Check the problem, the data and the constraints
Not every automation needs a language model. We map the workflow, identify the costly or repetitive step and compare possible approaches. We also review the information available to the system, access restrictions and the consequences of an incorrect answer.
An initial assessment defines the intended users, acceptable output, evaluation examples and boundaries. Where sensitive information is involved, data handling, provider choices and retention requirements need to be agreed before implementation.
Prototype a narrow task and measure the result
A focused prototype makes it easier to see whether the system helps. That might be finding answers in approved documents, preparing a draft for review or routing information between existing tools.
We test representative inputs, difficult cases and expected failures. Evaluation considers usefulness, accuracy for the task, response time and operating cost. The prototype should make limitations visible rather than hide them behind a confident interface.
Make human control part of the product
The interface should explain what the system is doing, give people a way to inspect relevant sources where applicable and provide a sensible route when it cannot complete a task. Consequential actions can require a review step instead of running unattended.
For a production integration, we agree permissions, monitoring, fallback behaviour and an update process. Model and provider changes can affect output, so evaluation should continue after launch. A feasibility engagement may conclude that a simpler, non-AI workflow is the better option.
THE WORK, MADE TANGIBLE
What we can deliver.
- Opportunity and feasibility assessment
- Focused prototype with representative evaluation cases
- Data access and integration plan
- Human review and fallback behaviour
- Documented limitations and monitoring approach
We agree the deliverables, ownership, budget and release responsibilities in your project scope.
BEFORE WE BEGIN
A few useful answers.
Can you add AI to an existing product?
Yes. We first review the task, available data, architecture and permission model, then propose a contained integration that can be evaluated.
Can you guarantee that every output is correct?
No. The design needs to account for incorrect or incomplete output, especially where a person relies on it for an important decision. Evaluation and appropriate review are part of the scope.
Do we need a large proprietary dataset?
Not always. Some use cases work with approved documents or existing services. We assess the available information and permission to use it before choosing an approach.