The messiest part of your business, made measurable.
Eight years. Twelve projects. Nine industries. Teams of up to thirty four engineers.
AI that ships and pays off. Plus the unglamorous foundation it depends on: data you can trust, governance that holds, systems that survive production.
Every engagement ends with a capability, not a dependency. Engineers who run the system. Documentation that survives my exit.
Independent by design. No vendor relationships. No resale commissions. No platform to sell.
Nine industries. One pattern.
AI and data programs fail the same way everywhere. The constraints change. The pattern does not.
You are not a test case.
Every outcome above was delivered on platforms already trusted in production by enterprises worldwide. No experiments on your budget.
- Microsoft Azure
- Amazon Web Services
- Google Cloud Platform
- Databricks
- Snowflake
- Azure Synapse
- BigQuery
- Redshift
- Apache Spark · PySpark
- Apache Kafka
- PostgreSQL
- dbt
- LangGraph · LangSmith
- AWS Bedrock · AgentCore
- Azure AI Foundry
- pydantic-ai
- RAG Architectures
- Agentic AI Systems
- AI Voice Agents
- GPT, Claude, Gemini
- ElevenLabs
- Power BI
- Apache Superset
- Looker
Know where you stand before the next AI dollar is spent.
Whether you need an independent readiness audit, an AI agent taken to production, or a senior leader to build the capability from within, I would like to hear about the problem first.
Not ready to talk? Take the free AI Readiness Calculator →