Agency specialised inAI Automationwith judgement to automate what delivers real value.
We design automations that combine classic orchestration (N8N, Make, Zapier) with a generative layer (GPT, Claude) for processes that used to require hours of expensive human time. With the judgement to decide what to automate well — and what to leave in human hands.
Automation without judgement produces automated chaos. AI without judgement produces automated chaos at higher speed. The useful question is not "can we automate this?"; it is "does automating this with AI add value, or is classic orchestration enough, or is it best done by a person?".
Situations where AI automation delivers real value
Not every process should be automated. These are the situations where we systematically see clear return.
Lead qualification at volume
You receive dozens of leads per day and your sales team is not qualifying them fast enough — or prioritising them poorly. An AI layer can qualify, enrich and prioritise in seconds.
Personalised follow-up that never happens
You know a well-crafted follow-up multiplies conversion, but the team never gets there. An AI layer writes and adapts the message to each lead's context, under senior review.
Reporting that arrives late or raw
Weekly reports arrive uninterpreted, or late, or not at all. We automate extraction and add an AI layer so the report arrives already interpreted.
Content at scale keeping the brand voice
You need to produce content — product descriptions, posts, copy variants — at volume without everything sounding the same. AI with brand context solves it; without context, it produces noise.
Operational documentation out of date
Your processes are poorly documented and everything depends on key people. We automate the generation and maintenance of documentation from real sources (tickets, code, conversations).
Customer service triage
First-line support consumes hours resolving repetitive queries. A vertical AI agent can resolve 60-80% and escalate the rest already qualified and contextualised.
Four pragmatic phases
Focus on business value. No over-engineering and no tools that do not contribute.
- 01
Process mapping
Sessions with business and operations to identify processes with volume, repetition and real value. We discard what should not be automated yet.
- 02
Prioritisation
A matrix of cases ordered by impact, feasibility and cost. We close what is addressed first and what stays for later — with clear success criteria.
- 03
Design and build
Pragmatic architecture: which orchestration tool, which generative model, how it integrates with your stack, with what controls and what observability.
- 04
Launch and operation
Gradual activation with monitoring. Clear monthly reporting, data-driven iteration and scaling to new processes once the first is stable.
Tangible deliverables
Automations in production, not slides. Clear documentation and real handover to the internal team.
Prioritised map of automatable processes
Inventory of processes with impact, feasibility and priority. Used to decide what is tackled in Phase 1 and what stays in backlog.
Automations in production
Live, monitored operational flows with success metrics. Not prototypes: automations already saving hours.
Technical and functional documentation
Each automation documented with architecture, dependencies, metrics, human-escalation criteria and maintenance process.
Performance dashboard
Unified panel with processed volume, success rate, hours saved, marginal cost and perceived quality.
Maintenance playbook
Operational manual so your team can adjust prompts, review outputs and scale the automation when conditions change.
Team handover
Applied training for your internal team. The goal is that you can operate and evolve the automations without external dependency.
What clients usually ask before starting
Which tools do you use?
It depends on the case. Classic orchestration: N8N (open source, self-hosted), Make, Zapier. Generative layer: GPT-4/5, Claude, Gemini per use case. Custom API integrations when they add value. We are not partisans of any single tool.
Can I use my own private models?
Yes. We work with OpenAI, Anthropic, Google Vertex AI, AWS Bedrock and on-premise deployments. When the sector requires it (finance, health, legal), we design with private models from the start.
What about GDPR and the AI Act?
We design with compliance from the process map. We document what data enters each automation, where it is processed and under which legal basis. If a use case cannot comply, we say so upfront.
How do you prevent AI from failing in production?
With layers: prompt validation upstream, output monitoring, quality metrics, human escalation when confidence drops below threshold, and periodic senior review. AI does not operate alone without controls.
Does it integrate with my current CRM / ERP / helpdesk?
Yes. Salesforce, HubSpot, Zendesk, Freshdesk, SAP, Odoo, Pipedrive… We work with the APIs you already use. When there is no API, we evaluate alternatives before proposing anything.
Can I start with a single process?
Yes, that is what we recommend. A pilot with a clear, measurable process with quick value. When that is stable and shows ROI, we scale to the next. No big-bangs.
Which process would you automate first?
Tell us which process is consuming most hours of expensive people without adding differential value. We reply within 48 business hours with an honest read — automatable, not yet, or best not to.
Shall we talk?A senior consultant signs the reply. Confidentiality by default.