AI Automation and Agents
Short answer
AI automation replaces repetitive process work with software that runs unattended: data moving between systems, documents read and classified, drafts prepared for review. PHOENIX VERUS builds these as engineered systems with approval steps, audit logging and a defined failure path, rather than as prompts wired to an inbox.
Automation that ships and keeps running, not a demo that works once.
What the engagement covers
- Process mapping
- What actually happens today, including the exceptions people handle silently. Most failed automations were modelled on the tidy version.
- Deterministic first
- Rules where rules work. A model is introduced only where judgement is genuinely required, because rules stay correct and models drift.
- Approval and audit
- Anything that writes — invoices, messages, records — gets a review step, a log and a rollback.
- Handover
- Documentation and training so your team can operate it, or a support arrangement if you would rather we did.
How it runs
- Map the process and agree what success means numerically.
- Build the deterministic path end to end.
- Add model-based judgement only where it earns its place.
- Run in parallel with the manual process before switching over.
Frequently asked
What makes an automation project expensive?
Four things: how many systems it touches, whether it writes as well as reads, how much judgement is involved, and who operates it afterwards. The pricing page publishes those drivers in full.
Do you use n8n, or custom code?
Both, decided by the process. Workflow tools are faster to build and easier for your team to change. Custom code wins where volume, latency or error handling matter more than editability.
What if the model gets something wrong?
That is a design question answered before the build starts. Every model-based step gets an evaluation set, a confidence threshold and a defined path for what happens when it falls below it.
Contact us
Send us the brief or book a short call. You get concrete next steps in reply.