About

An agentic AI company for Brazil.

AInda designs, builds, operates, and answers for teams of autonomous agents that do real work within Brazilian rules, with cost and outcome auditable per unit of work.

We design
We build
We operate
We answer

Four actions define our delivery. Each is rarer in the market than the last, and the fourth is practically nonexistent in Brazil. It is also the most valuable: the one who answers for the outcome is the one who provides the security to scale.

What we are not

We are not a consultancy, a SaaS company, or a platform: we are all three in one contract. Strategy, to choose which process to automate based on value; Engineering, to deploy inside your operation; and Platform, with AInda Agentes (our reference harness) and AInda Controle (our control layer) — one partner, one contract. You don't buy a pilot and get left alone with it. For mid-sized and large regulated companies, three differences: governance as the backbone, not a feature; portability and open formats; sovereignty and provenance.

A SaaS company: We don't sell licenses: we deliver the process up and running, and our software is what lets us run it and answer for it.
A slide-deck consultancy: AInda's work is getting AI agents into production under audited operations — not delivering reports.
A software house with an AI pitch: Our delivery units are not hours, sprints, or features: they are business processes executed by AI agents, with audit trails.
A platform reseller: Our value proposition is not tied to selling third-party products. We act as implementation and technology partners.
An automation agency: Automation executes known, structured, and deterministic rules; AI agents exercise judgment under uncertainty and on unstructured information. They are different engineering and risk disciplines.
A vendor of “an AI for everything”: Choices of AI models, tools, and harnesses are architecture decisions per task, with explicit cost and risk.

Our category: agentic AI

Agentic AI describes a new model of computational unit: from “transaction executed through instructions” to “task executed with judgment, under delegated authority, with evidence verification.” This changes four dimensions.

Axis	From	To
Infrastructure	Persistent applications	Ephemeral containers
Costs	Cost per seat	Cost per task
Security	User identity	Agent identity and permission
Management	Assigning tasks	Delegating and revoking authority

A company that treats this as “one more AI project” loses on all four dimensions. One that understands all four gains execution capacity that grows without a proportional increase in headcount, at a known cost per task. That is why we do not treat agentic AI as an evolution of chatbots or copilots. The question is not who does the work, but which work creates value.

Our assets

Our advantage comes from four combined knowledge assets.

01
Harness engineering, with a reference harness
We create and operate our own harness, and maintain in parallel a declared multi-harness policy. We choose harnesses based on the company's context and the specific needs of the project.
02
Vertical skill library, with Brazilian rules
Declarative skills for Brazilian business processes, including regulated ones, in open and portable format, each with a set of assessments, a traceability map of the applicable standard, and an audit trail model. The evidence pack is the product; the skill is the means. Each operation feeds the library, and the library shortens the next operation.
03
Dynamic AI model intelligence
Daily updated knowledge about AI models in the following dimensions: output quality, reasoning, speed, context window and discipline, multimodality, privacy, security, sovereignty and jurisdiction, availability and continuity, cost per task, and total cost of operation.
04
Enterprise experience of those who have already operated at scale with AI
Leadership with more than twenty years of international practice, more than ten years of pioneering work in AI, deep experience in regulated verticals, implementation and certification of regulatory frameworks, and management of large multinational engineering teams.

The combined value chain

Enterprise experience defines which processes are worth automating with AI agents and how to do it

Intelligence about AI models defines the architecture that delivers agents with the best cost-performance

Harness engineering delivers projects with speed and produces audit evidence by design

Skill library transforms learning into a reusable and continuously optimized asset

Each operation feeds the library, and the library shortens the next operation.

↺ The library feeds back into the next operation.

The result is the promise we make to clients: AI agents capable of executing complex tasks and answering for them, validated by human responsible parties and subject to audit. A chain in which each process gets better and cheaper with each cycle.

Leadership

Marcelo Negrini

Founder

More than twenty years of international experience in digital transformation, including Microsoft, IBM, and eBay. Work in finance, education, and healthcare — highly regulated verticals in Brazil and worldwide — implementation and certification against national and international regulatory frameworks, and management of large multinational software engineering teams.

This asset prevents AInda from being confused with a startup that just delivers technology. Putting AI agents to work on real processes requires four conversations an inexperienced team cannot lead:

What authority to delegate, and up to what value (approval limit)
What happens when an AI agent makes a mistake, and who answers and handles the error
How internal control and audit will accept the evidence trail
How the costs are allocated across different budget lines