Antenor Services 05 of 07 Process Automation & AI Agents
AI process automation services
AI agents, workflow automation and integrations that remove manual steps end to end. Not RPA scripts that break when a screen changes: systems that read, decide and act within the policies you set.
Who this is for
Today
- Invoices keyed in by hand, checked twice, wrong anyway.
- Month-end close that eats a week of your finance team.
- Reconciliations living in one analyst's spreadsheet.
- Low-code bots that broke the third time a vendor changed a form.
After
- Documents read, validated and posted by agents; people handle only the exceptions.
- Close that runs continuously instead of monthly heroics.
- Reconciliation as a pipeline with an audit trail.
- Automation engineered like software, monitored like production.
What we build
AI agent development
Custom AI agents for business processes: they read invoices, emails and documents, apply your decision rules, act across your systems and escalate what they cannot resolve. Every action logged, every decision explainable.
AI workflow automation
Whole processes orchestrated end to end, not isolated tasks: intake, validation, decision, posting, exception handling and the audit trail. The metric is hours returned and error rates removed, counted against the baseline we agree in Discovery.
ERP, CRM and legacy integration
The unglamorous part that decides whether automation survives: connecting agents to the systems of record. APIs, databases, files, government tax platforms, banking interfaces. We have 25+ years of scar tissue here, and it shows in the uptime.
Proof, not promises
A finance back office, automated end to end.
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Discovery
We map the process, the constraint and the money attached to it.
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Data Audit
We test whether your data can carry the model before promising results.
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Proof of Concept
A pilot built against a success metric agreed before we write code.
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Production
Deployed into your stack with your team, not handed off as slides.
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Monitoring
Models drift. We keep watching them after launch, not just at delivery.
Straight answers
The questions buyers actually ask, answered with numbers where they exist.
What is the difference between RPA and AI agents?
RPA replays fixed rules against stable screens and breaks the day anything changes. AI agents perceive context, make decisions within policies you define, and handle variation: a differently formatted invoice, a missing field, an unusual case. RPA suits stable repetitive tasks; agents suit processes with exceptions and unstructured input.
Which business processes should we automate first?
The ones combining high volume, clear decision logic and measurable cost: invoice processing, bank reconciliations, order intake, commission settlement. In Discovery we rank your candidate processes by expected return and automate the best one first, so the business case funds the rest.
How long does it take to deploy an AI automation solution?
Typically 4 to 12 weeks per process, depending mostly on how many systems it touches. The first automated process is usually live within a quarter, measured against a baseline of hours and error rates agreed before the build starts.
How do AI agents integrate with our ERP, CRM and legacy systems?
Through APIs where they exist, and through files, database access or watched folders where they do not. We have integrated automation with state tax systems, banking interfaces and decades-old ERPs. Nothing gets ripped out; the agents work with the systems you have.
What's the difference between an AI agent and a chatbot?
A chatbot converses with people; an AI agent executes work. Agents read documents, make decisions and act across your systems with no conversation involved. The two combine well: a chatbot in front, agents doing the work behind it.
Bring us the bottleneck.
One session with a senior engineer. We'll tell you whether AI pays for it, and what it takes to ship.