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What Data Platforms teams need most
WHAT WE BUILD
Data platforms & applied AI
Pipeline Orchestration
Dependencies scheduled and retried, with failures surfaced before consumers notice.
Schema & Data Contracts
Fields and owners agreed in writing, with changes reviewed before release.
Warehouse Modeling
One modeled layer that reports and tools read from.
Quality Monitoring
Tests on volume, nulls and ranges, with freshness visible per table.
Access & Lineage
Who can read what, and where each column came from.
Self-Serve Data Access
Documented tables a business team can query without a request.
Where Data Platforms teams lose time
A data platform serves many teams at once. Analysts query the warehouse, a product team reads events, and a finance report depends on a table someone else owns. Most incidents start with a change nobody announced, and the fix begins with finding out who made it.
We build the pipeline and the agreement around it. Sources land in a modeled layer with named owners and contracts, quality checks run at each step, and consumers see freshness, so a broken table is caught before it reaches a decision.
01
Ownership left unclear
A table has three contributors and no owner, so a schema change is discovered when a downstream report goes blank.
02
Two definitions of revenue
Finance and sales each model the same metric, and meetings are spent deciding whose number is right instead of what to do about it.
03
Freshness taken on trust
A dashboard shows yesterday's figures without saying so, and a decision is made on data that never loaded or loaded only halfway.
HOW THE BUILD RUNS
OUR PROCESS
Step 01
Walk the workflow
We sit with the people who do the work and follow one job from first contact to final invoice. The gaps show up quickly once nobody is describing them from memory.
Step 02
Model the data
Bookings, orders, patients or jobs become one set of records with clear owners. We agree on the fields before any screen gets built.
Step 03
Build in stages
The costliest step ships first, usually the one where revenue or time leaks. Each stage goes live while the next one is being built.
Step 04
Hand over and support
Your team gets training, written runbooks and a support window. We stay close through the first month of real use, then step back.
SYSTEMS WE CONNECT TO
INTEGRATIONS
We build around what you already run. These are the systems we most often read from, write to, or replace one part of, without asking your team to start over.
Data warehouses and lakes
Identity and access systems
Help desk and CRM tools
Document storage
Monitoring and evaluation tools
THE TARGET
6-8 wk
Pilot to Production
Typical timeline for a scoped, evaluated AI pilot.
THE EXPERIENCE
100%
Evaluation Coverage
Every AI output path tested before release.
THE COVER
24/7
Guardrails
Monitoring, fallbacks, and cost controls running continuously.
THE HANDOVER
1
Source of Truth
A single governed warehouse behind reporting and AI.
OTHER SPECIALTIES
AI & Data Intelligence
QUESTIONS & ANSWERS
Questions about Data Platforms
The questions that come up before a ai & data intelligence engagement starts.
Where do most data platforms fail?
At ownership. Someone adds a column, a downstream job breaks, and nobody knows who to call. Data contracts with named owners fix more of this than another tool.
Can you work with streaming and batch?
Yes. We keep one modeled layer on top so consumers ask the same question either way, instead of reconciling two truths.
How is quality enforced?
With tests at ingestion and transformation, plus a visible freshness state. Bad data blocks the report rather than filling it with plausible wrong numbers.
Do we need to replace our warehouse?
Not usually. We work with what you run and add the modeling, contracts and checks around it. Replacement is worth discussing only when the current platform blocks the work you need to do.
How do you migrate without breaking reports?
We run the new model beside the old one and compare outputs until they agree, then move consumers one at a time. The old path stays available until the last report is switched.
Who writes the data contracts?
The team that owns the source writes it, with the consumers in the room. We set the format and the review step, because a contract imposed by the platform team alone tends to be ignored.
HOW WE HELP
The wider team behind this work
Most specialty builds draw on more than one service line. One team, one scope, no handoffs between vendors.
Talk to us about data platforms.
Tell us what your team runs today and where it breaks. A real person replies, usually the same day.
Start a conversation
Start a conversation
contact us
Let's Talk About Your Project
Scope the problem and its constraints
Review your current product and stack
Agree on timeline, team, and budget
Cover security and compliance needs
Plan integrations with your tools
Leave with a clear first milestone
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