One working AI agent for one defined process. In production within 2-4 weeks, with human escalation built in, fixed price upfront.
30 minutes, free of charge, no sales pitch
Four familiar situations where AI presentations don't translate into working systems.
Plenty of demos, plenty of webinars, plenty of presentations. But no working system in your own organization where the difference is measurable.
Which process is suitable as a first agent? Which one has enough volume, clear rules, and low risk if something goes wrong? Without that choice everything stays theory.
Deloitte and McKinsey programs are built for enterprise budgets and timelines. At SME scale they become expensive documents without a working result.
No clear escalation rules, no measurement, no knowledge transfer. That's the combination where AI projects stall without leaving anything behind.
Five principles we apply to every agent project we run for SMEs.
No 450-use-case roadmap, no Deloitte deck. Just something that goes into production tomorrow on one clearly defined process.
Fixed-scope quote after the 30-minute agent scan. No open ended commitments, no surprises later, no time-and-materials drift.
We don't deliver an agent without human-in-the-loop for edge cases. That's the direct lesson from Klarna and the difference between a demo and production.
Concrete KPIs agreed upfront, telemetry built into the system. No "AI strategy" report, just hard numbers showing what improved.
Data stays within agreed boundaries, audit trails built in, no opaque foreign vendor chains. We build to the requirements the EU AI Act sets for high-risk systems.
From first conversation to a working agent in production, with measurement and knowledge transfer.
Together we determine which process is most suitable as a first use case: volume, rules, risk and feasibility at SME scale.
Scope, deliverables and deadline on a single page. You know upfront what you'll get, when, and what it costs. No open ended commitments.
We build the agent with clear escalation rules (human-in-the-loop where needed) and connect it to your existing systems. Live within 2-4 weeks.
Telemetry so after 2 months you can say with hard numbers what improved. Plus documentation, so your team can extend the system themselves.
No pilot without an end, no abstract advice. A concrete system that fully runs one process, with escalation built in and measurement from day one.
What enterprises shared publicly about agents in production, translated into what SMEs can learn from it.
Klarna replaced 700 full-time customer service roles with agents and then scaled back for the complex cases. Not a failure, but a design correction: agents for the routine, humans for the edges.
Not one mega project, but 450 separate, defined agents. That's exactly the scope discipline we apply at SME scale: one process at a time, make it measurable, then expand.
reMarkable built one working agent for one defined process, in production in three weeks. That's not an enterprise program, that's an SME-realistic template, and exactly how we set up every first project.
From public sector to SMEs: businesses building their AI systems with And AI.
Enterprises that succeed don't pick one big roadmap, they start small: one clearly defined process per agent, with escalation to humans for edge cases. Klarna learned that even they had to scale back for the complex cases. JPMorgan chose 450 separate use cases instead of one mega project. The common thread: scope discipline and human-in-the-loop.
Not with a 50-page strategy or a Deloitte deck. With one defined process that takes a lot of time and has clear rules (customer questions, quote drafting, invoice classification, contract review). reMarkable built one working agent in three weeks. That is the SME-realistic template: one process, one agent, in production within 2-4 weeks.
Processes that are recurring, have clear rules, and where a mistake can be corrected before it reaches the customer. Concretely: first-line customer questions, quote drafting from templates, invoice classification and booking proposals, contract review against house rules, supplier confirmations. During the agent scan we pick the best fit together based on volume, rules and risk.
Pricing varies by scope. After the free 30-minute agent scan we know which process we'll automate and how much work it involves. You then receive a fixed-scope quote upfront, so no open ended commitments and no surprises later. An investment, not a cost: the return sits in time saved on the recurring task.
Once the quote is approved, the first working version is in production within 2-4 weeks, connected to your existing systems. We measure from day one, and after 2 months you'll have hard numbers on the KPIs we agreed upfront. No pilot without an end, just an agent that gets work done.
We design for that from day one. Every agent has clear escalation rules: as soon as the input falls outside agreed scope or confidence is too low, it goes to a human. That's exactly the Klarna lesson: agents are strong on the routine, humans stay essential for the edge cases. Human-in-the-loop is not a weakness of the system, it's part of the design.
30 minutes, free of charge. One process, one working system, fixed price upfront. Together we determine which use case is most suitable and what an honest estimate of feasibility and investment looks like.
