AI on Your Data

How We Put AI on Your Data

Here is how most AI projects start: someone reads an article, pitches it to the CEO, a vendor gets hired, and six months later there is a proof-of-concept that works in a demo but nobody uses in production. The vendor moves on. The POC sits in a repo. The company concludes “AI does not work for us.”

AI worked fine. The foundation did not.

The three failure modes we see repeatedly

First: starting with the technology instead of the decision. “We should use LLMs” is not a strategy. “We need to cut survey analysis from 3 weeks to 3 hours” is. The technology follows the problem.

Second: pointing AI at an ungoverned stack. An LLM is only as trustworthy as the data underneath it — ask it a revenue question over five conflicting definitions of revenue and it will confidently pick one. That is why the semantic layer comes first: metrics defined once, in code, so the AI’s answer is the same answer your CFO signs. We have walked away from AI pitches and recommended data cleanup instead. It is not what the client wanted to hear, but it saved them six figures.

Third: building without guardrails. An LLM that hallucinates in a demo is a curiosity. An LLM that hallucinates in a patient-facing system is a liability. Every AI deployment needs explicit boundaries for what it can and cannot do, how errors are caught, and who is accountable.

What an engagement looks like

If you already have a governed foundation, we go straight to wiring the semantic layer and the AI on top of it. If you are not sure where AI fits, we start with AI Opportunity Mapping: a structured 2-3 week assessment that evaluates your operations, data readiness, and team capabilities against a library of proven use cases, ranked by impact, feasibility, and data readiness.

Then we build. Not a slide deck about what AI could do. A working system on your data.

We have delivered this pattern end to end — semantic layers and LLMs on client data — at both a 100+ location restaurant franchise and a global SaaS platform. For a healthcare provider, it meant a pipeline from SurveyMonkey responses through AWS Lambda and ChatGPT API into Snowflake — turning patient surveys into structured insights in minutes instead of weeks. For a cloud security platform, we delivered an FY27 pricing model in 9 days using Claude Code across 28 focused sessions — because the right architecture (structured seed tables, temporal lookups, parallel validation) eliminated the manual work that usually stretches pricing projects to quarters.

What we leave behind

Beyond the working system: an AI guardrails starter document tailored to your industry and risk profile. A data readiness scorecard showing which additional use cases your current data can support and which need investment first. A handoff plan so your team can operate, monitor, and iterate without us.

When AI is not the answer

If a SQL query and a well-designed dashboard solve the problem, AI adds complexity without value. If your data is not clean or centralized, AI will amplify the inconsistencies — start with Unified Data Foundations, or with the Data Stack Diagnostic if you want the gap mapped first. Roughly 30% of the use cases clients bring to us are better solved with conventional analytics, and we say so.

Questions worth asking before you invest

Can you describe the specific decision this AI system would improve — and how you measure that improvement today without AI? Do you have at least 6 months of clean, governed data for the process you want to automate? If the AI system makes a mistake, what is the cost — and who catches it?