ALMANOR AI — SERVICES
Almanor AI's forward-deployed engineers embed inside your organisation — learning your workflows, your data, and your edge cases before a single model is trained.
Most AI vendors build software in isolation. We build it inside your operations.
THE METHOD
Our engineers spend weeks — sometimes months — working inside your teams before any model is trained. They learn the workflows that documentation doesn't capture, the exceptions that break every rule, and the institutional knowledge held only in the heads of your most experienced people.
That embedded knowledge becomes the training signal. It is why Almanor AI systems outperform anything built on generic datasets.
WHY IT MATTERS
The rules that govern edge cases are rarely written down. Embedded engineers learn them by working alongside your most experienced staff.
Data captured from live operations is domain-specific, high-quality, and unavailable to any competitor. It is the moat that makes Almanor AI systems hard to replicate.
AI built by engineers who have seen the real environment handles the unexpected. Systems built from documentation alone do not.
WHY REMOTE DELIVERY FAILS
The standard enterprise AI delivery model works like this: a vendor collects requirements in a series of workshops, builds a system in their own environment using synthetic or sample data, delivers a demo, and then begins a long, painful integration period during which the system fails to handle real cases in ways nobody anticipated.
The failure is always the same. The vendor never saw the edge cases — the documents that don't match the template, the workflow exceptions that aren't in the process manual, the institutional knowledge that lives in the heads of three specific people. These are precisely the cases that matter most, because they are the cases where AI systems fail most visibly.
Almanor AI's forward-deployed model eliminates this problem by putting engineers inside your operations before the design phase begins. They observe, participate, and capture the real operational environment — including every edge case, exception, and piece of institutional knowledge — and use this as the foundation for everything that follows.
WHAT THE EMBED PHASE LOOKS LIKE
Engineers sit alongside your team — observing case handling, document processing, decision-making, and escalation flows. No interviews. No workshops. Direct observation.
Engineers map the data flows, identify the exception cases, and work with your subject matter experts to document decision logic that has never been written down.
Using the captured operational data and documented decision logic, engineers build and integrate the AI system — testing against real cases from the observation phase.
The system goes live alongside your existing operations — with monitoring dashboards, escalation paths, and a continuous improvement loop embedded from day one.
FREQUENTLY ASKED QUESTIONS
RELATED