Operational intelligence to get the most out of AI

Turn AI into work that isgoverned, measurableand scalable.

h|ve connects context, business rules, people, agents and evidence to put AI into operation without losing control.

The company

What is h|ve (Hybrid Velocity)?

Hybrid Velocity, h|ve, is a Brazilian Operational Intelligence startup built on agentic AI for software development, created to help companies get the most out of AI. We connect the decisions of your business areas with AI. That is the only way to reach the full potential.

How it works

h|ve installs a common layer between people, agents and tools, unifying context, rules and the record of every decision. That is how your clients put AI into operation with governance, control and measurable results.

What h|ve is not

h|ve is not a code generator and it is not an AI model. It is the Operational Intelligence layer that coordinates, governs and makes auditable the work AI carries out across the whole business.

Where h|ve works

From ambition to results, in four decisions.

More capable models, agents in trial and tools scattered across the business have already arrived. h|ve decides with the company where to invest, with what context, who signs and how to prove it.

AI ambition

Potential on the table

More capable models
Agents in experimentation
Tools scattered across the business

What the market says

90%
of developers already use AI day to day
DORA · Google Cloud, 2025
29%
trust the accuracy of what AI delivers
Stack Overflow Developer Survey, 2025
40%
of agentic AI projects will be cancelled by 2027
Gartner, 2025

Value still uncertain

Operational intelligence

h|ve connects business priority to the way people and agents work.

Priority: The problem worth investing in
Context: The knowledge that guides execution
Authority: The decisions that stay human
Evidence: What proves cost and result

Business result

More delivery capacity
AI applied to the work that moves the business
Less value lost
Less rework, scatter and decisions without context
Risk and cost under control
Authority, policy and spend visible during execution
A return you can prove
Evidence to decide where to expand or stop

Where the value shows

More finished deliveries, less rework and evidence to decide where to scale.

Portfolio

One product installs the capability. Four services put it to work.

The product stays in the operation. The services redesign the work and speed up adoption. The business keeps the result.

Product

h|ve Platform

A common layer between people and the technology they use. Context, authority, policy and evidence travel with every execution, whatever the agent, the model or the tool.

Map
Flow
Guard
Judge
Ledger

Explore the product

Services

Four services

Priority, rollout, enablement and evolution, hired according to the company challenge.

Scan & Go
AI Value Discovery
Governed Method Packs
AI Enablement Labs

Explore the services

What changes in the operation

People and agents
Human capacity extended, in the same workflow
Redesigned processes
End-to-end flows, from request to delivery
Decisions with context
Knowledge arrives at the moment of action
Governance in execution
Authority, cost and risk visible while the work happens

IT enables the base. Leadership and business areas turn the capability into results.

Comparison

Who delivers the full stack of AI governance?

The question is not who competes with one module, but who offers, under one roof, the model of the company, the human+AI working method, governance with real enforcement and proof in production. h|ve is the only column that fills the substance.

Who delivers the full stack of AI governance?
Capability	h\|ve	ChatGPTEnterprise	ClaudeEnterprise	GitLab	GitHub/MSFT	Factory	Palantir	Camunda	Cursor/Cognition	LangSmith	LangChain
Formal organizational model (own spec)
Documented human + agent working method
Orchestration of people and agents on the same task
Development governance product with enforcement
Enforcement inside the agent editor (Guard)
Business rule and technical rule in the same gate
Organizational memory and knowledge (distillation)
Human authority gates
Spec to diff, requirement to PR
Append-only trail per delivery
AI cost per card and per project
BYOK: the key and the spend belong to the customer
Model and stack portability
Minimisation: no indexing of your codebase
LGPD, with data residency in Brazil
Public pricing and SME entry point

does it fullydoes almost alldoes it in parttouches on itdoes not do it

Scale of 0 to 4, based only on each vendor official public pages; the h|ve column comes from our own repositories. «Does not» does not necessarily mean it does not exist, only that it was not evidenced. GitHub/MSFT and Cursor/Cognition stand for combined alternatives, and the score uses the strongest public capability of the set. ChatGPT Enterprise and Claude Enterprise are enterprise assistant suites, not engineering governance products: the low reading on the enforcement and per-delivery trail rows describes the declared scope of each one, not their quality. On the residency and portability rows, products that install in the customer cloud are read by what the customer gets to choose, not by where the vendor is based.

Proof

The first operation governed by h|ve is h|ve itself.

We build the platform with the platform. Specification, gate, review and decision stay on record, with hits, blocks and rework in plain view.

Our own operation, not a client case

The numbers come from the h|ve repository and show operating discipline. They are not presented as a client result.

Compliance screen: 115,200 combinations compared, zero divergences and 28 of 28 edges covered

SPECs: 605; written before the code; 307 already closed as DONE
pull requests: 364; 357 merged through the same human gate
tests: 3,636; across 300 files, with a CI gate on every PR
conformance cases: 115,200; compared field by field across two independent implementations of the same decision; zero divergences

Inventory of the h|ve Flow repository, August 26, 2026.

See the full proof

Integrations