Before you train,
know why.
A clear next step for your AI.
Turn your team’s real work into approved tests. Find what’s holding your system back—and whether fine-tuning is worth testing.
Expert-led. Evidence first.
Structured document review
72/ 100
Approved cases passed84/ 100
Same evaluation casesFix context first
Training pilot deferredWhere the baseline falls short
Share of categorized failures
Better context.
Before more training.
Test the workflow fix on fresh cases before committing to a training pilot.
01 / THE ASSESSMENT
Start with the work.
Follow the evidence.
A focused assessment of one valuable task. Your expert defines quality; measured experiments guide the recommendation.
Define the task
One repeated workflow. Clear requirements. A shared definition of a good result.
Approve the tests
Turn real examples into an evaluation your domain expert reviews and trusts.
Measure today
Establish a baseline. Look at the failures, the variability, and the operational cost.
Find the next step
Compare simpler fixes. Recommend a training pilot only when the evidence supports it.
Training is a possible next step. A better prompt, better context, or a decision to stop can be the right result.
02 / DECISION PATHS
The right fix.
For the actual failure.
Different problems need different interventions. We test the explanation before recommending the investment.
Training should earn
its place in the plan.
The system is missing the facts.
Fix retrieval & context
Check whether the right information reaches the model before changing its weights.
It knows the task, but is inconsistent.
Test prompts, then SFT
Compare clear instructions and examples before testing supervised fine-tuning.
The outcome can be verified.
Consider an RL pilot
Only with a trusted grader, suitable data, and a bounded experiment worth running.
The evidence doesn’t justify training.
Stop. Or gather more evidence.
A strong baseline, weak economics, or insufficient data is a reason to reconsider.
03 / WHAT YOU TAKE AWAY
More than a score.
A way forward.
An evaluation your team owns
Approved cases, explicit criteria, and a clear record of what was measured.
A diagnosis with real examples
Baseline results, important failure patterns, and the alternatives we tested.
A decision with its assumptions
A recommended next step, cost ranges, limitations, and a pilot plan where justified.
A GOOD FIT
One task.
Real examples.
Someone who knows good.
For teams already using an LLM in a repeated workflow, with an expert reviewer and a meaningful quality or cost problem.
Before we begin
Do we need to fine-tune a model?
No. The assessment can recommend a prompt, retrieval, model, or workflow change—or conclude that training is not justified. A training pilot is a separately scoped next step.
What should we bring?
One repeated task, representative inputs and completed examples, your current system details, and a domain expert who can judge a good result. We agree on the evidence needed during scoping.
Can you guarantee an improvement?
No. We compare measured results on approved evaluations and disclose uncertainty. A pilot can show positive, neutral, or negative results; an inconclusive result is reported as such.
Is this a self-serve training platform?
This is an expert-led assessment. We work through the task and evidence with your team. Training, provider selection, and hosting are considered only when the engagement calls for them.
How long does it take, and what does it cost?
Timing and pricing follow task scoping. They depend on data readiness, review effort, evaluation volume, and whether a separate pilot is included. We agree on scope and budget before experiments.
How is our data handled?
Data access, processing providers, retention, deletion, and permitted uses must be agreed before you share material. Approval for evaluation and permission to use data for training are separate decisions.
EVIDENCE BEFORE INVESTMENT
Bring one task.
Leave with a clear next step.
Start with a conversation.