Not chatbots. Agents that plug into your systems, work with bounded permissions and leave a trail for every decision.
The model is the easy part. What makes an agent trustworthy is everything around it.
The model never touches a system directly: an intermediate layer validates, limits and logs every call.
Whoever checks is not whoever acts. A verifier approves every action with side effects before it runs.
The agent gets only what it needs, from permissioned sources, and forgets what it should forget.
Behavioral, data, tool and operational limits, defined before the first prompt is written.
Every step is traced: what it saw, what it decided, which tool it used and with what permission. Designed to catch drift before your users do.
Every task goes to the right model by cost, latency and data sensitivity, including private models.
Production errors become test cases. The agent improves on evidence, not intuition.
Companies that trust Nivelics
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
Security audits for AI agents: instruction injection, data leakage and abuse of connected tools: an attack surface traditional pentests do not cover. See Cybersecurity and Ethical Hacking →
We pick one process, define measurable success criteria and the autonomy level of each action.
Deliverable: Harness design and scope
Least-privilege tools, agent, verifier and eval suite.
Deliverable: Agent in staging with evals
Production with limited scope, human approval and full observability.
Deliverable: Agent in production and dashboard
AgentOps: monitoring, drift, costs and new capabilities.
Deliverable: Periodic quality and cost report
Custom AI agents that run real processes, integration of your systems through APIs and MCP, private AI deployments on your infrastructure or cloud, and ongoing agent operations (AgentOps). Also sales agents, process automation, content management and AI-powered marketing.
With one concrete process. In discovery we define measurable success criteria, the systems involved, and which actions the agent can run on its own and which need approval. That leads to a controlled pilot in production.
Not with the architecture we design. We can process your data in your cloud (Amazon Bedrock or Azure OpenAI, with regional deployments and without the provider using it for training) or on your own infrastructure with open-weight models.
We design so errors are detectable and contained: an independent verifier, evals before every change, human approval for irreversible actions and per-action traceability. Every production error becomes a test case.
We design agents to absorb repetitive, high-volume work and to keep important decisions in people's hands.
Tell us about the case and we will propose how to take it to production.