No jargon, no runaround. Here's what leaders ask us most — about what an agent does, how the thirty-day proof works, how it integrates and is governed, what it costs, and what you own at the end. Still have a question? Bring it to a workshop.
Dandori builds prepared AI agents for operationally complex companies — built for the mid-market, strong enough for enterprise teams, and proven on one real workflow in 30 days. We prepare and build one working agent around a real workflow in your operation — governed by your rules, integrated with your systems of record, proven on your own work, and owned by you.
No. That's the point. Your team understands your operation — the workflow, the rules, the way you want things handled. We handle the AI. Our whole method is built so you never have to become a prompt engineer or babysit a tool. You tell us how the work should go; we prepare an agent that does it that way.
An agent is a piece of AI that does a job — not a chatbot you talk to, but a worker you delegate to. Where a typical "AI assistant" waits for you to tell it what to do every time, an agent has been prepared in advance: it knows your process, your rules, and the moment it should stop and ask. It reasons, decides, and acts — within the boundaries you set.
No. General AI tools retrieve and summarize — a smarter search box. We use AI for the harder thing: to reason, plan, and carry a whole workflow end to end, against explicit rules, connected to your real systems. The value isn't the model; it's the preparation around it that makes it reliable on your actual work.
Repetitive, rule-based work that eats your team's hours: drafting quotes, chasing overdue invoices, answering routine customer questions, booking and confirming appointments, keeping records in sync across your tools, sorting documents, preparing the reports you check every week. If it follows a pattern and you can describe it, it's a candidate.
No — it clears the busywork so your people can do the work only humans should. An agent handles the repetitive, after-hours, easy-to-get-wrong tasks and hands anything that needs judgment straight to a person. Leaders tell us it feels less like replacing staff and more like finally freeing skilled people from the drudgery.
The ones you already use — email and chat (Microsoft 365, Google, Slack), your CRM and finance systems (Salesforce, HubSpot, QuickBooks), your booking, billing, and helpdesk tools. If your work reaches deeper systems — ticketing, cloud, infrastructure — we handle those too. We build around your stack, not the other way around.
You can, but we usually recommend against it. Start with the one workflow costing you the most in time or cycle time, prove it in thirty days, then add the next. Because the groundwork carries over, the second agent is faster and cheaper to build than the first — so there's no rush to do everything at once.
The specific outcomes we wrote down together at the start — the tasks the agent must complete and the rules it must follow. If it does those on your real work, it works. If it doesn't, you don't pay for the build.
The build fee for the thirty-day engagement. Any third-party costs (like the AI model usage itself) are always yours, and we're transparent about those before we start.
Because the risk in AI projects is almost always the preparation, and that's the part we're disciplined about. We settle the workflow, rules, and scope before we build — so thirty days is realistic, not a gamble.
You decide: keep the agent running and pay, or walk away owing nothing for the build. If you keep it, we're there for adjustments and, when you're ready, the next agent. Either way, what we prepared is yours.
Every agent runs inside boundaries you set. It does only what it's authorized to do, checks with you at the points that carry risk, and logs every action so nothing happens in the dark. During the proof it runs alongside your team on real work precisely so mistakes surface and get corrected before you rely on it. And every correction is written down and kept — as yours.
Everything we prepare: the workflow map, the rules and boundaries in plain language, the agent's configuration, and everything it learned during the build. It's documented and portable. No lock-in, no black box, no holding your own business logic hostage.
We design data handling into the preparation from day one — what's accessed, what's logged, what's retained, and where it lives. Sensitive work can run inside boundaries you control. Nothing about how your data moves is left vague or decided after the fact.
No — the opposite. What we build is yours and portable, the logic is kept independent of any single AI model, and everything the agent learns is captured as yours to keep. If you ever want to move to another model, take it elsewhere, or run it yourself, you can — and you carry every learning with you. We'd rather serve you well enough that you stay by choice than trap you into it.
Our framework runs many models and routes each task to the right one — open-source models where data must stay private or run on your own infrastructure, commercial frontier models only where the work genuinely needs them. That's how we deliver fast without overpaying and keep sensitive data secure by design. And because models keep commoditizing — better, cheaper, more specialized every year — we deliberately don't chain your agent to any single one. The agent improves over time through a feedback loop, and we capture those learnings as portable AI infrastructure that sits above the model. So when a better model arrives, you move to it and carry everything the AI has learned across — you upgrade the engine without losing what it learned, and without rebuilding. The AI grows with any model.
With a short workshop — a working session, not a sales pitch. Bring one repetitive task that's eating your team's hours. We'll find where a prepared agent earns its place, sketch the rules it would follow, and show you exactly what thirty days would deliver. There's no obligation.
That's what the workshop is for. The best first candidates are repetitive, happen often, are currently done by hand, and touch tools you already use. If you're not sure, we'll help you find the one with the most time to save and the clearest result.
Dandori is built for enterprises that need working AI without a multi-year program — whether your AI team is stretched thin or you don't have one yet. If a workflow repeats often enough to matter and you can describe how it should go, it's a fit. What matters isn't scale for its own sake; it's a workflow worth preparing.