SERVICES
AI Automation and Agents
Automation that ships and keeps running, not a demo that works once.
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.
How it actually works
The automations that pay are rarely the ones that get demonstrated. A demo shows an agent completing a clean task end to end. A business process is mostly exceptions: the invoice with a missing PO number, the supplier who sends a PDF instead of a CSV, the customer who replies to the wrong thread. An automation that handles the clean case and fails silently on the rest creates more work than it removes.
So we begin with the process as it actually runs, not as it is described. We map the exceptions your team handles without thinking about them. We build the parts that can be rules as rules, because a rule behaves identically in two years and a model does not. We put an approval step in front of anything that writes, and a log behind it, so a wrong output is a correction rather than an apology. And we run it alongside the manual process before you rely on it.
The result is not a demo. It is something your team can operate, with documentation that survives the person who commissioned it leaving.
A read-only automation across one documented system is eight to twelve days. The same automation across four systems, writing and requiring judgement, is fifty to seventy. That difference, not the software, is the price.
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.
Is it for you?
It is, when
- The same task happens forty times a week, and someone could write the rules down in a morning.
- The systems involved have APIs and you have working credentials for them.
- You already know what the freed hours will be spent on.
It is not, when
- The process happens a few times a month. No automation pays back against forty hours a year.
- Every case is different. That is judgement, not process, and it costs several times more to automate.
- A critical system has no API. It will set the timeline regardless of everything else.
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.