of agentic AI projects will be canceled by the end of 2027.
Source: Gartner ↗your company.
We make companies ready to operate Enterprise AI solutions through our Context Infrastructure.
Most AI pilots never see the light of day.
Not for lack of a model. For lack of a foundation: incomplete data, answers without a source, access without governance.
of generative AI pilots show no measurable return.
Source: MIT ↗AI answers with confidence about what it does not know.
no one can point to the source of the answer.
everyone accesses everything, or no one accesses anything.
What is missing is not a better model.It is a piece of architecture:the layer that turns the information your company already has into AI-ready knowledge.
How the Context Infrastructure works:
On one side, the systems you already use. On the other, the answers. In between, the platform.
- Foundationthe operation's data lake
- Connectors50+ sources plugged in
- Knowledge graphthe digital brain
- MCP Servereach person and each agent sees only what they are allowed to
- Event Busreal time and alerts
- Agentsin the workflow
- Monitoringthe operation in plain sight
- Governancewho can do what, on record
“which clients have maturities this week?”
3 clients, with the origin of each piece of information
who to serve first, and why
each priority opens its rationale
“this CDB is guaranteed to beat the CDI”
conflict with CVM 179, with the excerpt from the meeting
This already happens in your company every day. What changes is that information stops getting lost: when it is time to decide, it arrives together and with its origin.
Security is not a feature.
It is the architecture.
Bring your own cloud: the platform is hosted on your cloud infrastructure.
Every inference runs in your Private Cloud: the prompt, the context and the answer never leave your infrastructure.
We inherit the permissions that already exist in your systems: they apply to people and to AI agents, on every query.
Every query and every agent action, logged in your account: who, what, when.
We work in three stages.
Everything starts with a 2-week Assessment. We map the operation, prioritize the first use case and define scope, success criteria and an execution plan.
We diagnose and fill the gaps in your data foundation.
A recipe validated in brokerages: capture → cleaning and cataloging → unified base, with automations and alerts.
Map of the sources, prioritized gaps, a unified and living base.
We deploy the AI Infrastructure.
Your data gains business context and graph structure: the most efficient way for an AI to query and operate. Controlled access and real-time events.
Knowledge graph of the operation, MCP Server with permissions, Event Bus with alerts.
We build AI applications that deliver value from day zero.
Agent, dashboard or predictive model: the first project leaves the Assessment already designed to generate value.
An application in production, measurable, on the foundation from steps 1 and 2.
Book 30 minutes
with the people who build.
A founder joins the call. You bring a real problem from your operation and leave with an honest read on where AI creates value. If we are not the right fit, we say that too.
The calendar opens right away. You pick the time and get the invite by email.
If you would rather write before booking, the email is contato@entitylabs.com.br.
What fits in 30 minutes
- You tell us which decision is stuck in your operation today.
- We show the layer applied to a case like yours.
- We tell you what can be done with the data you already have. And what cannot.
- If it makes sense, you leave with a written scope — timeline and success criteria included.
