AI consulting from the engineers who build it

AI consulting at Cloudsail covers the decisions a company faces before and during its first AI projects. Advice and implementation stay with the same people, so a recommendation can be tested before you commit to it.

• AI strategy: which processes and products to start with, ranked by measured cost, risk and value
• Use case assessment: whether a task suits a rule, a workflow, a model or an agent
• AI readiness: data access, integration, security and the people who will own the result
• Model and vendor choice: products compared on your own tasks, with no commission from any vendor
• Implementation planning: architecture, test sets, approvals and how the system is run after launch
• Coding agents for engineering teams, our flagship: rollout, harness, cost control and measurement

A strategy that never meets real data tends to overestimate what a model can do and to underestimate the integration work. We keep the strategy short and test its main assumption early: a list of candidate tasks is narrowed to one or two, and the first is built far enough to measure cost per case and quality on your past work.

The consulting phase ends with a written decision: which use case to start with, what it needs from your systems and data, how it will be measured, who owns it, and what we found that argues against it.

Before a project starts we check the conditions it depends on. The check is scoped to the use cases you are considering rather than a general maturity score.

• Data: where the inputs live, their quality and who may grant access
• Systems: APIs, permissions and how a model's output reaches the place it is needed
• People: who reviews results, who owns the system after launch and what changes in their work
• Rules: data protection, the works council where there is one and your own policies, prepared for your advisers to decide
• Baseline: what the task costs today and how often it goes wrong, so a change can be measured

Suppose a mid-sized insurer wants to use AI in claims handling. We sit with the claims team, collect a few hundred closed cases with known outcomes and list the steps where time goes. Two candidates emerge: extracting data from incoming documents, and drafting the first reply to a customer. We test both on the past cases.

Extraction reaches the accuracy the team needs on typed documents but fails on handwritten forms, so those stay with people. The drafted replies need so much editing that they save little time. The recommendation is to build extraction with a review step and to drop the replies for now, with the figures behind both decisions.

Strategy consultancies are strong on business cases and change programmes, and some companies need exactly that. AI agencies often configure or resell a particular tool. We are an engineering company: the people advising you write the code, run the tests in your environment and hand over what they built. No model or tool vendor pays us, so a recommendation is not tied to a product.

If what you need is a company-wide transformation programme or a procurement process across many vendors, we will tell you so in the first call.

The price depends on scope, and we propose one after the first call. The main drivers are the number of use cases examined, how much data access and integration the first build needs, whether workshops run remotely or on site, and how long you want our engineers involved after launch. We don't sell fixed packages, and the first conversation is free.