Working with company materials
An agent finds and processes information in the documents you make available. We account for source access and how answers can be checked.
Services / AI agents
AI agents
built around your business.
We build agents that use company data and tools to perform defined tasks. We also develop environments for evaluating their output and refining how they work.
When it helps
A clear task. A way to evaluate the result.
We start with the work an agent should complete and how its output will be assessed. This establishes which information, tools and permissions it needs.
Tasks across several tools
An agent can prepare an output, invoke an available tool and hand work to the next stage. Its scope follows agreed permissions and approval points.
Environments for developing agents
We prepare reference materials, example tasks and evaluation criteria to test agents under conditions that reflect everyday work.
Solution scope
What the solution includes.
Scope and operating rules
The task, input data, expected output and situations requiring a person to take over.
An agent and its tools
Agent logic, access to agreed sources and connections to selected applications after their integration options have been assessed.
Examples and quality evaluation
Tests using representative materials, acceptance criteria and a review of the solution's limitations with your team.
How we work
How we develop an agent.
Understand the task
We examine real examples, user expectations and the consequences of errors. We agree where the agent can operate independently and where approval is required.
Build and evaluate
We connect models, data and tools. We compare outputs against agreed criteria, including incomplete inputs and unusual cases.
Join the workflow
We review the solution with your team within an agreed scope. The results inform which elements need further development.
A real implementation
Behave Studio
An agent for content production.
An AI video-editing agent developed around Behave's reference materials and approved templates. The implementation connects editing tools with a production memory tailored to the channel's style.
Read the case study
Questions & answers
Before implementation.
Where should an AI agent project start?
With one recurring task and a few real examples. Together, we establish what a good result looks like and whether the available materials allow it to be evaluated reliably.
Can an agent work with our documents?
We can design that workflow after reviewing document formats, quality and access rules. How information is stored and made available is part of the project.
How can we control the agent's actions?
Tools and permissions are defined for the task. Actions that need a decision can be routed to a team member for approval, while output quality is assessed using agreed examples.
What determines project scope and cost?
The number of tasks and integrations, data preparation, quality requirements and how exceptions are handled. Scope is established after reviewing the process and available systems.
Let’s talk
What should your agent do?
Describe the task, your current workflow and the tools your team uses. We will shape the conversation around your process.
Describe your workflowoffice@eclipsesystems.com.pl