A working prototype in weeks, wired into what you already run.
n8n, Make, Power Automate or Zapier for the wiring. Claude, Azure OpenAI or open-weight models for the reasoning. Guardrails from week one, real systems from day one. If the prototype earns its keep, it hardens into production; if it does not, you learn that for the price of a spike.
The first two figures are founder casework: a Jira Service Management + n8n build at a regulated Maltese operator (~100 staff). The full story, including the part where developers tried to hack it, is in the field notes below.
Everyone has an AI strategy. Few have a working flow.
The gap between the board deck and the daily workflow is where AI initiatives go to stall. Prototyping fast, inside the real stack, is how the gap closes.
Pilot purgatory
The AI initiative has been "in evaluation" for six months. Slides exist, a sandbox exists, a committee exists. A thing an employee can actually use on a Tuesday does not.
Tool-first shopping
A platform got bought before the use case was found. Now the licence renewal is coming up and the strongest argument for it is sunk cost.
Every choice feels irreversible
Which model, which vendor, which framework: teams freeze because each decision looks like a marriage. So the decision defaults to nothing, and the backlog keeps growing.
We plug in where you are. All of it swaps.
Every prototype is the same fabric: your systems on one side, outcomes on the other, and a core of orchestration, guardrails and a model in between. Pick a scenario and watch how the same pattern lands in three different stacks.
Illustrative wiring. Real prototypes are scoped to one use case, one system and one measurable outcome, then judged on the numbers.
The automation rule
“A senior engineer at €80k/year isn't a password-reset queue. Automation is.”
The workflow that replaced half an L1 queue.
Before ITSailor, the founder ran this build at a regulated Maltese operator (~100 staff): an IT team drowning in Jira backlog, where a "give me access to folder X" ticket sat for three days waiting on three approval signatures.
The build. Jira Service Management wired to n8n through webhooks and internal system APIs: a self-serve bot that grants simple permissions the moment a manager approves in Slack or Teams. The engineers went back to engineering.
The part that went wrong
During testing, developers tried to game the bot by injecting malicious JQL payloads to grant themselves admin in production. The flows were rewritten with hard RBAC limits, and injection testing has been part of every build since.
What that attack changed
Guardrails go in during week one, while the prototype is still small enough to reason about. Permission boundaries, injection-hardening and the human approval step are part of the first wiring, because your own users will probe the system long before an outsider does.
Two weeks in, you hold six artefacts.
All of it lives in your repository and your tenant, whichever way the go/no-go memo lands.
Feasibility verdict
The use case scored against GDPR Art. 22 and the EU AI Act risk classes before anything is built. Some ideas should stay ideas; we say which and why.
A working prototype in your tenant
Wired to real systems through n8n, Make, Power Automate or Zapier. Real tickets, real documents, real approvals, from the first week.
Prompt + tool pack, version-controlled
Prompts, tool definitions and system messages live in your repository, injection-hardened and reviewed like any other code.
Golden-question eval set
A regression harness of real cases with expected outcomes. Model swaps and prompt changes get tested against it, in CI, before they ship.
Cost model per outcome
What a resolved ticket, a processed invoice or a drafted follow-up actually costs in tokens and euros, measured on the prototype, projected to production volume.
Go / no-go memo
An honest verdict with the hardening plan if yes, and the documented reasons if no. Either way the eval set and the findings stay yours.
Spike, integrate, then keep it healthy.
Fixed fee or retainer, agreed before work starts. No hourly billing at any tier.
AI Feasibility Spike
Two weeks. One use case. A working answer.
- Use-case scoping with GDPR Art. 22 + AI Act risk check
- Prototype wired to one real system (ticketing, mailbox, CRM)
- Model and orchestrator picked from what you already run
- Golden-question eval set seeded from your real cases
- Go / no-go memo with cost-per-outcome numbers
Outcome
You know if it works, what it costs and what breaks, for the price of a spike.
Production Integration
The prototype that earned its keep, hardened for daily use.
- Abstraction layer: model and vendor become configuration
- Injection-hardening, rate limits and permission boundaries
- Eval harness wired into CI: regressions fail the build
- Langfuse observability, self-hosted: traces, costs, latency
- Runbooks and Exit Kit in your repository
Outcome
AI in the daily workflow with the same discipline as the rest of your stack.
Platform Retainer
We keep the AI layer healthy while your team uses it.
- Monthly eval runs with drift and quality reporting
- Model swaps and price renegotiations as the market moves
- Cost watch: per-outcome economics reviewed monthly
- New use cases spiked against the existing fabric
- Single Slack channel to the engineer who built it
Outcome
The AI layer keeps earning its keep as models, prices and your team change.
What we actually build with.
Picked per engagement, starting from what you already pay for. Nothing here is exotic, and nothing here is mandatory.
n8n (self-hosted)
Default wiring when nothing is set: runs in your infrastructure, no per-task tax.
Make
Visual scenarios for SaaS-heavy teams that want to see the flow.
Power Automate
The native choice inside a Microsoft 365 tenant. No new platform to buy.
Zapier
Fastest wiring for simple SaaS-to-SaaS handoffs.
Anthropic Claude
Strong reasoning and tool use; the default for agent-shaped work.
Azure OpenAI (EU)
GPT-class models with EU data residency and tenant-level controls.
OpenAI
API-tier access where the workload and data classification allow it.
Open-weight via Ollama
Local and self-hosted models when the data must not leave the building.
Langfuse (self-hosted)
Traces, evals, token costs: the observability layer under every build.
pgvector / Qdrant
In-tenant vector stores when the prototype needs retrieval.
Fast does not get to mean careless.
Prototypes move quickly because the compliance questions are answered before the build, in the feasibility verdict, where changing the answer is still cheap.
Decisions with legal or similarly significant effect keep a human in the loop. The approval step is designed in, and it is not removable by prompt.
Every use case is classified before the build. High-risk shapes get flagged in the feasibility verdict, with the obligations spelled out.
Azure OpenAI EU endpoints, EU-hosted orchestration or fully self-hosted open-weight models. The data classification decides, and the fabric supports all three.
Built from three standing services.
Custom AI Integration
The engagement behind this page: scoping, model selection, evals, production wiring.
View serviceWorkflow Engineering
The n8n and Power Automate build discipline, with or without a model in the loop.
View serviceDECKLOG Implementation
When the prototype needs private retrieval: grounded answers with citations, in your tenant.
View serviceHonest answers to the questions buyers actually ask.
Do we have to adopt a new automation platform for this?
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No. The prototype goes into whatever you already run: Power Automate inside a Microsoft tenant, Make or Zapier for SaaS-heavy teams, self-hosted n8n when nothing is set or when per-task pricing stings. The orchestrator is a delivery detail, and the abstraction layer means it can change later.
Can this run EU-only, or fully on-prem?
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Yes. Azure OpenAI EU endpoints cover most regulated cases; fully self-hosted open-weight models via Ollama cover the rest. Our own DECKLOG retrieval core started as a local Ollama prototype, so the on-prem path is one we actually use, in our own product work.
What happens if the prototype shows the idea does not work?
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Then the spike did its job. You get the go/no-go memo with the documented reasons, the eval set, and the cost numbers, and you did not spend a platform budget finding out. A spike that can only ever say yes is a sales demo.
How do you handle GDPR Article 22 and the AI Act?
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Feasibility scoring includes the AI Act risk classification and a GDPR Art. 22 check before anything is built. Where a decision has legal or similarly significant effect, a human approval step is part of the architecture, and DPIA support is available when your DPO needs it.
Who owns the prompts, flows and evals afterwards?
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You do. Everything lives in your repository and your tenant: prompts, tool definitions, workflow exports, the eval set, the runbooks. The Exit Kit documents it well enough that another vendor could take over in 24 hours. That is the Sovereign Mastery rule.
Who actually does the work?
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One senior engineer, the same one you meet on the discovery call. No account managers, no offshore hand-off, no junior rotation. You get a single Slack channel and a direct line to the person writing the prompts and the flows.
Put one use case in front of real users instead of another evaluation.
The workshop picks the case with the shortest path to a measurable result, and names the data, the owner and the kill criterion before any code is written.