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Find the right starting point.

Choose by the decision you need to make: improve an answer, test an action, compare models or prepare better task data.

Seven capabilities. A shared evidence standard.

01

RAG Evaluation

A convincing answer can still cite the wrong policy. We separate retrieval problems from answer problems, so your team knows whether to change the index, the prompt or the response rules.

Start with: A versioned question set with reference passages and expected behaviour

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02

Agent Evaluation

An agent can say a refund is complete even when nothing changed—or retry until it creates two refunds. We test the outcome, the tool calls and the steps between them.

Start with: Task scenarios with explicit starting states and expected outcomes

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03

Model Comparison

The best model for a long policy document may be the wrong choice for a fast support response. We compare candidates against your tasks and constraints, then show where each one fails.

Start with: A comparison matrix with task-level results and known trade-offs

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04

Dataset Preparation

A dataset full of easy examples can make a weak system look ready. We turn raw inputs into a documented set that covers ordinary work, expensive mistakes and situations where the answer is unknown.

Start with: A versioned dataset with a data dictionary and provenance notes

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05

Model Training

Fine-tuning is useful when you need consistent task behaviour and have suitable examples. It is not a substitute for missing knowledge, a broken workflow or an unclear requirement.

Start with: A feasibility recommendation with alternatives to training

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06

Coding Evaluation

Code that compiles can still break authentication, miss a requirement or change unrelated behaviour. We evaluate coding systems against executable tasks and the constraints of the repository.

Start with: Repository task definitions with reproducible starting points

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07

Multimodal Evaluation

A single misplaced decimal or table column can matter more than a fluent document summary. We test extracted fields and evidence across the document types your workflow actually receives.

Start with: A document test set with reviewed fields and evidence locations

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Begin with one workflow.

Before launch

Define what should happen, the failure cases and the evidence needed for a release decision.

Already in production

Bring a recurring failure or a representative trace. Separate system behaviour from missing references.

Changing your system

Compare the current and proposed versions on the same task set before choosing a migration.

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Tell us what your AI needs to get right.

Share the workflow and the problem. We’ll confirm fit, then discuss scope, access, deliverables and cost before work begins.

Discuss your project