AI Agents

AI Agents that connect your systems and act on your data. With a source for every statement and approval before every action. Locally in your own data center or flexibly in the cloud.

Connect. Understand. Act.

The agent connects your systems, understands your questions, and gets done what needs doing – without your data ever leaving your environment.

Cited, not claimed.

The agent answers from your documents, databases, and business systems, not from the language model's memory. Every statement can be traced back to the exact passage in the original.

As autonomous as you allow.

The agent researches, drafts, and prepares actions. Whether it waits for your approval or executes on its own, you decide for each workflow individually.

You choose the model and the deployment.

The agent works with the language model of your choice. Connect it via API, or run open models fully locally within the AltaSigma platform.

The answer is almost always already in your company – in manuals, data sheets, contracts, tickets, spreadsheets. It's just that no one can find it fast enough.

You ask the question. The agent finds the way to the answer.

Every AI Agent sits at the center of its documents, data, and tools. It handles simple tasks in a single step. For complex ones, it combines sources, evaluates intermediate results, and follows up until the answer is solid.

AI AgentOrchestration
DocumentsWiki & intranetTables & databasesTickets & emailBusiness systemsVector indexFull-text indexGraph index
Ticket replyCRM entryEmailReportML prediction
Approval

Built for the systems you already have.

The agents talk to file storage, databases, SharePoint, your ticketing system, and your CRM. And to any system you still want to connect.

Documents and files

DocumentsPDF, Word, PowerPoint
SpreadsheetsExcel, CSV
Web and wiki pagesHTML, intranet
File storageNetwork shares, S3-compatible object storage

Structured data

DatabasesRelational and analytical databases
Data lakesParquet, CSV, Delta Lake, Iceberg
Graph dataRelationships between entities
ML modelsPredictions from your own models

Knowledge and communication

Knowledge platformsConfluence, SharePoint, Notion
Tasks and projectsJira, Asana, Planner
Chat and messagingMicrosoft Teams, Slack
EmailOutlook, Exchange, Gmail

Business systems and actions

Ticketing systemsZendesk, Jira Service Management, ServiceNow
CRMSalesforce, HubSpot, Dynamics
Line-of-business applicationsIndustry-specific systems
Custom APIsany API via MCP

Don't see your system?

Our agents use the MCP standard, so no system is out of reach: existing MCP servers connect directly, and missing ones we build to your requirements. Access rights and approvals are always part of the integration.

Run agents like models: versioned, measured, comparable.

Every agent is an object in Model Management, with its own sources, its own evaluation suite, and its own history. So you can see what a model change or a modified setting actually does.

No answer without evidence.

  • See every step. Each multi-step search is laid open: which tools the agent called, in what order, with what result – shown above every answer, with one click down to the parameters.
  • Jump to the original. Every reference below the answer opens the document at the exact passage that supports it – highlighted, with page number and relevance score.
  • Add your own knowledge. Abbreviations, internal terms, exceptions: enter them into the knowledge base via feedback, and the agent works with them from then on.

Continuous quality monitoring.

  • Measure retrieval accuracy. The right tools, the right documents – recall and precision show, per tool, how reliably the agent finds what matters.
  • Measure answer quality. Each agent can have its own evaluation suite with reference answers. Every answer is scored against it in a structured way – continuously, after every change.
  • Test models and configurations. Every run is a version in Model Management; compare two of them to see what a model swap or a changed setting does on the same evaluation suite – today against yesterday, or variant A against B.

Stay in control of your data.

How many agents run, where the platform runs, and which model does the work is up to you. Everything is managed in one place: AltaSigma Model Management.

  • Model Management

    Every AI Agent is an object in Model Management — alongside your ML models and locally hosted language models, with the same tooling for versioning, deployment and monitoring.

  • As many agents as you need

    Not one agent for everything, but the right one for each area. Every agent works with its own sources and rules, strictly separated from the others.

  • Flexible deployment

    On-premises, private cloud, or public cloud: you choose where it runs, the platform stays the same. On Kubernetes, with the full feature set.

  • Observability

    Every LLM and tool call is recorded and can be traced. Connect your existing monitoring via OpenTelemetry, or use the monitoring stack that ships with the platform.

  • Your choice of model

    Any OpenAI-compatible endpoint works. Open language models run locally, managed in Model Management.

  • Data sovereignty

    Local models mean processing, storage, and answers stay within your infrastructure. No data flows out to third parties.

In practice: where AI Agents are at work today.

Four examples from customer service, knowledge management, engineering, and financial controlling.

Suggested replies for support tickets

Every customer request lands in the ticketing system, and every second one has been answered in much the same way before. The agent reads the new ticket, searches resolved cases, manuals, and product data, and drafts a reply with references. Your support team reviews, adjusts, and approves.

Scattered knowledge, one place to ask

Process descriptions in SharePoint, project docs in Confluence, decisions in Jira comments, the rest on network drives. The agent connects all of these sources and answers questions across them, every statement with a jump to the original document. Access rights carry over from the source systems: everyone sees only what they are allowed to see.

Error codes and spare parts in seconds

A technician is standing at the machine and needs to know what fault code E-417 means and which part number fits, not the twenty manual PDFs it is buried in somewhere. The agent finds the exact term, jumps to the highlighted passage in the original, and returns the part number from the data sheet.

Ask your data instead of writing queries

"How did order intake in the southern region develop compared to last year?" A question that used to take a SQL query and a ticket to the data team. The agent explores the table structure and the data itself, runs the necessary queries, and delivers the result along with an explanation. When the question is about the future, it calls your forecasting models.

Tell us about your use case.

Manuals, spreadsheets or just a well-stocked SharePoint: bring your case along – we'll tailor the demo to it.

FAQs

Agentic RAG (agentic retrieval) means the language model plans the research itself instead of firing off a single, fixed query. The agent selects sources and tools on its own, queries them and works through several steps when needed.