Most data platforms don’t need another rebuild. We keep, fix and build.
Lakehouses, pipelines and governed data foundations on Fabric, Databricks or Snowflake, built from what already works and engineered to carry analytics and AI.
Case study · Manufacturing and distribution · Supply chain
The planning system stayed. The layer on top did the work.
A large food manufacturer and distributor ran on a planning backbone that worked. We did not replace it. An AI and analytics layer on top fed cleaner demand and lead-time signals into the existing planner, mapped supply relationships and optimized routes.
Six things we build, each handed to a team that can run it.
The platform is decided on the diagnostic, not assumed.
Lakehouse and cloud data platforms
Architecture and implementation, with the security, governance and cost controls designed in from day one.
Batch and streaming pipelines
Pipelines that alert when they fail, built as the pattern every later pipeline follows.
Migration and modernization
On-premises and legacy estates moved to the cloud, starting from what should not move at all.
Integration and master data
Disparate sources brought into one governed repository, with one definition of each thing that matters.
DataOps and platform operations
Continuous delivery, automated testing and monitoring for data, so the platform stays reliable as it grows.
Data products and semantic models
Shared definitions modeled once and certified, so three teams stop producing three versions of the same metric.
Common ecosystems include Azure and Microsoft data services, Fabric, Snowflake, Databricks, Power BI, modern ELT and ETL, and catalog and governance tooling.
Stood up properly means four things.
Acceptance criteria, written down before the build starts and tested at handover. The last test is always the same: your team runs it without us.
Fails loudly A broken pipeline raises an alert. Nothing fails silently.
Traceable Every table traces to its source in the lineage graph.
Governed Role-based access enforced. The wrong role cannot read the data.
Costed Cost per run and per query known at the planned volume.
Why it matters
Everything the business wants runs on this layer.
Reporting people trust, one view of each customer or patient, automation, and the models and agents built on top all inherit their limits from the platform underneath.
Executive and operational reportingCustomer or patient 360AutomationModels and agents
Governed data platform
ERPCRMFilesEvents
How to engage
Three ways in. All run on evidence, not a deck.
01 · Prove the pattern
Data Platform Accelerator
Diagnose the estate, prove the target pattern on a bounded scope, and leave with a working foundation your team can extend. Two to four weeks, because we adapt proven patterns rather than starting from zero. Fixed scope. Fixed fee agreed in writing first.
The diagnostic. Every in-scope component gets a keep, fix or build call, with the reason.
A governed, costed foundation. The chosen platform pattern with access, controls and unit economics built in.
First pipelines live. Implemented on one reusable pattern for what follows.
Your team can run it. Runbook, handover and a pattern they can operate and extend without us.
Your estate diagnosed, then built out on the platform that fits it: every source, workload and team, with migration, integration, master data and the models downstream. Scoped and sequenced in a proposal.
The diagnostic, with a reason behind every keep, fix or build call
A platform governed and costed from day one
Pipelines, data products and models moved on one proven pattern
Once the platform is live: continuous delivery, automated testing, monitoring and cost controls, with senior engineers alongside the team that runs it.
How we work. Every engagement follows our Data IQ™ methodology, from diagnosis through implementation and handover. See how we work
Before you commit
Questions we get every week.
What do we get from the Data Platform Accelerator?
The diagnostic: every component through the seven tests, with its keep, fix or build call and the reason. A working foundation on the chosen platform, with its access and governance model. The first pipelines live, built as the pattern for the rest. And the runbook, walked through with the team that will own it. Two to four weeks, fixed scope, with a fixed fee agreed in writing before work starts.
Do we have to replace our data warehouse?
Usually not. The warehouse is often sound; what is broken is around it, such as ingest that fails silently, missing lineage, or definitions that drifted apart. Every component goes through the same seven tests. What passes is kept, what is worth saving is fixed, and only what does not exist is built.
Our migration stalled. Do we have to start again?
No. Migrations usually stall on things nobody designed: a pipeline pattern, an access model, one set of conventions across two teams. We find the missing pattern, build it once, and what is stuck follows it. What already moved is kept.
Which platform should we use: Fabric, Databricks or Snowflake?
If you have already chosen, we build on it. If you have not, the decision is made on the diagnostic: your existing cloud footprint, the workloads the platform has to carry, and the team that will operate it. We work across all three, and the recommendation is not conditioned on an exclusive vendor relationship.
Will our team be able to run it afterwards?
That is the seventh test and the last acceptance criterion. Your team gets the runbook to operate, extend and add a source, is walked through it on the platform itself, and adds a new source from the template before handover.
Do you only run accelerators?
No. The accelerator proves the pattern on a bounded scope. The practice exists for what follows: full platform builds, migrations of every source and workload, integration and master data, and the DataOps that keeps a platform reliable once it is live.
Can our current platform carry AI workloads?
Often the constraint is one layer, not the whole estate: the storage and compute that documents, retrieval and models need. We modernize that layer and carry forward the semantic models, history and access rules that still work.
How do you keep platform costs under control?
Unit economics is one of the seven tests. Budgets, per-run cost tags and alerts are built in from the start, so the cost per pipeline run and per query is known at the volume you are planning, before the bill arrives.
The people who scope it are the people who build it
Show us the estate. We’ll tell you what to keep.
Thirty minutes with the senior people who would do the work. If a rebuild isn’t the answer, we’ll say so.
