Five uses to put into practice
Choose an initial need. Together, we define which sources can be accessed, which actions are allowed and how success will be measured.
AI agent deployment
We configure agents that follow instructions, consult resources and use approved tools to carry out a task. Integration includes connections to your applications, testing and monitoring.
Example: An agent prepares a support request for handling: it gathers useful information, suggests a reply and waits for the team's approval before sending it.
Chat with your documentation
Ask questions in natural language about your procedures, guides and internal documents. We organise content retrieval so that the assistant bases its answers on identifiable passages, respecting each user's access rights.
Example: A colleague asks how to configure a service. The assistant finds the procedure and identifies the documents and passages to consult to check its answer.
Chat with your technical logs
Explore application and infrastructure logs through conversation. The assistant helps search for events, connect errors and summarise a timeline. Your teams still need to verify the suggested diagnoses.
Example: After an alert, ask which events preceded the error within a given period. The assistant presents the relevant log entries and possible causes to investigate.
Chat with your data
Query your databases, spreadsheets and exports in your own words. We define the metrics and access permissions, then connect questions to appropriate queries and calculations. Each result should be traceable to its source and filters.
Example: Ask how the number of requests in each category has changed over the past three months. The assistant returns the figures, the selected period and the calculation rules.
Automation assistance
We help you identify repetitive tasks, design workflows and connect applications. AI can classify text, extract information or draft content. Stable rules are handled by deterministic automation.
Example: When a document arrives, extract the relevant fields, check they are present and prepare an update in a business application, with human approval when information is ambiguous.
From the first use case to deployment
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1. Define the scope
We select a task with your teams, identify the available data and set expectations for answer quality, processing time and operating budget.
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2. Test
A prototype is evaluated on representative cases, including errors and missing information. Your teams check the answers, sources and proposed actions.
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3. Integrate and hand over
We prepare deployment in the selected environment, document how it works and help your teams get started. Monitoring covers errors, quality and costs.
Access and actions agreed with you
The choice of model and hosting depends on your confidentiality, quality and cost requirements. We define which data is transmitted, how it is retained and who can access it before connecting sources. Secrets and sensitive information in documents or logs should be filtered according to the project's needs.
An assistant can make mistakes or lack context. We plan for responses that flag uncertainty, verifiable sources where available and escalation to the relevant team. Sensitive actions require human approval; read access and limited permissions are preferred.