AI agents and business automation
Software that does the work, not just answers questions.
An AI agent is software that does the work instead of only answering questions. It reads an incoming email, understands what the sender wants, calls the right tools in your systems, and either finishes the task or hands a person only the decision. That is the difference between a chatbot and an employee-shaped piece of software.
Ltiora builds tool-calling agents and MCP setups connected to your real systems, with email automation and intent understanding that route every request to the right place. The gap between a demo and a dependable agent is measurement and boundaries, so we build evaluation in early, keep a person in the loop where a mistake would be expensive, and design the token cost in from the start.
What it looks like in practice
- Tool-calling agents and MCP setups connected to your real systems
- Email automation and intent understanding that route every request
- Workflows that run themselves, with a person in the loop where it matters
- A working slice on your own data before anything is committed
- Evaluations that score the agent's behaviour, so quality is measured rather than assumed
- Clear limits on what the agent may do on its own and what it must hand to a person
Imagine An assistant that reads every incoming email, understands what the customer wants, does the routine part and hands you only the decisions.
How an engagement runs
- 01
Discover
A real conversation about the problem, then a close look at how the work actually happens today. We tell you plainly if we are not the right fit.
- 02
Prototype
A working slice on your own data, not a slide deck, so you can judge it with your hands.
- 03
Ship
Production-grade delivery: security, cost controls and the foundations that keep it standing.
- 04
Operate
Evals, monitoring and steady improvement, so the system gets better after launch instead of decaying.
Questions people ask
What is an AI agent?
An AI agent is software that uses a language model to decide which actions to take and then takes them through tools: reading an email, looking up an order, filling in a record. Unlike a chatbot that only replies, an agent completes tasks in your systems.
How do you keep an AI agent from making costly mistakes?
By building evaluation in before features, giving the agent narrow permissions, and keeping a person in the loop for decisions where an error would be expensive. Quality is scored on your own examples, so you can see how it behaves before it touches live work.
What is MCP?
MCP, the Model Context Protocol, is a standard way for AI models to connect to tools and data sources. Setting it up properly means an agent can use your systems through well-defined, controllable connections instead of one-off glue code.
Can an agent work with the tools we already use?
Usually yes. Agents are connected to existing systems through their interfaces. What matters is which actions are safe to automate, and that is something we work out with you at the start.
How do we get started?
Describe the problem through the questionnaire or the AI assistant on this site. We reply personally and say plainly whether and how we can help.