Most AI projects fail at the part nobody demos.

Two jobs. The first is deciding which AI tools and platforms are worth their cost for your specific workload. The second is building the workflows and agents where automation pays back. A large part of the value is saying which ideas should not be built.

Buy where a tool already does the job, since the AI features bundled into software you pay for are usually the cheapest route. Build where the workflow is specific to how your business operates and no vendor covers it. The build case gets stronger the more the process is yours alone.

That depends on the provider and the tier, and it is worth answering before deployment rather than after. Enterprise agreements with major model providers commonly exclude your data from training, but regional storage guarantees vary considerably. We check the terms rather than assume them.

No. No reseller agreements and no referral fees, on AI tooling or anything else. If the honest recommendation is a free tier or a feature you already own, that is what you will hear.

We build, integrate, govern and hand over, documented and owned by your team. Ongoing support is available, but the goal is that it runs without us in the loop.

Scoping and decision work is normally a fixed fee against a defined question. Build work is quoted against the workflow once it is specified, because a scope written before anyone has mapped the process is a number invented to fill a proposal. What we will not do is quote a build before establishing whether the build should happen.

A decision engagement is usually two to four weeks. A first production workflow is commonly six to twelve, and the variable is rarely the model. It is integration surface, error handling, and how long it takes to agree what the system should do when it is unsure.

For most of what companies want from it, no. Document workflows, drafting, extraction and internal process automation run on the systems you already have. A data team becomes necessary when the goal is prediction on your own historical records, which is a genuinely different project and a much higher bar.

The cheapest answer is usually the AI features already bundled into software you pay for, and they are worth exhausting before adding a line item. Beyond that it depends entirely on the workload, and the honest test is whether a tool changes how often a specific task gets done rather than whether it demonstrates well. We hold no reseller agreements on any of it.

Automation executes a process you have already defined. AI handles the parts that need judgement, language or unstructured input, which is why it is reached for when the process resists definition. In practice most useful projects here are mostly automation with a model doing one specific step, and describing them as AI projects oversells the interesting part.