Customer service
Agents that answer questions, triage issues, look up documentation and hand off to your team when needed.
AI Agents
AI agents for business
We design, build and integrate agents that work on your real processes: they handle requests, query systems, carry out tasks and hand off to your team when needed.
We usually start with an assessment. See AI consulting →
What they are
An agent is
automated work with judgment
An AI agent combines a language model with tools, data and business rules. It can understand a request, look up information, make decisions within set limits and take action. The key isn't the technology: it's the process it automates and the controls designed around it.
01
Understands context
Interprets natural language, history and system data to respond accurately.
02
Uses tools
Queries databases, APIs, documentation and internal apps to get tasks done.
03
Acts with control
Takes actions within defined limits and hands off to your team when appropriate.
Use cases
Where it adds value
an AI agent
It's not about automating everything. It's about finding repetitive, high-volume processes with clear rules, where an agent can improve speed, quality or coverage.
Sales and lead qualification
Agents that handle first contact, gather information, qualify opportunities and brief your sales rep.
Operations and back office
They take over repetitive work: data extraction, report generation, validations and handoffs between systems.
Human Resources
Candidate support, onboarding, answers on internal policies and help for the HR team.
Finance and analytics
Agents that query data, write summaries, flag variances and prepare the information you need to decide.
Voice and phone support
Voice agents for inbound and outbound calls: information, appointments, follow-ups and first-line support.
Method
From use case
to production
01
Use case assessment
We define the goal, the process, the available data and the agent's success metrics.
02
Design and architecture
We choose the tools, model, memory, integrations, controls and human handoff.
03
Working prototype
We build a usable first version in weeks, with real data and workflows, to prove the value.
04
Integration and deployment
We connect the agent to your CRM, ERP, email, databases or internal tools and put it into production.
05
Evaluation and continuous improvement
We measure quality, cost, latency and resolution, then iterate on real data to improve the agent.
Best practices
A useful agent
isn't a demo
The difference between an impressive prototype and an agent that pays off is in the design: scope, data, integrations, control, evaluation and continuous improvement.
- Start with a focused, measurable use case.
- Design the human handoff from day one.
- Control data access and privacy.
- Evaluate quality and bias before scaling up.
- Measure cost per interaction and the agent's ROI.
- Document behavior and keep it traceable.
Development
We design the agent
with you
I work with business and technical teams to define the scope, build a prototype, integrate it with your existing systems and measure results. No hype, no black boxes.
FAQ
Questions about
AI agents
What's the difference between an AI agent and a traditional chatbot?+
A traditional chatbot follows rules or answers FAQs. An AI agent can reason, use tools, query systems, take actions and adapt to each case, with human oversight.
How long does it take to get an AI agent up and running?+
A working prototype can be ready in 2 to 4 weeks. A production agent with integrations, metrics and controls usually takes 6 to 12 weeks, depending on complexity.
Do we need our data ready?+
Not always. During the assessment we review what information exists, its quality and what the agent needs to work safely.
Can it integrate with our CRM or ERP?+
Yes. We integrate through APIs, databases and automations. The integration is defined during the architecture phase.
What happens if the agent can't solve something?+
It hands off to your team with the full context of the conversation. A good agent knows its limits and asks for help when it should.
How do you measure the ROI of an AI agent?+
With metrics like automated resolution rate, response time, cost per interaction, qualified leads, hours saved or customer satisfaction.
Contact
Tell me about your
use case
Describe the process you want to improve, the workload and the tools you use. I'll tell you whether an AI agent makes sense and how I'd approach it.
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