AI is on the roadmap. The data layer isn't ready for it.
Inteledyne designs and builds the data platforms AI runs on. A principal architect leads every engagement, and delivery engineers build it with your team, on AWS, Snowflake and the major clouds.
Selected client experience- University of Wisconsin
- Salesforce
- Taco Bell
- Viasat
- Hashmap
- Wiley
- Blue Shield of California
- Bank of America
lower platform cost after cost-optimization and governance policy, with better performance
to onboard a new business unit, down from weeks, with metadata-driven self-serve
lower infrastructure cost on a Hadoop to AWS lakehouse migration, with 5x query performance
tables in an EMR/Iceberg student data warehouse, refreshed every 6 hours
It usually starts with a problem you can already name.
Five situations bring most clients to us. Each one maps to an engagement that fits it. See all fifteen
Most architecture advice never gets built.
We run engagements so the design gets built, and so your team can run it after we leave.
Principal-led architecture
One named principal owns the design from the first assessment to the handover, and writes down every decision.
Engineers who build it with you
Delivery engineers work in your repositories and ceremonies. Build capacity scales through our development partner, Reenbit.
AI built on governed data
Agents and retrieval go into production on a data layer that is clean, current and access-controlled.
Five practices, from the cloud foundation to the agents on top.
Each practice is led by a principal and staffed to the engagement. Most platforms need two or three of them at once.
A large commitment should start with evidence.
A first conversation, free
Bring the problem. If we are the wrong firm for it, we say so and name someone better placed.
A written answer
A Health Assessment or an Architecture Review: fixed scope, delivered as a document your board can read.
Renew, close or hand off
Embedded leadership and delivery teams are reviewed every quarter. You decide whether the work continues.
Most AI pilots stall at the data layer.
A retrieval demo needs clean, current, governed data before it becomes a production system. We build both halves: the agents, and the platform they read from.
Natural-language query
Natural-language query designed into the enterprise data platform, alongside a governed Iceberg lakehouse.
University of WisconsinAuto-relationalization
Workday and PeopleSoft data become governed tables with no manual schema mapping.
University of WisconsinML and data-engineering workflows
ML and data-engineering workflows supporting downstream machine-learning models.
Wiley · IntricityApplied AI on Bedrock
Agentic and LLM systems on Amazon Bedrock are part of the practice stack, used where the engagement calls for them.
Core stackData lakes, AI agents and migrations, already in production.
A governed Iceberg lakehouse for a major research university, with auto-relationalization of Workday and PeopleSoft data and a 4,000-table student warehouse.
Company-wide dbt and Snowflake standards on a Marketing data platform later adopted by Revenue, Sales, and Product.
On-prem Hadoop to an AWS lakehouse for a Fortune 500 financial-services client, with real-time pipelines at a million events a day.
You know who is accountable before the work starts.
Robin Tanner Industries where the data is regulated, high-volume or hard to change.
- Higher educationUniversity of Wisconsin
- Enterprise softwareSalesforce
- Retail & restaurantsTaco Bell, Smart & Final
- Publishing & mediaWiley Publishing, Integral Ad Science
- HealthcareBlue Shield of California, Precision Digital Health, Healthcare Partners of Nevada
- Financial servicesHashmap (Fortune 500 financial-services client), Bank of America, Penn National Gaming
Running a lakehouse costs more than it should.
- How S3, Lambda, EMR and Iceberg assemble into a production lake
- The four things you have to get right
- Where Bedrock and agents plug in on top
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Lake AI, the product that grew out of the practice. It packages the patterns from our engagements: data foundations that absorb source-system change instead of breaking on it.
Visit Lake AI ↗Bring us the problem you can already name.
The first conversation is free. If we are the right firm for it, we propose a scope in writing. If we are not, we tell you who to call.