Agents and assistants
We develop internal assistants, chatbots, document search, intelligent classification, and agents connected to business tools.
Systems that can interpret information, search for context, query data, and execute bounded actions. Connected to the tools the team already uses, not a new platform to learn.
Some tasks don't follow fixed rules. They require interpreting context, checking information spread across several places, and making a reasonable call. A script isn't enough — but that doesn't mean you need something magic, just something well built.
What we analyze
- Internal assistants for employees
- Chatbots with access to real data
- Intelligent document search
- Automatic classification
- Agents connected to business tools
How it works
An agent is a system with access to a bounded set of tools — searching documents, querying a database, executing a specific action — and clear instructions on when to use them. It doesn't decide without limits: it operates within a defined scope, with a log of what it does and checkpoints where a person signs off before anything real happens.
What it can do
- Search and summarize information spread across several documents
- Query real data from connected systems
- Classify and prioritize incoming requests
- Guide a repetitive task step by step
- Execute a specific action within a defined scope
What it shouldn't do
- Make irreversible decisions without human sign-off
- Act outside the set of tools it's been given
- Replace judgment that carries legal or professional liability
- Make up information when it doesn't have it — it has to be able to say it doesn't know
How we build it
The outcome depends on how it's built, not just the model behind it.
- Tool access — a limited, explicit set, not open access
- Context — the relevant information, no more, no less
- Document search — over your real documents, not generic memory
- Validation — checks before an answer is trusted
- Oversight — human checkpoints on the actions that matter
Examples
- Internal assistants that answer using the company's real data
- Document search across contracts, manuals, or historical records
- Automatic classification of requests or incidents
- First-line support with access to the knowledge base
- Queries about operational data in plain language
- Guided step-by-step tasks with human oversight
When it makes sense
When the task requires interpreting natural language, searching across documents, or making decisions with variable context.
When it doesn't
When the process has fixed, predictable rules — in that case conventional automation is more reliable and cheaper.
How a project starts
We start with one specific, bounded use case, not an assistant that does everything. A small, well-defined scope is what makes an agent reliable from day one.