Embedded AI & Data Architecture
Listed on the current site as “Fractional AI Data Architect”
For companies building modern data lakes and agentic AI systems on AWS — and getting them right the first time.
Specialized fractional architecture for organizations whose competitive advantage will be how they harvest, structure, and reason over their data. Iceberg, Bedrock, Claude, agentic systems — built deliberately.
You are probably here because…
- You're committing to AWS as your data platform and want senior architecture from the start.
- You're building agentic AI systems that need to query enterprise data — and the stakes for getting the data layer right are high.
- You have an existing data lake that has accumulated decisions and you need someone who can see what's load-bearing and what's not.
- You're integrating Bedrock, Claude, or other LLMs into operational workflows and want a designer-in-the-room as the patterns are established.
Deliverables
- Architecture and operational design for AWS-native data lakes — Iceberg, Spark, Glue, Lake Formation, EMR, Redshift, Athena.
- Patterns for AI / agentic systems — Bedrock orchestration, retrieval architectures, schema-aware context, multi-agent designs.
- Adaptive ingestion design — schema-drift handling, automatic relationalization, source-system change isolation.
- Cost-allocation models that connect engineering posture to run-rate dollars.
- Working code examples and reference implementations when the conversation needs them.
From first call to handover.
Architecture working session
An initial deep technical conversation to map the current state and the strategic ambition. Half-day, on-site or remote.
Recurring weekly cadence
One to two days per week of focused work — architecture, design reviews, code-level guidance, agent design, vendor selection.
Production transitions
Decisions are documented. Implementations are reviewed. The team retains the architecture once the engagement concludes.
Where this has been done before.
Engagements led by Robin Tanner, Inteledyne's founder.
Designed and built a consolidated enterprise data lake on AWS EMR and Apache Iceberg, with a Python/Spark auto-relationalization engine that turns complex on-premises Workday and PeopleSoft data into queryable relational tables, plus a serverless Student Data Warehouse on Step Functions, EventBridge, and Lambda.
Implemented Bedrock AI agents for automated PII classification at 95%+ accuracy across the data lake, plus an LLM-based pipeline-failure triage system that meaningfully reduced time-to-diagnosis for the operations team.
Practice: Data Platforms · Applied AI & Agents
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.