Pillar 2
Process Automation
Workflow automation and LLM integration for organisations that have identified manual or inefficient processes slowing them down. This pillar is often introduced after a Pillar 1 engagement, when we have enough internal context to identify where automation creates the highest leverage.
What we deliver
Workflow design and automation using n8n and Zapier
Custom LLM integrations for internal process enhancement (document processing, classification, generation, summarisation)
AI-assisted customer-facing features integrated into existing products
Data pipeline automation connecting internal tools and third-party platforms
Automation audits: identifying where manual effort can be reduced without introducing fragility
How clients arrive here
Pillar 2 is not sold cold. It is most effective — and most credible — when introduced after we have visibility into how a client actually operates. The trigger is usually a pattern we observe during a Pillar 1 engagement: repetitive handoffs, manual reporting, or data moving between systems by copy-paste.
What we do not do
We do not train custom models
We are integration and delivery specialists — we connect the right models and tooling to the right problems.
We do not operate ML infrastructure
We build the product layer that makes AI usable, not the platform underneath it.
Engagements that require data science, model training, or MLOps are outside our current scope and we refer those explicitly.
Our principles for AI in client work
Usefulness over novelty
We do not add AI to a product because it is interesting. We add it when it demonstrably solves a problem the user has. If a simpler solution works, we use the simpler solution.
Transparency with end users
Where AI is making decisions that affect users, the product should communicate that clearly. We push back on implementations that obscure AI involvement in ways that could mislead.
Evaluation before production
Any LLM integration we ship includes a defined evaluation method — how do we know it is working? What does failure look like, and how do we catch it? We do not ship AI features without a monitoring and feedback loop.