What you need to know before you start.

To take AI out of improvisation and put it inside your operation. Instead of people copying and pasting answers from a chat, we build agents that run repetitive tasks and whole flows inside your systems, with human approval where it matters and a record of every step. The team stops spending time on busywork and focuses on what needs judgment.

A chat answers when someone asks, outside your systems and with no trail. An agent works inside the process: it reads the right sources, runs the task, asks for approval when the risk requires it, and records what it did. ChatGPT and Copilot raise one person's productivity at a time. An agent standardizes and automates the whole team's process, integrated with your data and with an audit trail.

For any team that loses hours on repetitive tasks and needs clear, documented, and auditable processes. It makes even more sense where the wrong move is expensive and auditability is not optional: corporate legal, financial services, regulated operations, and back office that depends on many systems to decide.

Brazilian companies in finance, tech and legal with critical processes and sensitive data. Typically mid-sized banks, funds, corporate law firms and in-house legal teams, scale-ups with large proprietary data, and regulated operations (BCB, SUSEP, ANVISA, LGPD). If your company has legacy systems, meaningful proprietary data and expensive people losing time on repetitive tasks, it's probably worth a conversation.

Forward is the service: our engineers join your team to get AI live in four weeks, with contract priced by deliverable. It's the entry point for any sector. Solon is the SaaS: the same Forward playbook industrialized for Brazilian corporate legal (M&A, contracts, VDD). Agents is the technical orchestration foundation behind Forward and Solon. You engage Forward or Solon. Agents is the how, not the what.

Yes. Training the team is part of the method, not a separate sale. Our engineers work side by side with your people, document the system and do the handoff until whoever will operate knows how to operate. We leave when your team is running with autonomy, not when the software goes live.

The repetitive work (checking, classifying, filling in, reconciling, summarizing) gets done or prepared by the agents. People move from manual execution to validation and decision. And unlike a chat, every action stays documented and traceable, so the process no longer lives only in someone's head.

The repetitive, high-volume ones that eat hours and create errors today: reconciliations, triage and classification, document and contract checking, standard replies, report generation, and compliance checks. The agent handles the automatic part and the team keeps what needs judgment, relationships, and decisions.

No. An auditable process matters whenever the process matters. Auditable means knowing what was done, by whom (person or agent), based on which source, and when. That reduces rework, error, and dependence on specific people. It is not only a compliance requirement.

We do not publish a price table. The amount is defined in the scope and contract based on the deployment environment, data volume and sensitivity, integrations, number of flows, and governance level. The proposal or Order Form records deliverables, assumptions, and amounts.

It starts with a concrete pain, not a giant project. You point to a repetitive task that eats your team's time, and we put that first flow on autopilot, with a clear owner and a clear result target. No big bang: we prove the value on one case before scaling.

Four weeks is the reference cycle for putting a first well-bounded flow into production. It is not a universal SLA. The contracted timeline depends on integrations, internal approvals, data quality, and governance; availability and support are defined in the contract or Order Form. For commercial inquiries, the team usually replies the same business day.

In hours the team stops spending on busywork and starts investing in what moves the business. We look at concrete things: less rework, faster response, more volume without growing the team, and decisions you can audit. We don't promise a generic percentage; the numbers are agreed with you before we start.

No. You don't replace anything and you don't need to build an AI team. We bring the engineering, connect to the systems you already use, and operate the first flow. It works best when there is an owner on the business side to say what matters.

We use Anthropic, OpenAI, and private models according to the flow's technical needs. The choice considers data sensitivity, error tolerance, cost, and deployment environment. External providers are used only through enterprise APIs and under contracts with retention and processing limits; Customer Content is not used to train generic models.

Customer Content is isolated by tenant and processed only to perform the contracted functions. We can operate in the customer's own environment, a dedicated cloud, or controlled infrastructure; external providers are used only when the contract provides for them, with limits for retention, access, and personal-data handling.

Our engineers work inside your team's technical context to turn a use case into running software. We don't deliver a diagnosis in slides: we put a controlled flow live, with monitoring, documentation and agreed acceptance criteria.

The platform brings together the connection to your data, the organization of that data, agent execution, permission control, audit logs, and rollout options. The makeup changes by environment, but the base of governance and integration is the same across all products.

The solution runs in a dedicated cloud, the customer's own environment (on-premises), or a hybrid model. The selected architecture is recorded at the start of the project and considers security, latency, cost, regulatory requirements, and operating capacity.

There is no zero-error guarantee in AI. The system finds evidence, cites sources when the case requires, records steps, and keeps human approval on sensitive actions. CatechLabs makes risk visible, controllable, and auditable, but professional decisions remain the customer's responsibility.

We integrate databases, document repositories, internal APIs, management systems, and authorized public sources. An integration is feasible when access is permitted, security requirements are met, and sufficient technical documentation exists; scope and acceptance criteria are defined in the project.

Agents do not need full autonomy. In sensitive processes, they can just prepare the analysis, suggest an action, or fill in a step for human approval. The client defines where automation can go on its own and where a person needs to authorize.

Controls include restricted access, tenant segregation, AES-256 encryption at rest, TLS 1.3 in transit, MFA for operators, and action logging. CatechLabs processes Customer Content under the contract and LGPD; the customer remains responsible for the legal bases and sector compliance of its own use.

Every platform creates some level of dependence. Our way of reducing that risk is to work with documented integrations, APIs, explicit architecture, and clarity about the client's data, permissions, and artifacts. This point should be addressed in the contract and the technical design.