For organizations whose information cannot leave their control, we deploy agents with open-weight models on your infrastructure or with commercial models consumed from your own cloud account. The rigor of evaluation, security and operation is the same either way.
Some information must not leave your organization, whether because of regulation, contracts or plain prudence. That does not mean giving up on agents; it means choosing carefully where the model runs.
We evaluate on your own cases which open or private model reaches the quality you need, and design an architecture where each piece of data takes the path its sensitivity allows.
vLLM for production and Ollama for pilots, on your GPU servers, with quantization and sizing for your load.
Commercial models through Amazon Bedrock or Azure OpenAI, consumed from your account, in your region and under your access controls.
We classify the sensitivity of every request and send it to the allowed model: the private one for sensitive data and the most capable one for everything else.
We compare models on your real tasks before you invest in infrastructure.
Deployments with no internet egress and controlled updates for models and dependencies.
Search over your documents with indexes and embeddings that also stay on your infrastructure.
Which data is involved, how sensitive it is and what regulation or contracts require.
We measure quality, latency and operating cost of candidate models on your real tasks.
GPU, memory, storage and network based on the model and expected load.
Installation, security hardening, load and behavior testing.
Controlled updates, regression evals and monitoring.
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
For many bounded tasks, yes; for others, no. That is why we measure on your real cases before deciding, and design hybrid architectures when they make sense.
It depends on the model and the load. In the benchmark we size GPU, memory and storage. You can also start in your own cloud account and migrate later.
Yes. We design air-gapped deployments with a controlled process for updating models and dependencies.
It helps, but compliance depends on your policies and processes. We work with your legal and security teams so the architecture supports those requirements (for example, Colombia's Law 1581 of 2012 on personal data protection).
We can operate them through AgentOps or hand the operation over to your team, with documentation and support.
A private agent still needs to be operated. See AgentOps →
We classify your data and measure models on your cases before recommending an architecture.