Test design
- — Representative tasks drawn from your environment, not a generic sample
- — Knowledge retrieval, catalog and record lookup, and end-to-end workflows
- — Your real content, so results reflect your data quality
Evaluation · Evidence from your environment
Don’t take our word for it.Run it on your data.
Vendor accuracy numbers are measured on vendor data, which is why they rarely survive contact with a real knowledge base. We hand you the evaluation approach and run it against your content, so the result you act on is your own.
YOUR CONTENT◆REPEATABLE RUNS◆MODELS COMPARED
WHAT YOU GET
- A test set built from your own tasks and content
- A documented run protocol you can repeat
- Side-by-side results across the models you are considering
- The suite itself, to re-run whenever anything changes
Your data
Not a vendor sample
Repeatable
Multiple runs, not one pass
Transparent
Rubric and protocol shared
Comparable
Models tested identically
Yours to keep
Re-run it whenever you like
What actually improves
Better, faster, and less of it.That is the whole point.
The gains show up in the work itself rather than in a headline figure. These are the changes customers set out to make, and what an evaluation is designed to confirm in your environment.
Faster answers
People get a direct answer in the system they are already in, instead of searching across articles, records, and tabs to assemble one.
Less repetitive work
Routine gathering, summarizing, and form-filling is handled so your team spends its attention on judgment and exceptions.
More consistent output
The same request produces the same shape of answer every time, because workflows are versioned and structured rather than improvised per user.
Better use of what you already own
Existing knowledge bases, records, and documents become genuinely usable, without a migration or a re-platforming project.
L2H does not publish accuracy or time-savings figures for your environment, because they depend on your content quality, process maturity, and the models you approve. We measure them with you instead.
How we evaluate
Reproducible by design.Rerunnable on your data.
The test set, run protocol, and scoring approach are explicit so nothing rests on a number you cannot check. If a result matters to your decision, you should be able to reproduce it.
Run protocol
- — Multiple runs per configuration to expose variance, not a single lucky pass
- — Retrieval settings tested both enabled and disabled
- — Identical prompts and retrieval logic across every model compared
Scoring
- — A structured rubric with a pass or fail recorded per task
- — Results averaged across runs rather than cherry-picked
- — The full test set handed over so you can re-run it yourself
Model choice, decided with evidence
Pick the model that earns it.
Because model choice is configuration on this platform, the useful question is not which model wins in general — it is which one is good enough for your tasks at a cost and hosting posture you accept. An evaluation answers that with your content.
- Compare frontier and open-source models on identical tasks
- See where a cheaper or self-hosted model is genuinely good enough
- Understand how much each model depends on retrieval quality
- Re-run the suite whenever you change models or content
From claim to evidence
Bring your knowledge base.
We will scope a test set from your own tasks, run it across the models you are considering, and hand you the results and the suite.