An agent is only as good as its integrations. We build the layer that connects your ERP, databases, internal APIs and legacy systems to AI agents using the Model Context Protocol (MCP), with least privilege and an audit trail for every call.
Many agents do not fail because of the model: they fail because they cannot safely read from or write to the systems where the work lives.
MCP is the open standard for exposing those systems as well-defined tools. We design those tools so the agent can do exactly what it should, and nothing more.
We expose your systems as tools with clear schemas, input validation and responses designed for a model.
When there is no API, we build one on top of databases, files, queues or existing interfaces, without touching the original system.
Internal APIs, events and queues so agents and systems communicate reliably and idempotently.
Least privilege, reads separated from writes, usage limits, secrets out of the model's reach and defense against instruction injection through tools.
Every call is logged: who requested it, which agent made it, with which parameters and what the result was.
Combines deterministic steps with agent decisions, with retries and compensation.
Which systems are involved, what data they expose, who owns each one and what constraints apply.
Every tool with its schema, its minimum permission and its read or write policy.
MCP servers and adapters with validation, limits and audit logging.
Injection through tools, permission abuse and behavior under load.
Tool versioning, usage monitoring and evolution alongside your systems.
What we offer is what we use every day.
In Niveleads, our commercial platform, agents prospect, build campaigns, send automated emails and review how each lead engages. They also make AI voice calls, run technology watch, support growth and marketing, and read signals to give the team insights.
We build software with agents that write, test and review code, but we do not leave everything to AI. Deliveries go through an independent QA agent and verification standards defined by our team. Whoever builds is not whoever approves.
nivelics.com is designed to be read by agents and LLMs as well: a dynamically generated llms.txt, schema.org structured data and content written with LLMs in mind.
Illustrative scenarios of how we build. They do not describe clients or results.
An organization with many systems and an operations team flooded with alerts.
The agent correlates each alert with recent deployments and changes, queries logs and runbooks, classifies severity and proposes an action. Reversible actions pre-approved in the runbook, such as restarting a service, run once the verifier signs off, and the team is notified. For irreversible ones it prepares the plan and waits for human approval.
Harness: Read-only tools by default · verifier checks against the runbook · traces for every decision · evals built from past incidents
A process that crosses several systems and teams, with rework caused by inconsistent data.
An orchestrator receives the request. An integration agent validates data across the ERP, the document system and the database. An execution agent records the changes, and an independent verifier confirms consistency before closing. Exceptions reach a person with all the context already assembled.
Harness: One least-privilege MCP server per system · idempotent transactions with compensation · model routing · AgentOps dashboard
The Model Context Protocol is an open standard that Anthropic introduced in November 2024 to connect AI models with tools and data. Since December 2025 it has been governed by the Agentic AI Foundation, under the Linux Foundation. It defines how an agent discovers and uses tools in a uniform way, with no custom integration for each model.
Yes. We build an adapter that exposes the system in a controlled way, without modifying the original system.
No. It is an open, neutral protocol supported by several clients and models. The tools we build work across different models.
Every tool has the minimum permission it needs, we separate reads from writes, constrain parameters and require verification or approval for any action with side effects.
Yes. The integration layer also puts your systems in order for other uses, such as automation, reporting or new applications.
Your data cannot leave your network? See private AI →
We start with a systems and permissions inventory: you will know what can be exposed, how and with what risk.