AI Enablement

AI features you can test, ship, and improve.

For CTOs and product leaders, the challenge is getting AI to do useful work with real users and real constraints. We help you choose the task, build the feature, and establish the evidence for releasing it.

Talk to our team

See the AI quality case

SimpleDocs AI contract review, built with Telos. Product view uses demo data.

Start with the work

Define what a useful answer has to do

An impressive demo is a starting point. We work with you to identify one valuable task, the information it needs, and what happens when the model gets it wrong. That gives the prototype a concrete standard to meet.

The user task

Review an agreement, extract information, or answer a question using the right context. Define where the feature fits in the product and what the user does with its output.

The acceptance criteria

Choose representative inputs and expected behavior, including ambiguous and failure cases. Evaluate changes against the same tasks so comparisons have meaning.

The operating boundaries

Agree on data access, permitted model and provider use, and which actions need human approval. Specify failure behavior and an escalation path before rollout.

For example: extract a renewal date

A document feature should find the right date, show supporting text, and flag missing or conflicting information for review. If it updates a record, check the saved value and permissions too. A plausible answer alone doesn't establish that the task succeeded.

This is an illustrative test, not a client result. Anthropic's engineering guidance distinguishes an agent's reported success from the actual outcome and recommends repeatable evaluations with explicit success criteria. Read the evaluation guidance.

Evidence from SimpleDocs

Make model changes reviewable

For SimpleDocs, we helped build prompt versioning, structured evaluations, model comparisons, and publication checks. The team can inspect a candidate against explicit product tasks before deciding how to use it. Provider configuration and adapters support changes to the underlying model infrastructure.

Read how the quality controls work

What you receive

A working feature and evidence for the next decision

We agree on the task, scope, and release criteria with you. The deliverables connect the feature to its evaluation and rollout plan, so your team can assess later changes too.

An AI feature integrated into the agreed product workflow, with data access and human approval boundaries defined.
Representative evaluation cases, expected behavior, and versioned prompts. Repeated trials help expose inconsistent results; established cases become regression checks.
A comparison of quality, response time, and operating cost for the candidate models on the same task, with an agreed usage budget.
Known limitations and failure cases, including what the user sees when the feature cannot complete the task reliably.
A rollout and monitoring plan with release criteria, fallback behavior, and an owner for reviewing failures after launch.