AI consulting.For real work.

Consulting helps decide what to try, tests the assumptions and defines the scope. Workflow implementation builds and connects an agreed process to your systems, then tests it and prepares the handover.

Before rollout, agree an owner for access, output checks, updates and incident handling. Your team, Astir under an agreed support scope, or another provider can take that role. Software, hosting and model usage belong in the running-cost budget.

An AI consultant helps choose a useful business problem, assess the information and systems involved, and decide whether AI is appropriate. The work can include a pilot, software integration, testing and team training. The starting point is a workflow and a measurable result, rather than a particular model or tool.

Bring one example of the work, the tools involved, who owns each step and a rough estimate of time or errors today. Use redacted or sample information for the first discussion. We can then identify data gaps, access requirements, review points and a sensible trial scope.

Start with one repetitive job and a clear way to judge the result. We look at the information it needs, the mistakes that would matter and who checks the output. A small trial helps you decide whether to go further.

Not always. Connecting the tools you already use may solve the problem. We compare that with changing software before recommending a build.

Automation follows agreed steps or rules. AI can help with less structured work, such as reading a document or drafting a reply, but its output needs checking. A useful solution may combine both.

An assistant can be designed to use approved tools, but its permissions need clear limits. Start with draft or read-only steps, add human approval for important changes, and keep a record of what happened.

The scope depends on the job, information quality, integrations and level of testing. A useful starting brief identifies one workflow, its current cost and the result you want. Ongoing model, software and support costs also need to be considered.

Use rules when the inputs and decisions are predictable. A simpler form, a system connection or clear ownership may remove the problem without AI. Use AI where unstructured material needs interpretation, with checks for accuracy and a fallback when the result is uncertain.

The timeline depends on access to information, the integrations and the review needed. Before starting, agree the workflow, test examples, acceptance criteria and the decision the pilot must support. A prototype is not the same as a production rollout; deployment, training and ongoing operation need their own scope.

We agree what information a trial can use and who can access it before work begins. Where possible, we start with sample records. Important decisions need a person to check the result.