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Service · AI automation and agents

Agents that take on the repetitive work, with a person in charge of what matters

We design and build AI agents and automated workflows that read, reply, classify and update your systems. They plug into what you already use and log every step, so you always know what they did and why.

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What we solve

If someone on your team spends the day copying and pasting between systems, there is something to automate

  • Customer service. Answers on WhatsApp, web chat and email at any hour, with a handoff to a person when needed.
  • Documents. Invoices, purchase orders, contracts and forms that get read, validated and recorded without retyping.
  • Internal knowledge. An assistant that answers from your manuals, policies and procedures, and cites the source of every answer.
  • Processes across systems. Workflows that move information between your ERP, CRM, spreadsheets and email with no manual steps.
  • Triage and routing. Tickets, requests and emails that reach the right team with the data already extracted.

What you get

Not a demo: a process that runs

The solution in production

On your infrastructure or the cloud of your choice, connected to your real systems and data.

Controls and oversight

Clear limits on what the agent can do, human approval for sensitive decisions, and a log of every action.

Measurement

A dashboard with volume handled, time saved and error rate, compared with where you started.

Documentation and handover

Code, configuration and manuals are yours. Your team understands how it works and how to change it.

Examples

What it looks like across industries

Representative examples of the agents we build. Every company has its own; the diagnostic is how we find them.

Distribution

WhatsApp orders with no retyping

Customers write the way they always have. The agent understands the order, checks stock, totals it and records it in the ERP. Sales only reviews the unusual ones.

Professional services

Contracts reviewed in minutes

The agent compares every contract against your standard template and flags the clauses that differ, so the lawyer starts with what matters.

Manufacturing

Three-way matching on autopilot

Matches the purchase order, the invoice and the warehouse receipt, and only speaks up when something does not line up.

Clinics

Appointments and reminders on WhatsApp

Confirms, reschedules and reminds, freeing the front desk to look after the people in the waiting room.

Education

Admissions that answer at night

Answers prospective students about programs, requirements and costs at any hour, and books the call with the admissions team.

Retail

Returns and warranty claims without the queue

Takes the request, checks the purchase and the applicable policy, and leaves the case ready to approve in one click.

Safety

How we keep an agent from making a serious mistake

Language models make mistakes. We design assuming they will, not hoping they will not.

  • Suggest first, act later. Agents start by proposing while a person approves. Autonomy grows when the data shows it is safe.
  • Least privilege. Each agent reaches only the systems and data its task needs, and nothing else.
  • Tested on real cases. Before launch we evaluate it against examples from your operation, and we keep measuring quality afterwards.
  • Everything is logged. What came in, what the agent decided and what it did, so any case can be audited.
  • Your data does not train other people's models. We use business-grade services configured so your information is not used to train third-party models.

Technology

With what you already have, and the best fit for each case

If your system has an API, a database, or even just exports files, it can almost always be connected.

Models
  • OpenAI
  • Anthropic Claude
  • Google Gemini
  • Llama
  • Mistral
Automation
  • n8n
  • Make
  • Power Automate
  • Python
Channels
  • WhatsApp Business
  • Web chat
  • Email
  • Microsoft Teams
  • Slack
Systems
  • SAP Business One
  • Odoo
  • Microsoft Dynamics
  • HubSpot
  • Salesforce
  • Google Workspace
  • Microsoft 365
Knowledge
  • Semantic search (RAG)
  • PostgreSQL and pgvector
  • SharePoint and Drive

Questions

What people usually ask

How much does an agent cost?

It depends on how many systems it touches and how much volume it handles. After the diagnostic we quote the pilot. On top of that there is a monthly cost for model usage, which we estimate upfront so you can weigh it against the hours it frees.

Will it replace my team?

Most projects remove repetitive work, not people. Usually the same team handles more volume, or spends its time on work that needs judgment.

What if the agent gets something wrong?

That is why we start with human approval, clear limits on what it can say and do, and monitoring. We measure quality on real cases before and after launch.

Do I need modern systems?

No. If your system has an API, a database or exports files, there is almost always a way in. If there is not, we will tell you during the diagnostic.

How long until it is running?

A pilot usually takes 4 to 8 weeks, depending on complexity and how quickly we get access to systems and data.

Which repetitive task would you like never to see again?

Tell us what it is and how many hours it takes. In 30 minutes we will tell you whether it makes sense to automate.

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