AI Campaign · Custom

First working AI solution in your process — in 4 to 8 weeks.

No slide decks, no isolated demos. We pick one concrete process, build a custom pilot on your data and validate whether it actually delivers value — before you commit long-term.

Plan your AI Opportunity Scan → See what we build
Reply within one business day Free 30-min scan Control, governance & GDPR by design
Why this approach

Four stories we hear often — recognise any?

If one of these sounds painfully familiar, it's a good starting point for an AI Opportunity Scan.

"We have ChatGPT licenses, but nobody uses them for real work."

Generic chat doesn't know your documents, clients or rules. We build AI that actually plugs into your systems, data and workflow.

"Our people retype the same reports out of the ERP every week."

Document extraction and structured output. Build once, runs daily — with confidence scoring and human review for edge cases.

"We did an AI pilot before — then it stalled."

Pilots not designed for production stay as samples. We start with production as the end goal and build towards it in a controlled way.

"Everything I read about AI is hype. What actually works for my organisation?"

Honest answer in a 30-minute scan. Sometimes AI is the answer, sometimes a regular integration or a better form. We'll say so.

What we build

Four patterns we apply every day

Not one-size-fits-all, just proven patterns we adapt quickly to your context, data and workflow.

From 4 weeks

Chat over internal knowledge

A Q&A that knows your docs, manuals and processes. Answers with source references — no hallucinations, with role-based access.

RAG Vector DB Source citation
From 3 weeks

Document extraction

Turn PDFs, emails, scans and forms into structured data. Runs daily with confidence scoring and human-in-the-loop for edge cases.

OCR Vision LLM Human-in-the-loop
From 6 weeks

Agents with tools

AI that doesn't just answer — it acts: pulls data, fills forms, routes tickets — with audit log and approval steps.

Tool-use Audit log Approval gates
From 6 weeks

AI copilots inside your app

AI embedded inside your own web app — as a side panel, smart field, or natural-language search. Part of the UI, not beside it.

Streaming UI Context-aware Fallback
How to start

Start small, prove value fast

Two concrete products to start with AI in a practical, low-risk way — without committing to a large project upfront.

Step 1 · Product

AI Opportunity Scan

A focused product to identify where custom AI can add the most value. A concrete shortlist of opportunities, prioritised by relevance, speed and business impact.

  • Intake session + process analysis
  • 3–5 promising AI use cases
  • Assessment of impact, feasibility and fit
  • Clear advice on the best first step
Plan a scan →
Step 2 · Product

AI Pilot MVP

A working pilot for one concrete process challenge. Not a prototype for the shelf, but a custom solution you can test in practice, evaluate with your team and use as the basis for rollout.

  • Workflow and solution design
  • Working custom solution for one use case
  • Testing, monitoring and validation
  • Plan for further development
Start a Pilot MVP →
Approach

Four phases — honest about week one

We start small, validate fast, and only build for production once the pilot has proven its value. No AI projects stuck in pilot for months.

1

Use-case scan — Week 1

A 60-minute conversation with your team. We determine which process takes the most time, whether AI is the right answer, and what a first working prototype looks like. Problem & data mapped, AI vs. classical automation, success criteria agreed.

2

Pilot on real data — Week 2-4

We build on your documents and your systems — no demo data. At the end: something running, evaluations that measure if it works, and an honest conversation about what does and doesn't. Working pilot, evaluation set & metrics, go/no-go decision.

3

Production build — Week 4-8

Only now do we really break ground. Authentication, monitoring, cost controls, fallback paths, audit logs. AI that keeps running without us. Monitoring & observability, cost & rate-limit controls, human-in-the-loop where needed.

4

Continuous improvement — Ongoing

Models change. Your data changes. We stay involved — monthly review, periodic evaluations, and new use cases once the first works. Monthly review, model updates & evals, next use case.

Security & Governance

Deploy AI without losing control

For most organisations, security and governance are not optional extras — they are part of the business case. That is why we design custom AI solutions from the start with control, traceability and manageability in mind.

What we always include:

  • Human approval where needed

  • Logging and auditability of AI actions

  • Role-based access to data and functions

  • Clear boundaries for what AI may and may not do

  • EU hosting and secure implementation choices

  • Attention to GDPR, data flows and operational ownership

A hybrid setup is often the smartest first step

Full autonomy is rarely the best starting point. In many business processes, a hybrid setup — with AI support, review moments and escalation rules — creates faster trust, lower risk and better adoption. That creates the right foundation for further scale.

GDPR-aware Audit trail Controlled autonomy Human-in-the-loop RBAC EU hosting
Frequently asked questions

What organisations often ask us

An AI Opportunity Scan is free (30–60 min). A Pilot MVP typically ranges from €8k–€20k depending on data complexity. Production build starts from €25k and scales with scope. We work fixed-fee per phase with a clear go/no-go between pilot and production. No open-ended contracts.
By default we host in the EU (Hetzner, Vercel EU, AWS Frankfurt). For sensitive data we run open-source models on your own infrastructure — Llama or Mistral on a private GPU host or Bedrock. OpenAI and Anthropic offer enterprise tiers where data isn't used for training; we arrange those contracts with your DPO.
It depends on the use case. Document extraction often runs well on GPT-4.1 or Claude Sonnet. Classical classification can use a small open-source model. For agents we often pick Claude for better tool use. We isolate the model choice behind an abstraction layer, so you're not locked to one vendor when pricing or policy changes.
In your repo, on your cloud, with your team as co-developers. We don't leave behind 'RocketCode-only' code that becomes a regret later. For longer projects we commit at least one engineer full-time; for smaller pilots we work in 1–2 week sprints.
We'll say so honestly. A well-built form, a Zapier flow or a regular API integration often solves more than an LLM with RAG. We advise what fixes the problem — not what sounds most exciting.
Before building we lock in an eval set: a few hundred examples where we know the right answer. On those we measure accuracy, cost per call and latency. On model updates we re-run the evals. In production we log every call (anonymized) so drift and regressions show up early.
ChatGPT is a great personal tool but knows nothing about your documents, clients or systems. We build the layer underneath: embedded in your workflows, with your data, with proper guardrails. Put short: ChatGPT helps someone type an email faster. We make sure the email doesn't need to be typed at all.
Start with one concrete process

Discover your highest-impact AI use case

Schedule a no-obligation intake and we'll explore where a custom AI solution can reduce manual work, improve execution and create the most value for your organisation. Reply within one business day — from a real engineer or designer.