AGENTIC AI THAT WORKS AS EXPECTED
AInda builds and runs AI agent teams for your organization, using the platforms you already have, and leveraging your investments. Coordinated AI agents handle critical processes 24/7, reduce bottlenecks, and increase your capacity, all while keeping transparent costs, requiring human approval, and providing audit-ready evidence.
The thesis
Companies have already tested AI, and in Brazil 76% keep AI agents in production. The hard part is getting out of the pilot and staying in production: less than 10% of AI-deployed use cases move beyond the pilot stage, and 80% of Brazilian organizations have already paused or rolled back an AI agent deployment.
The risk is not in the agent: it lies in the gap between written policy and the control that enforces it at runtime. AInda closes that governance gap and takes agents from pilots to production, creating value, with owner, authority, cost, and records from day one.
Those who measure are the ones who scale. See how.Why it stalls
01The task wasn't for an agent
Not everything calls for an AI agent
02Context and integration are missing
The agent can't reach the process's systems and rules
03Nobody defined authority or an owner
So Risk and Legal hold it back, and rightly so
04The cost is unpredictable
Nobody knows what each task costs
Operate within defined authority
What the agent may do, up to what limit, and who answers for it.
Measure what happened
Accuracy, human intervention, and cost per task.
Revalidate every quarter
An identified person decides, with the track record on the table.
Revise the authority
Raise, keep, or reduce.
↺ The cycle starts again, with the revised authority.
The payback
Managed AI agents bring clear advantages to your business, with the potential for exponential growth — while respecting your rules, market guidelines, regulatory frameworks, and legislation.
The process that limits growth today for lack of people gains elastic capacity, with humans concentrated on the exceptions that require judgment.
Not the marginal saving of a copilot suggesting text. The structural economics of a unit of work that comes to cost cents.
At Brazil's largest private bank, scale came with fewer incidents, not more. Autonomy and control push in the same direction.
When every unit of work carries cost, accuracy rate, and intervention rate, deciding what to scale stops being a bet and becomes reading numbers.
Each managed AI agent becomes the company's intellectual property in an open format: portable across models, harnesses, and clouds, and a base for other agents.
How we work
Know what already exists in AI and decide what is worth doing
Real agents in production, at real volumes
Operation is the center, with a behavior SLA
AInda Controle
Our control layer: governance, observability, FinOps, and evidence — as a continuous service.
Entry point
Recurring production is the destination. Governance is the critical path.
How we charge
The units of work are measurable, concrete, and auditable. Each one carries four numbers that are captured and published.
The four numbers published per unit of work
Unit of work
Invoice or entry reconciled
What it cost
How well it worked
Measured and published, per unit.
We support a variety of harnesses and execution platforms. Our policy is clear: we choose harnesses together with our customers, based on use-case characteristics, regulatory requirements, and existing vendors and platforms, while keeping agents, skills, integrations, and intellectual property in open, portable formats.
Harnesses and platforms we support
We work with leading global and local frontier AI labs and support both proprietary and open-weight AI models, right-sized for each task, hosted across public and private clouds, data centers, and on-premises environments. Multi-platform, multi-model policy is a core element of our customer value proposition.
AI models we support
We support virtually all enterprise systems through MCP servers, APIs, and services, including ERPs, core banking, claims systems, tax systems, transactional systems, productivity tools, and CRMs, as well as multiple data sources, to accelerate integration and context readiness. We work with cybersecurity partners on the infrastructure layers we don't cover directly — keeping agent red teaming with us, which we consider a differentiator.
Systems and platforms we work with
We work with trade associations, employer federations, technology hubs, Brazil's industry-funded training network (Sistema S), funding agencies, think tanks, and universities to combine capabilities and generate qualified demand. In addition, we use these partnerships to identify trends, develop vertical agents and skills, and co-finance research and development.
Institutions in the ecosystem we operate in
Method
01Know your AI and agent maturityDays 1–21
02Define process boundariesDays 15–45
03Agentic Sprint Zero in productionDays 30–75
04Continuous operation stateDays 60–90
Days 1–21
Full census, a human owner per agent, sensitive-data exposure map, and the list of what to switch off, formalize, evolve, and scale.
Gate · Committee approves the target processes for Agentic Sprint Zero.
Days 15–45
Authority and Delegation Matrix signed. Process baselines measured and accepted in writing, with metrics and boundaries.
Gate · Framing closed, with matrix and baselines.
Days 30–75
Agentic Sprint Zero with deliberately narrow scope and real volume, human owners active, and telemetry collecting behaviors and costs from day one.
Gate · Production entry, verified against the four criteria.
Days 60–90
Thirty measured days of operation, runbooks delivered, SLAs agreed, and the first evidence packs reviewed with internal audit.
Gate · Steady state. AgentOps can be contracted.
A managed team of AI agents, with clearly named human owners, engaged in active processes under delegated authority, handling actual operational volumes. Costs per unit of work are known and compared to previous records. An evidence pack was reviewed by internal audit. In addition, an informed decision on what to scale was made, based on solid numbers, not just expectations.
Us included. They were written to be answered.
For those who decide
An agentic AI initiative is not decided by a single person. When the operation is well planned, the following six roles act as a committee, sponsor the initiative, and share the benefits.
You will be able to say yes to the business faster: management, security, consistency, and portability are set out in the contract, not just in presentations.Learn more
Each agent's decision retains links to data sources, and telemetry and evidence packs are generated during the operation rather than reconstructed afterward.Learn more
We establish a baseline for operations before making promises about results, as our SLA focuses on process performance, not server availability.Learn more
Cost per task, agent, and process, defined and compared to the costs without agents. Payment per unit of work, when there is a reliable baseline and predictable conditions.Learn more
Agents' autonomy is established by positive enumeration: any action not explicitly authorized is prohibited. Traceability and evidence are created at an audit-quality level.Learn more
Your AI pilots and agents that deliver value will not be shut down. They will become official, improved, turbocharged, integrated, measured, and scaled — forming the official AI agent team for your area.Learn more
Industries and verticals
AI agents excel in critical, document-heavy, high-volume decision-making processes, including regulated ones, where evidence is mandatory and the cost per unit of work (money, time, quality) is controlled.
We do not sell absence of risk. We sell risk that is known, bounded, measured, and owned.
Assess your company's stage of AI use, including team readiness, tools used, pilots underway or stalled, and existing agents. For each agent, we identify the owner, the estimated cost, and a decision: switch off, formalize, evolve, or scale.
Recurring production is the destination. Governance is the critical path.