We leave when your team can run what we built without us.
Most AI consultancies sell dependency. Retainers that compound. Systems only they can maintain. We do the opposite. Every engagement is scoped around a handoff from day one. Here's how.
Most teams come to us with one of these questions.
€999 · One week · About three hours of your team's time · Credited to the build
You might have dozens of ideas for AI and no clear place to start. We sit with the people who do the work, map ten or more workflows, score each one against the result you care about, and put them in the order you should build them.
If you build with us, the €999 comes off the build. If you don't, everything we made is yours to use or hand to someone else.
Sessions with the people who run each workflow, broken into real steps. How work actually moves, not how the org chart says it does. Where handoffs stall, and where hidden manual effort lives.
Every workflow scored the same way, on impact against complexity. A build, buy or skip call on each. Sequenced so the early wins pay for the rest, and every later build gets cheaper.
Previously scoped: 10-day on-site audit across 6 departments at a global OTA. 40 workflows mapped. 6–8 sequenced AI projects. Roadmap targeting 30% profitability uplift. Read the case study →
Scoped per workflow · From the audit's build order
Pick a workflow off the list. We scope it, build it, and ship it into your stack, tested on your real data, with the guardrails and the human checkpoints agreed before anything goes live.
We map the data sources and structure the data so it's AI-ready, build the custom integrations and scrapers, and set up the one source of truth every workflow reads from. Priced after the audit, because the scope depends on your existing stack.
Pick the next workflow off the roadmap. Build, test, release with your feedback, and iterate until it's ready for your clients. Each one reuses the integrations, the guardrails and the patterns already paid for.
Not a prototype. A system designed for production from day one: evals so you can tell if the output is good, guardrails for when it isn't, observability for when it breaks, documentation for the person who inherits it.
Which tools, what order and who to hire. You or your team do the building.
One to two days a week or a month, from mapping through to shipped and adopted.
Previously shipped: a voice agent that freed 30% of sales time at CitySupply. A fully-automated outbound engine driving 4× faster sales cycles at BaseClaims. An onboarding agent that cut onboarding time by 95% at Regulars.
Hands-on · For technical and non-technical people · Built on your own workflows
Every engagement above ends with a handoff to your team. This is the handoff, sold on its own.
Two formats, depending on how far your people need to go.
Best for: teams who need to start using AI in their own work now.
Book an intro call →Best for: business people picking up the tech, and engineers picking up the business.
Download the one-pager →Six weeks that take a cohort from opening a terminal to shipping agentic systems in production, with the judgment to know what's worth building in the first place.
Every module ends with something running: an agent, a workflow, a deployed system. Exercises are built on your industry and your processes, so week-one output is already relevant. By week six the cohort has closed with a real internal workflow in production.
Repetition builds instinct. After six weeks of building, your people can take a new idea, break it into steps, and build it themselves, long after the program ends.
A repeatable method to find the highest-return workflows, prioritise them, and kill the ones that won't pay. AI projects, not AI theatre.
Before the program, AI felt like an alien thing. Now, when I have an idea, I feel like I can sit down, break it into the right steps, and build it.
Olympia · Program graduate
Fixed price · Remote · Scoped to workspace size
Dust is moving from tier-based billing to usage-based. Under tier pricing, a wasteful agent and an efficient one cost you the same. Usage-based billing prices the difference.
Most of the waste sits in the plumbing. A sync agent re-copying rows a script already computed. An afternoon briefing that recomputes what the morning one already said. A sub-agent returning 14,000 tokens that its parent re-reads at every later step. Nobody built any of it badly. It grew, and nothing was watching.
One workspace, 35 users, every agent opened and costed.
Identified, sequenced, and costed, with an evidence grade on every number.
More agent work for the same budget, against the pre-audit pace.
Every agent, its real cost, its real usage, its owner, its value rating, reconciled against your billing so the baseline matches what you were actually charged. Includes a trigger-ownership census, which is the only workspace-wide view of your schedules, since Dust triggers are visible solely to whoever created them.
Opened up from their real runs. One page each: what it does, where the money actually goes step by step, and what to change, ranked by effort class — a settings toggle, an instruction paste, or a small rebuild.
Eight plays that generalise past your top ten, each with the trigger that tells your builders when it applies, plus a reusable template for workflow #11 onwards. This is the durable part.
Scripts you run monthly: spend reconciliation, spike attribution, trigger census, per-tool cost and error drift, zero-usage sweep, and two automated watches that surface new optimisation candidates before they become line items.
Bounded queues with an "already tried" marker. Named owners. Return contracts on sub-agents. The paid-twice check: does a platform you already own do this natively. No automation running on personal credentials. And a validation protocol for every model or instruction change.
Vlad dove deep into how we were using Dust—identifying what we were doing wrong, what could be improved, and opportunities we hadn’t considered.
Felix Keser · Co-CEO, Nexio Projects
What that engagement delivered: up to 95% cost reduction on the priority AI workflows, credit spend on a major sync taken to zero from 1,210 per run, and a 40% cut in token usage across agent instructions.
Nexio's wider rollout, 88% daily AI usage in four months, is now a published Dust customer story. Read it on dust.tt →
Not "ongoing partnership." Not "trusted long-term advisor." A handoff.
On the Audit, it's the internal leader who'll own the roadmap.
On a build, it's the team that'll run the workflow we just shipped.
On Training, the handoff is the whole product.
On the Cost Audit, it's the admin who runs the monthly review from then on.
The test we apply: can the team run this without us? If the answer is yes, we step back. If the answer is no, we haven't finished.
This is the single biggest reason to work with us instead of hiring a freelancer or signing a long-term consulting retainer. Both of those are optimized for you to keep paying. We're optimized for you to stop.