What is enterprise automation with AI?
Enterprise automation with AI combines workflows, integrations and artificial intelligence agents to carry out tasks that currently need manual work: answering enquiries, processing documents, recording data in systems or preparing decisions. Metatech Perú designs it on top of existing systems, with explicit business rules, a trace of every action and human approval for sensitive decisions.
Who it is for
- Operations and customer service leaders with a high volume of repetitive tasks.
- Sales teams losing opportunities because of slow response times.
- Technology and transformation teams that must take AI from pilot to production.
The problem it solves
- Teams spending hours on repetitive tasks: copying data, answering the same questions, sorting emails or documents.
- Slow response times in service and sales because everything depends on a person being available.
- AI pilots that work in a demo but are neither integrated nor governed, and never reach production.
- Processes without traceability: nobody knows who did what, when, or with which information.
How it works
Select the right process
We assess volume, rule stability, data quality and risk. Not every process needs AI: many are solved with a workflow or an integration.
Design responsibilities
We define what the agent decides, what the workflow executes and what a person approves. Every agent tool has an explicit scope.
Build on a real flow
We implement the first flow end to end, connected to real systems, with tests and documented edge cases.
Operate and measure
We monitor answers, hand-offs and errors, and adjust knowledge and rules based on data rather than intuition.
What we deliver
- A map of candidate processes prioritized by impact, feasibility and risk.
- Flow design: what the agent does, what the workflow does and where a person steps in.
- AI agents with scoped knowledge (RAG) and least-privilege tools.
- Workflows and integrations with ERP, CRM, email, WhatsApp and other systems.
- Guardrails: scope limits, validations, approvals and an audit log.
- Operating metrics: volume, resolution rate, hand-offs and errors.
What it integrates
When it makes sense
It makes sense when
- There is enough volume for automation to pay for itself.
- There is information or knowledge that can be verified.
- The process has a business owner willing to define the rules.
- Before and after can be measured.
It does not make sense (yet) when
- The process changes every week and has no owner.
- Source data is inconsistent and nobody can correct it.
- AI is expected to make irreversible decisions without supervision.
- A simple rule or a well-designed form is enough.
Typical scenarios
First-line service
An agent answers frequent questions from the company knowledge base, checks the status of an order or request through an API and hands off to a person with the full history when the case requires it.
Document processing
Invoices, orders or requests arrive by email; data is extracted, validated against the ERP, and only exceptions go to human review.
Sales qualification
Leads from the website and WhatsApp get an immediate reply, are qualified with criteria defined by sales and are recorded in the CRM with the conversation context.