AI and Automation Lab.Agents that do real work, inside guardrails you set.
We build AI agents, LLM integrations and the automations around them, connected to your systems through scoped permissions. Every agent ships with an evaluation harness, a cost and latency budget, and a clear point where a person takes over.
Build · Run · 24/7
01What we build
What the AI and Automation Lab builds.
Archetypes, named by what they do. Every engagement also carries the standards we apply in every lab.
- 01
Customer and staff agents
Assistants on web chat, WhatsApp, Teams or email that look things up, take actions and hand over cleanly when a person is needed.
- 02
Retrieval over your documents
Answers grounded in approved sources, cited back to the paragraph, with permissions that mirror who may read what.
- 03
Copilots inside existing tools
Drafting, summarising and checking built into the CRM, ERP or case system your team already works in.
- 04
Workflow automation
Multi-step processes across email, forms, CRM and finance on n8n, Zapier or code, with retries, error queues and alerting.
- 05
System sync and integrations
CRM, ERP and billing kept consistent through event-driven sync and a reconciliation report, not silent drift.
- 06
Custom MCP servers
Model Context Protocol servers that expose your own systems to agents through scoped tools and an audit log.
02Services
Services and deliverables.
Concrete scopes with named outputs, so you know what you will hold at the end of each stage.
Use-case workshop and ROI baseline
We rank candidate processes by value and risk, measure what they cost today, and agree what success means before any model is chosen.
- Ranked use-case map
- ROI baseline
- Risk classification inputs
Agents and retrieval
Production agents built on the Claude API and Agent SDK, connected to your systems through custom MCP servers.
- Agent with scoped tools
- Evaluation harness
- Human handover flow
Automation and integrations
Workflows on n8n, Zapier or code, chosen by volume, cost and who will maintain them after launch.
- Workflow designs
- Error queues and alerts
- Run documentation
LLMOps and run
Quality, cost and latency monitored in production, with a regression run whenever a model, prompt or tool changes.
- Cost and latency dashboard
- Regression suite
- Monthly quality review
03Live tool: Live agent demo
Watch an agent work.
Ask about our labs, approach or costs. You see how the agent reasons, which tools it calls, and the point at which it hands over to a person.
04Live tool: Automation ROI calculator
Price the automation.
Enter what a process costs you today. The result is indicative and shows its assumptions, so you can test it against your own numbers.
05Lab standard
The A* standard for this lab.
On top of fixed-price discovery, a named lead, code you own, test gates, observability, written decisions and an SLA-backed run retainer, this lab adds:
- 01
An evaluation harness with a golden dataset
Agents are scored against agreed real examples before release and again after every change to prompts, tools or models.
- 02
Cost and latency budgets
Each agent has a ceiling for spend per task and time to answer, monitored and alerted rather than discovered on the invoice.
- 03
Red-teaming before launch
We try to make the agent leak data, ignore its instructions or take actions it should not, then fix what we find.
- 04
An EU AI Act and ISO/IEC 42001 pack
Purpose, data, risk controls and oversight documented in a structure your compliance team can work from.
- 05
Retries, alerting and a measured baseline
Automations recover on their own where it is safe, alert a person where it is not, and are measured against the baseline agreed at the start.
06Human sign-off
Where people sign off.
Agents can do a great deal on their own. The decisions that carry legal or organisational weight stay with the people accountable for them.
- Data protection impact assessmentsYour data protection officer owns the DPIA. We supply the technical facts and change the design when the assessment asks us to.
- AI Act risk classificationClassification is a legal judgement made with your advisers. We document the system facts it rests on.
- Process redesignHow work should change around an agent is decided with the people who do that work today.
- Consequential actionsRefunds, account changes and anything irreversible can require a person's confirmation by design.
07FAQ
Questions, answered.
Which models do you use?
Mostly Anthropic's Claude through the Claude API and Agent SDK, with other models where a task or a data residency requirement calls for them. Agents are built so the model can be swapped.
Is our data used to train models?
Not by us. We use commercial API terms that exclude training on your data, and sensitive fields can be redacted before they reach a model at all.
What happens when the agent gets something wrong?
The build plans for it. Confidence thresholds, confirmation steps for consequential actions and a clean handover to a person are part of the design, and every action is logged for review.
n8n, Zapier or custom code?
Zapier suits low volumes and simple flows, n8n suits higher volumes and self-hosting, and code suits complex logic or strict testing needs. We recommend per workflow, not per vendor.
09Contact
Show us the work that should run itself.
Your first call is with an engineer from the AI and Automation Lab, not a sales script. Bring the system, the deadline and the constraints.
hello@aurionlabs.io