AI, in plain English.

The pattern that works: give AI your real sources, show where each answer came from, and keep a person in the loop where mistakes are costly.

Your documents are indexed by meaning, not just keywords. When someone asks a question, the system finds the most relevant passages and gives only those to the AI, which writes an answer and links to where each part came from.

Those links are what make people trust it. Anyone can check an answer in one click, and outdated documents become obvious.

The AI turns the question into a database query. The safe version can only read data, uses agreed definitions (so "revenue" always means the same thing), and shows its work so an analyst can check it.

Each field gets a confidence score and is checked against simple rules: totals add up, the vendor exists, dates make sense. Anything uncertain goes to a person with the source highlighted. People stop typing and start reviewing exceptions.

• Nobody owns it, so nobody pushes it over the line
• The data wasn't ready and no time was planned to fix it
• No way to measure "good," so nobody knows if it's improving
• Built without the people who'll use it
• A tool was bought but never connected to real work

Our process is built around these: the first week names an owner and a goal, your team tries a working version by week three, and we stick around after launch.

• It happens often, daily or weekly
• It involves a lot of reading or writing
• A mostly-right answer is still useful, and a person can check it
• It's internal, so rough edges don't reach customers
• You can measure it: hours saved, tickets closed, time to ramp

A company assistant ticks almost every box, which is why most clients start there. Not sure? Try the readiness check.

LLM

Large language model. The engine behind tools like Claude and ChatGPT.

Prompt

The instructions and context sent to the AI with each request.

RAG

Find the relevant passages first, then have the AI answer from them.

Hallucination

When AI states something confidently that isn't true. Sources and checks reduce it.

Eval

A set of test questions with known answers, used to measure quality.

Agent

AI that can take actions with tools, like updating a record, not just write text.

Fine-tuning

Training a model further on your examples. Most business projects don't need it.