Use-case assessment
Define the task, baseline, success criteria and points where a person must review the result.
Services
Put AI to work on a problem you can measure.
Start with a task your team already does: finding information, reading documents or handling repetitive requests. We help define a useful pilot, test it against real examples and decide what belongs in production.
Discuss your project ↗
Is this the right service for your team?
For teams with a specific workflow and access to the people and information needed to evaluate it. AI is useful when its output can be checked and the cost of an error is understood.
Scope and deliverables to agree together
Knowledge and retrieval
Prepare document search and retrieval-augmented generation where appropriate. Plan source references and access boundaries.
Workflow integration
Connect the assistant or automation to approved systems, with clear permissions and limits on the actions it can take.
Evaluation and operation
Test representative questions and failure cases. Plan logging, cost monitoring and a process for reviewing changes.
The proposal defines the scope and acceptance criteria for your project before work begins.
From the first conversation to delivery
Choose the task
Identify a frequent problem and how the team handles it today.
Check the data
Review quality, access rights and the examples needed for testing.
Evaluate a pilot
Test useful answers, failure cases, latency and cost.
Decide the next step
Expand, revise or stop based on the agreed evidence.
What affects cost and timing?
Data preparation, document volume, integrations, permissions and evaluation effort all affect scope. Model usage and infrastructure are operating costs to plan alongside implementation.
What should you prepare?
Bring the target workflow, sanitized examples, available data sources, current processing time and any security restrictions. Avoid sending confidential documents before agreeing how they will be handled.
Questions before you start
Do we need to train our own model?
Not always. Existing models, retrieval and workflow design may be sufficient. We choose an approach after checking the task and data.
Can you guarantee that AI will never give a wrong answer?
No. We design evaluation, source references, access controls and human review around the consequences of an incorrect output.
Can we start with a pilot?
Yes. A bounded pilot with explicit acceptance criteria helps the team decide whether to invest in a broader rollout.