Process automation consulting and AI agents for companies
Agents that nail a demo and never touch a real business process. We don't start by building software: we start from the same diagnosis and architecture we govern in Intelligence Lab, and only when the right solution genuinely doesn't exist in the market do we build it custom — integrated into that same architecture, not as a standalone build.
Automation measured against the real process, not the demo.
01Process automation
Complete workflows, not loose tasks — from the trigger to the outcome the business actually needs.
02Agents by CIRA phase
Agents specialized in diagnosis, vendor evaluation, risk monitoring, and adoption measurement — each with a clear owner and purpose.
03Impact measurement
Every agent is measured against the real process it replaces or accelerates, not against a demo — time, error rate, and team adoption.
We're not a software factory, and we won't propose building something that already exists in the market. The same agnostic diagnosis we use to select an ERP or CRM is what we apply here: we first evaluate whether the right solution already exists.
When it genuinely doesn't, we know how to build it: process automations and specialized AI agents for tasks within each phase of the CIRA method — diagnosis, vendor evaluation, risk monitoring, adoption measurement — integrated into the enterprise AI architecture we already govern, so it doesn't become one more ownerless tool.
An AI agent is a system that receives a goal and completes a multi-step task on its own: it looks up information in your systems, applies business rules, drafts or updates records, and alerts a person when it needs a decision. It differs from a chatbot in that it doesn't just answer questions: it acts on processes.
Some use cases in companies are classifying and answering incoming requests, preparing and reconciling information across systems, watching indicators and flagging deviations, and assisting a team with repetitive analysis tasks. None of them works well without a clear process behind it and someone inside the organization who is accountable for the result.
This is how we work on an AI agent: first a specific process is chosen and the way the result will be measured is agreed; then we define what the agent can do on its own and what must go through a person's approval; next it's tested with real cases from operations and adjusted; and finally it runs with a clear owner inside the organization. Without that order, an agent impresses in a demo and changes nothing day to day.
An AI agent isn't always the answer. If the process is stable and rule-based, RPA or traditional automation is cheaper and more predictable; if the process isn't defined, it has to be put in order first. Some risks of a poorly designed agent are excessive permissions, wrong answers delivered with confidence, and lack of traceability, which is why we design it together with AI governance.
Every agent lives inside a bigger framework
Frequently asked questions
It's a system that receives a goal and completes a multi-step task on its own: it queries your systems, applies business rules, acts on them, and escalates to a person when a decision is needed.
A chatbot answers questions within a conversation. An AI agent also takes actions on the company's processes and systems. That's why it needs permissions, governance, and measurement, not just a good model.
When there's a specific, repetitive process with a measurable result that takes up the team's time today, and someone inside the organization can be accountable for it. If the process is stable and rule-based, RPA or traditional automation may be cheaper and more predictable; if it isn't defined, it has to be put in order first.
No. We're digital transformation consultants, not a development house. We first evaluate whether the right solution already exists in the market — as we do in CIRA's Solution Selection phase. We only build when that option genuinely doesn't exist.
We design every agent around a specific business process, not as a generic layer on top of everything else.
We prioritize with the same diagnosis we use in CIRA's Construct phase — real impact, not the flashiest task to automate.
We work alongside your technical team, not in place of them. The automation gets integrated into the architecture and AI governance we've already defined, so your team can maintain it.
Every automation is measured against the process it replaces: time saved, error rate, and real adoption by the team — not the polish of a demo.
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