Benchmark an AI application before you put it into production
Pre-Deployment AI Assurance is structured model testing, performance comparison between models, and evaluation results stored in a database rather than in a spreadsheet someone later edits. It exists so a model gets chosen on evidence, and so the decision can still be explained months afterwards.
How an evaluation runs
Five steps between a model you are considering and a decision you can defend.
- Define
Write down what good means
The tasks the application has to do, and the standard each one has to meet. Nothing can be benchmarked until somebody states what passing looks like.
- Test
Structured model testing
The same suite, run the same way, against each candidate. Not a demo, not a handful of prompts typed by whoever was free that afternoon.
- Compare
Performance comparison
Results placed side by side so the difference between two models is a measured one rather than a preference argued in a meeting.
- Lock
Evidence storage in a database
Runs and results are stored where they cannot be quietly rewritten. The record of why a model was chosen outlives the people who chose it.
- Decide
Ship, change or stop
Evidence-backed model selection, or a decision not to deploy. Both are useful outcomes, and the second one is cheaper before launch than after.
What the solution provides
Four things, all of them stated plainly because the product is a narrow one.
Structured model testing
A defined suite rather than ad hoc prompting, so a result from one quarter and a result from the next mean the same thing.
Performance comparison
Objective comparison between multiple LLMs and between AI systems, on identical tasks, scored the same way for each.
Database-locked evidence storage
Evaluation results are held in a database, not in a document. The evidence behind a launch decision stays available and stays unedited.
Validation before launch, not after
The point of the exercise is to reduce deployment risk while changing course is still cheap and nobody outside has seen the system.
The question you have to answer, and the evidence that answers it
Every item on the left is a question somebody will ask before launch, or shortly after it goes wrong.
- Which model should we shipObjective comparison across multiple LLMs
- Is the new version better than the one we runTwo runs on one suite, scored identically
- Will it hold up on our own dataStructured tests built from your own cases
- Why did we choose this modelA stored, locked record of the run
- Should we deploy at allA measured result rather than a strong opinion
The decision point sits in the first week
The audit that decides whether you need this product at all comes before the build, not after it.
Three depths of evaluation
What gets tested widens depending on what the decision in front of you actually is.
- One model
A single candidate against a fixed suite
Useful when the model is already chosen and the open question is whether it clears the bar you set for production.
- Several models
Objective comparison between LLMs
The same tasks put to each candidate, so the choice rests on measured differences rather than on which vendor demonstrated most recently.
- The whole system
The application, not only the model
Comparison between AI systems end to end, because retrieval, prompts and orchestration usually move the result more than swapping the model does.
How the engagement is shaped
The same three phases as every Woodfrog engagement, applied to evaluation work.
- Week 1
Fixed-fee audit
Two calls and a one-pager you keep either way. What the application has to do, what evidence you need before launch, and whether this product is the right instrument.
- Weeks 2-6
Build the evaluation
The suite, the comparison, and the evidence store, built against your own cases by a pod of three rather than handed to a single contractor.
- Week 7 onward
Operate
The suite is re-run when the model, the prompts or the data change, so a later version is measured against the version you approved.
Where this sits
Pre-deployment assurance is one instrument inside a wider evaluation practice.
AI evaluation
The service this product belongs to, covering how AI systems are measured, compared and kept honest over time.
Read the service page →After launchThe sequelPost-deployment governance
Assurance ends the day you ship. Control and policy enforcement on a running system is a separate piece of work, and we treat it as one.
How we handle it →Start hereWeek 1The audit checklist
What the fixed-fee audit covers and what the one-pager contains, so you can see the first week before you buy it.
See the checklist →EvidenceDeliveredCase studies
Work that has shipped, described in terms of what changed rather than in terms of the technology used.
Read the case studies →Where this is the wrong answer
Four situations where the honest recommendation is that you do not need this product.
You have nothing to test against
Structured testing needs cases with a stated right answer. If nobody has written down what good output looks like, that is the work to do first, and it is not this product.
The problem is a system already in production
This is pre-deployment assurance. If your system is live and the worry is drift, misuse or policy breaches, what you need is post-deployment control and enforcement instead.
You already have an evaluation harness you trust
If your team runs its own suite, stores results properly and the business believes the numbers, buying a second one adds process without adding certainty.
You want a certification
This produces evidence, not accreditation. If your blocker is a compliance certificate rather than a launch decision, an evaluation report will not clear it.
Who is behind it
The firm, in its own published numbers.
Questions buyers put to us
The four that come up in almost every first call.
What do we actually receive at the end?
A structured evaluation suite built from your own cases, a performance comparison across the models or systems in scope, and the results stored in a database rather than in a document. The point of the database is that the record behind a launch decision cannot be quietly edited later, which is what makes it usable when someone asks the question a year on.
Can you compare models from different vendors?
Yes. Objective comparison between multiple LLMs and between whole AI systems is the main use. Woodfrog is an Anthropic Build Partner and was in the Claude Partner Network at launch, and that does not mean an evaluation is written to make one vendor win. If a different model performs better on your tasks, the evidence will say so and so will we.
Does a good score here mean the system is safe to deploy?
It means the system met the standard you set, on the cases you supplied, at the time it was run. It is not a guarantee about inputs nobody thought of. That is why the suite is re-run when the model, the prompts or the data change, and why post-deployment control is a separate piece of work rather than something assurance covers.
How do we start without committing to a build?
The Week 1 audit: two calls, a fixed fee, and a one-pager you keep whatever you decide next. If the audit concludes that your existing testing is adequate, or that your real problem sits after deployment rather than before it, that is what the one-pager will say.
Find out whether you need this before you buy it
Start with the Week 1 audit: two calls, a fixed fee, and a one-pager you keep either way. It covers what your application has to do, what evidence you need before launch, and whether this is the right instrument. If not, we say so.