Home / Capabilities / Agentic AI & Automation
AI agents for enterprises, in production and inside your private cloud
The agent queries your ERP or CRM via MCP, completes the task and cites its source. No need to replace your systems.
What we solve
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Your team copies data by hand between the ERP, the CRM and email.
An agent queries your systems via MCP and carries out the task.
- 02
A generic assistant confidently answers with something that is not in your documents.
Private RAG that cites its source and abstains when there is no support.
- 03
Connecting AI seems to require replacing your systems.
Connectors on top of what you already have: SAP, Salesforce, ERPs and APIs.
An agent that would rather stay silent than make things up.
Ask the agent below a question. It answers only if it finds support in its sources and cites where the answer came from; if not, it abstains and escalates. You control the confidence threshold and see the calculation.
Demo with a sample corpus of six fictitious fragments. In a real project the corpus is your corporate data, the agent runs inside your VPC and every answer is traced.
What you receive
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Agent in production on your current stack
Running on your current systems, without replacing them.
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Knowledge base with citations and abstention when there is no supporting evidence
Every answer says which document it came from.
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Traceability of every agent execution
What the agent did, when, and with which data.
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Vendor-agnostic architecture: the model is swapped through configuration
If a better model comes out, it is swapped by configuration.
Technical focus: Autonomous AI agents and multi-agent orchestration · Private RAG over real corporate data · Deterministic connectors via the MCP protocol (SAP, Salesforce, ERPs, banking and logistics APIs) · Deployment in the client's private cloud · Guardrails and DLP for AI
What is an AI agent?
A system that receives a goal, decides the steps and uses tools (querying a system, calculating, sending a request) to get it done, under defined supervision and limits. An assistant talks; an agent acts. For example: it resolves a customer request end to end, reconciles invoices against the ERP or validates the documents to onboard a supplier.
What is RAG?
RAG (retrieval-augmented generation) is the technique in which the model first searches for relevant passages in your own knowledge base and answers based on them, citing the source. It reduces made-up answers because the agent can abstain when it finds no supporting evidence.
How is it different from RPA?
RPA automates fixed, predictable steps; an agent handles exceptions and natural language. In real projects they are often combined: RPA for the repetitive and agents for what requires judgment.
How it is used in each way of working
Questions about Agentic AI & Automation
Is my data sent to a public model?
We design so that data and code reside in your private cloud, and the model is configurable to avoid dependence on a single vendor.
Do you replace my ERP or my CRM?
No. We connect the agents to your current systems (SAP, Salesforce, ERPs, APIs) through the MCP protocol, without replacing them.
Which AI models do you work with?
Whichever solves your case best. The architecture is model-agnostic: switching models or providers is configuration, not rebuilding the agent.
Shall we start with Agentic AI & Automation?
A person on our team will reply.