How it works
We re-engineer one workflow at a time, and prove it before you commit to the next.
No deck, no requirements binder. From the first conversation, you're inside the work.
Before the audit
The first conversation isn't a sales call.
Come with a workflow that's bothering you. You'll leave with how we'd think about it — where automation, AI, or a person would likely sit — whether or not you go further with us. The audit is where we prove that with evidence. This is where we show you how we think.
You know the work. We know how to re-engineer it.
We don't need to be the expert in your process. You already are.
Your operators understand the exceptions, the dependencies, the workarounds — why the work actually behaves the way it does. That's not something we could learn faster than they already know it, and we don't try to. We bring the re-engineering method: what to automate, where AI helps, where a person has to stay in control. Then we run it — orchestrated on Krayso, measured from day one.
What's an agent
A job, start to finish.
An agent is a defined job — a clear start, a clear end. Inside that job, each step gets done the way it should:
- AI where judgement's needed — drafting, scoring, deciding what's next.
- Automation where it's just a fixed rule.
- A person where the call actually matters.
One job — AI, automation, and people, combined however the work actually needs it. A named person accountable for all of it.
The stages below show how that comes together, one workflow at a time.
1.0 — Find the workflow
We start with your priorities, not a guess.
We work with your leadership team to understand what matters most, and prioritise the workflows where fixing them will make the biggest impact. Then we go to the people who actually do the work — they're the source, not a sign-off.
2.0 — Build it into production
Live on your systems in weeks, not quarters.
No rip-and-replace. Agents deployed where the work already happens, measured from day one against the baseline you set. If it doesn't fit, we say so before anything proceeds.
3.0 — We stay while it beds in
We don't hand the workflow over cold.
We keep running the workflow ourselves, as a managed service, while your people learn to run the agents alongside us. Every agent has a written brief and a named human manager — clear on who's accountable, who's consulted, and who's informed. Nothing runs without someone responsible for it.
4.0 — Now it's yours to run
Your team runs it. We stay in the background.
Trained and certified, your team runs the workflow — orchestrated through Krayso. We're still there — improving, monitoring, optimising — but the capability is yours, not rented.
5.0 — Find the next workflow
Proof in hand, capacity keeps compounding.
Each workflow that goes live makes the next one cheaper and clearer to justify — without the next hire.
How the work is actually divided
Not everything needs AI. Some of it just needs to be automated.
Automation
Deterministic. Data sync, record creation, routing by fixed rule. Cheaper and more reliable than AI whenever there's no judgement call to make.
AI agents
Judgement, at the step level — drafting, scoring, matching. Used only where interpretation is genuinely required, routed to the cheapest model that can do the job.
People
Irreducible. Decisions, relationships, anything that sets a precedent. Never automated away — approved and owned by a named person, every time.
Every agent has a named human manager. Automation isn't asked to think. People are accountable, always.
Could you build this yourselves?
Fair question. The engine's the same one we use.
A general-purpose AI assistant and Krayso run on the same underlying models. The difference isn't the engine — it's what's missing when you try to run it yourself: no queue, no owner, nothing that survives past the session, no baseline to prove a saving against. Most DIY builds point at the most expensive model available, not the one suited to the step — we route every step to the cheapest model that can do it.
Vendor-built AI succeeds roughly twice as often as internal builds — 67% versus about 33%. — MIT NANDA, The GenAI Divide: State of AI in Business 2025, July 2025
60% of organisations started a Microsoft 365 Copilot pilot; only 1% completed a full rollout to all eligible workers. — Gartner survey, June 2024, reported via Computerworld