Answers from your own data
Assistants that answer only from approved sources, show where each answer came from, and hand over to a person when they are unsure.
AI Integration & Agentic Workflows
AI that works inside your rules, not around them. Put AI to work inside your products and operations: assistants that answer from your own data, agents that take approved actions in your systems, and automations that connect them, with quality checks, cost limits and human sign-off built in. Managed or private models, your choice.
The problem
Why this goes wrong without the right expertise.
What we see in the field
A demo needs a prompt and an API key. Production needs controlled access, safe actions, a way to say “I don’t know”, an audit trail, a cost ceiling and tests. Most teams find that gap after launch.
What we deliver
Concrete, merged, documented.
Everything lands in your repositories and your cloud subscriptions, with the runbooks to own it.
Safe actions in your systems
Your business operations exposed to AI (including over the Model Context Protocol) as limited, logged actions that preview changes before they apply.
People in the loop
AI drafts, people approve. Approval steps, audit logs and rollback are designed in, not bolted on.
Quality checks on every change
Automated tests with real questions that stop a worse prompt or model from reaching customers.
Private and self-hosted models
Open-weight models running inside your own Azure subscription when data cannot leave it. Prompts and data never leave your environment.
Workflow automation with n8n
Self-hosted n8n connecting AI to your CRM, ERP, ticketing and finance systems, with approval steps and every workflow version-controlled.
Cost control and model routing
Usage tracked per customer and per feature, with each request sent to the cheapest model that meets the quality bar.
Typical stack
- Azure OpenAI
- Microsoft Foundry
- Self-hosted LLMs (vLLM)
- n8n
- Model Context Protocol
- Azure AI Search
- .NET / Semantic Kernel
Proof, not slides
Where we have already done this.
Our own products run these patterns in production. FAQ Hub answers from approved content with citations and hands over to a person when it cannot. Max & Mia answers trade customers’ calls and hands the owner a structured job card. Our own lead capture runs on n8n.
Related insights
Go deeper.
Designing an MCP Server Agents Cannot Misuse: Lessons from FAQ Hub
What we learned exposing a shared SaaS platform to AI agents over the Model Context Protocol: scoped keys, plan-then-apply tools, explicit publish and a gap feedback loop.
Read the article →Other services
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From a separate copy per customer to one platform that serves them all.
Explore →Talk to an expert, not a sales team.
Tell us about your stack, your constraints and where AI needs to fit. A founder replies within one working day.