagements start with a working session — not a sales pitch. We will be back to you within two business days, with a senior engineer or partner on the call.
- For RFPs and assessmentsrfp@dezynum.com
- Direct linehello@dezynum.com