Your board wants an AI strategy. Here's one that actually works.

A working AI application built around your most time-consuming process, in 14 days and ready to test on your own data. All for a fixed price.

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Where AI initiatives actually go nowhere

Strategy decks, zero shipped

Six months of consulting slides, frameworks, and 'AI maturity assessments.' Not one process is actually faster, cheaper, or better. The board is asking what the budget actually bought.

ChatGPT wrappers calling themselves products

A vendor demos a slick UI on top of OpenAI. Looks magical for ten minutes. Falls apart the moment it hits your real data, your edge cases, or your compliance team.

Hallucinations you can't defend

The model invents an account number, a policy clause, a customer name. By the time you catch it, it's already in front of a customer or in an audit log. There's no way to explain it away.

Demos that never reach production

The proof of concept ran on sample data, with no auth, no monitoring, no rate limits. Productionising it is a six-month replatform, so it never happens and the budget evaporates.

Vendor lock-in disguised as innovation

The platform fee creeps up. Your data is now in a format you can't easily extract. Switching providers means starting over. 'AI strategy' has become 'managing the AI vendor.'

Compliance says no

GDPR, SOC2, sector-specific rules: your AI demo can't answer where the data went, who saw it, or how to delete it on request. The use case dies in legal review.

Strategy decks, zero shipped

Six months of consulting slides, frameworks, and 'AI maturity assessments.' Not one process is actually faster, cheaper, or better. The board is asking what the budget actually bought.

ChatGPT wrappers calling themselves products

A vendor demos a slick UI on top of OpenAI. Looks magical for ten minutes. Falls apart the moment it hits your real data, your edge cases, or your compliance team.

Hallucinations you can't defend

The model invents an account number, a policy clause, a customer name. By the time you catch it, it's already in front of a customer or in an audit log. There's no way to explain it away.

Demos that never reach production

The proof of concept ran on sample data, with no auth, no monitoring, no rate limits. Productionising it is a six-month replatform, so it never happens and the budget evaporates.

Vendor lock-in disguised as innovation

The platform fee creeps up. Your data is now in a format you can't easily extract. Switching providers means starting over. 'AI strategy' has become 'managing the AI vendor.'

Compliance says no

GDPR, SOC2, sector-specific rules: your AI demo can't answer where the data went, who saw it, or how to delete it on request. The use case dies in legal review.
Why most AI initiatives stall

You've already tried the alternatives.

Use ChatGPT directly

Quick to start, impossible to defend in production.

  • No connection to your systems

    It can't read your CRM, your warehouse, or your ticket queue. Every answer is generic until someone copy-pastes context, and that's not a workflow.

  • Nothing to audit

    There's no log of what was asked, what was returned, or what action was taken. Your compliance team has nothing to sign off on.

  • Confidently wrong

    It will make up a policy, a number, or a clause and present it as fact. There's no grounding to your sources, so every output needs human verification.

Buy an off-the-shelf AI tool

Built for the average customer, not for your actual process.

  • Demo data isn't your data

    The shiny demo runs on synthetic examples. Your real data has missing fields, inconsistent formats, and edge cases the vendor never tested.

  • Lock-in you can't reverse

    Your prompts, fine-tunes, and workflow live inside their platform. Migrating away means rebuilding everything from scratch.

  • Roadmap controlled by someone else

    The feature you need lands in their next quarter, or never. You're optimising your business around their release schedule.

Hire an AI consultant

You get a deck. You don't get a working system.

  • Maturity assessments, not software

    Months of workshops produce a slide pack about 'AI readiness.' Nothing your team can actually use the next day.

  • Recommendations without ownership

    They tell you what to build, then leave. Implementation, integration and monitoring, the hard part, is now your problem.

  • Generic frameworks, your costs

    The same playbook gets sold to your competitors. You pay senior-rate hours for advice that doesn't account for your stack, your data, or your customers.

What your AI application actually does

This is what practical AI looks like.

Software
An e-mail from a supplier with an invoice attached. AI has read the invoice and filled in the supplier, amount and due date, and drafted a reply, ready for a person to approve.

Working AI in your daily process

AI integrated into the application we build for you, so it works inside your team's daily process. Vision, speech and language models take over the reading, listening and typing your team does by hand: documents read, calls written up, replies drafted.

Signal
Test results on 412 of the business's own invoices: 396 read correctly, the hardest cases listed one by one, and the ones it could not read sent to a person.

Real outputs on real data

Ready to test on your own data and your hardest cases. You see where it gets things right and where it doesn't, before anyone relies on it.

Decision
Results after two weeks: 14 hours saved per week, 3 cents per invoice, 96% read correctly, a log of every step, and a choice to roll out or stop.

Decide with audit data, not vendor claims

Audit logs, cost data, accuracy on your edge cases, throughput at your real volumes. Concrete evidence to fund the rollout, or stop it early instead of two quarters in.

Continuity
Team and access settings: sign-in with the company's Microsoft account, a role for each person, and an activity log of who did what.

AI that carries forward

Production-grade from day one: real login, role-based access, audit logging. If the first version proves out, you're already running the production system. No replatform, no rip-and-replace.

Roberto Heckers

Roberto Heckers

Developer at Vattenfall

Our deployment setup went from 20 manual inputs to just 2. That's the level of simplification Cermus brought.

See what we build
Turn your AI strategy into something real. 
Turn your AI strategy into something real. 

Tell us where your team is losing time.
We'll show you how it could be solved with a working AI system in 14 days.

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