LLM Reliability Review
Find out why your LLM feature can't be trusted, and what to fix first.
A fixed-scope LLM evaluation of a pipeline that's in production or about to be. In one to two weeks you get a failure analysis, a small eval set you keep, and a written report with fixes in priority order.
This is for you if
- Your LLM feature sometimes makes things up, and nobody can say how often.
- Quality is judged by reading a few outputs, not by an eval you can rerun.
- A RAG or agent pipeline works in demos but breaks on real inputs.
- You're about to put an LLM feature in front of customers and want a second opinion first.
What you get
- 01
Failure analysis
Where the pipeline fails and why: retrieval, grounding, prompts, model choice or the code around them.
- 02
An eval set you keep
A small labeled set of real cases, with scoring you can rerun after every change.
- 03
Prioritized report
A written report with fixes ordered by impact and effort, and a call to walk through it.
How it works
01
Intro call
Twenty minutes to understand the system and confirm a review is the right fit.
02
Access and samples
You share read access to the pipeline or its logs and a sample of real inputs and outputs.
03
Evaluate
I build the eval set, run the pipeline against it and trace each failure to its cause.
04
Report
You get the report and the eval set, and we go through the fixes together.
Pricing
From
US$3,000
Timeline · 1–2 weeks
Fixed price, agreed in writing before work starts.
If the review shows something needs rebuilding, you decide whether to do it with me or in-house.
After launch: ongoing support (evals, monitoring, fixes) from US$2,500/month.
In production
2 days → 2 hoursAn LLM documentation platform: 2 days → 2 hours per documentIn-house engineer · Fortune 500 manufacturerRead the case study →Questions
What do you need from us?
Read access to the pipeline or its logs, a sample of real inputs and outputs, and someone who can answer questions about how it's supposed to behave.
Do you change our code during the review?
No. The review is read-only. Fixes come as a prioritized list; implementing them can be a separate engagement.
Is this only for RAG?
No. It covers RAG, agents, document generation and classification: any pipeline where an LLM's output reaches people or other systems.
What happens after the review?
You keep the eval set and the report. If you want help with the fixes, that can be a Production AI Build or ongoing support from US$2,500/month.
Accepting new projects
Have an LLM feature nobody fully trusts yet?
Twenty minutes is enough to tell whether a Reliability Review or a build makes sense.