AI code and output review
Independent review of AI-generated SQL, Python and R before it reaches production or a decision. Fluent enough to use it, sceptical enough to catch errors.
Analysts are shipping more code than ever, and a growing share of it is generated. Much of it is fine. Some of it joins on the wrong key, silently drops rows, or reports a number that is plausible and wrong. The cost lands later, in a decision made on it.
This service is a senior review layer for AI-assisted analytics work: code, method and output, before it reaches production or a leadership pack.
What you get
Delivered, not recommended
- Review of generated SQL, Python and R for correctness, data-handling errors and silent failure modes.
- Reproduction of key outputs from first principles where the stakes justify it.
- Practical guidelines for the team: where AI assistance is reliable, where it is not, and what review is proportionate.
- A written record of findings that the team can learn from, not just a pass or fail.
Questions
Asked before the first call
Are you against using AI for analytics work?
No. I use it daily. The point is that generated code needs the same review as any other code, and the failure modes are different enough that experience with them matters.
What do you look for in a review?
Joins that multiply or drop rows, filters that quietly exclude edge cases, date handling across time zones and financial years, and statistics applied in ways that do not match the question being asked. Then the output is checked against the question it was meant to answer.
How quickly can a review be turned around?
A focused review of a single model or report is usually a matter of days. Standing review inside a retained arrangement runs on whatever cadence the team ships at.