An AI consultant who builds it too

An AI consultant helps you choose which AI pays off in your business before you invest. Concretely: mapping the high-impact areas, working out a business case per use case, giving a build-vs-buy recommendation and delivering a prioritised roadmap. At DataDream that advice comes from someone who also builds the solution afterwards, so it stays concrete rather than abstract.

It depends on scope: a second opinion on a quote is very different from a full advisory track with a roadmap. The first call and the AI Readiness Scan are free, so we first check together whether there is a worthwhile case. A tailored proposal follows. No fixed packages that push something you do not need.

Advice comes one step earlier. An AI consultant is about the choices: which use case first, what it returns, build or buy, which risks. An AI agency then builds that solution. DataDream does both, but you can take the advice on its own. Want it built afterwards? That can happen here or with another party. No lock-in.

SMEs specifically. Large consultancies target corporations with their own IT departments and months-long engagements. DataDream is built for SMEs: moving fast, short phases, plain language and a direct line to the person who also builds. No six-month enterprise project.

Both, but the advice stands on its own. You do not have to take the delivery from DataDream for the advice to be valuable. That keeps the advice vendor-neutral: there is no incentive to push you toward one tool or one build. If you do want it built, I know the technology because I use it myself.

Yes. A second opinion on a plan, quote or proposal you already have is a frequently requested track. DataDream tests whether the approach holds up, whether the price is reasonable and whether the promised results are achievable. Independent, because there is no stake in the vendor that made the proposal.

Part of the advice. For each application, an assessment of which AI Act risk category it falls under, what you must document and where human-in-the-loop is needed. DataDream works with GDPR-compliant tools, signs standard data-processing agreements and offers EU-only data storage on request.

In short, reviewable phases. First an intake and process analysis to see where AI really makes an impact. Then prioritisation: which use cases return the most for the least effort, with a business case for each. Then a roadmap with the choices laid out. You decide after each phase whether the track continues.

A recurring example: a company processes incoming requests entirely by hand, costing hours every day. The advice starts with the build-or-buy question. Often an AI agent that reads requests and drops them straight into the CRM returns the most for the least effort, because the link to existing systems is what makes the difference. The goal: the manual work per request gone, a shorter turnaround, and people freed for the work that does need attention. I work out the business case for it up front, so you know what it returns before anything is built.

Repetitive work with a clear structure pays off fastest: recurring reports, content production at volume, email and lead follow-up, and reading documents. These are exactly the spots where an AI agent or AI automation can take manual work off your plate, when it is set up well. In the advice I prioritise those spots by impact versus effort, so you start with the use case most likely to pay off, not the trendiest one.