Billow is a data and AI services company. From data engineering and analytics to generative AI and production AI agents, we build enterprise AI solutions grounded on your own data — the ones that make it past the proof of concept and into real work.
Our data analytics and AI services cover the whole path — reliable data first, then the analytics, models and agents that turn it into decisions and action.
Agents connected to your real systems — MCP server development, tool integration, orchestration, evaluation and observability, with guardrails to run them safely.
RAG assistants, copilots and document intelligence grounded on your own data — with the guardrails and evaluation that keep enterprise AI solutions trustworthy.
Reliable pipelines, lakehouses and warehouses on Databricks, Snowflake, Azure and AWS — the foundation everything else depends on.
Semantic models and dashboards in Power BI and Tableau that produce numbers leadership trusts — and actually use.
Forecasting, classification and anomaly detection built, deployed and monitored with MLOps discipline — models that keep working after launch.
Plenty of AI solutions companies can build a demo. The hard part is the data underneath it, the governance around it, and keeping it working once it's live. As your data and AI consulting partner, that's exactly what we focus on.
Great AI needs great data. We fix the pipelines and foundations before the model — because that's where most AI projects quietly fail.
Evaluation, monitoring and MLOps built in, so what you launch keeps performing instead of drifting after the demo.
Grounded on your own data with access controls, guardrails and auditability — the discipline enterprise AI solutions demand.
We'll tell you when a use case doesn't need AI. Advice you can trust is what a real data and AI consulting partner owes you.
Value compounds when the layers build on each other. We deliver the whole stack, or slot into wherever you are.
Engineering · pipelines
Analytics · BI · ML
GenAI · agents · MCP
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We assess your data, use cases and readiness — and identify the AI opportunities actually worth pursuing.
Fix the data underneath — pipelines, quality and governance — so what's built on top is trustworthy.
Develop the model, assistant or agent, and test it rigorously against real cases before it goes near production.
Ship with observability and MLOps in place, then monitor and improve so it keeps performing over time.
Tell us the decision you wish you could make faster, or the AI idea you're weighing — we'll tell you honestly what it takes and where to start.
