Question 1

What is a corporate AI governance layer?

Accepted Answer

It is a software layer that sits between the company's tools and the LLM providers and applies the organization's rules to every request: it blocks or masks sensitive data, controls spend through budgets, restricts which models can be used, isolates access per department and records everything in an audit trail. HorseLabs implements this layer across 5 fronts — data, cost, models, access and connectivity — behind a single key, for any provider.

Question 2

How does HorseLabs stop sensitive data from reaching the LLM provider?

Accepted Answer

Every prompt passes through the gateway before the model. The DLP Shield inspects the content pre-call: deterministic rules detect bank data — credit-card numbers (Luhn) —, CPF, e-mails and credentials; an NLP layer detects person names and PII that rules can't reach. Depending on the team's policy, the data is masked or the request is blocked before it leaves — and the violation is recorded in the trail, with the data already masked.

Question 3

Does using HorseLabs solve my LGPD compliance?

Accepted Answer

Not on its own — and be wary of anyone who promises that. Compliance involves process, legal basis, contracts and people; no tool "solves" it. What HorseLabs delivers are the technical controls that sustain your posture: blocking and masking of personal data before the provider, a violation audit trail and access isolation. And let's say it plainly: every relevant LLM provider is foreign, so international data transfer is intrinsic to using AI. We give you control, reduction and proof over what leaves — not the illusion that nothing does.

Question 4

Do I need to replace the tools my team already uses?

Accepted Answer

No. The layer speaks the OpenAI-compatible standard: anything that already works with that standard — IDEs, agents, scripts, internal tools — starts pointing at the gateway by swapping the base_url and using the team's virtual key. The employee keeps their workflow; the company gains control.

Question 5

What happens if the data detector goes down?

Accepted Answer

Fail-closed. When the team's policy is set to block and the detector becomes unavailable, the layer stops the request instead of letting it through. Protecting the data is the architectural default — not a setting someone forgets to turn on.

Question 6

How does AI cost control work?

Accepted Answer

Each team uses a virtual key with its own budget. Spend shows up in real time per user, key, team and cost center. When consumption crosses the threshold you set, the layer fires an alert (webhook into your workflow); when it overruns, it cuts off. And every request is logged: who, which model, how many tokens, how much it cost.

Question 7

Which providers and models are supported?

Accepted Answer

Claude (Anthropic), GPT (OpenAI), Gemini (Google) and Grok (xAI), behind the same key and the same API standard. The catalog is fed by each provider's live models and governed by an allowlist: everything starts off, and only what an administrator approves goes into use. A non-approved model gets a 403.

Question 8

How is access isolated between departments and companies?

Accepted Answer

Each organization and each department lives in its own tenant, with strictly scoped roles (operator, admin, member). Provider credentials live in a vault and never reach the end user. Sensitive actions require a second factor, and every access lands in the audit trail.

Question 9

How much does it cost?

Accepted Answer

It depends on scope — operation size, number of departments/tenants and request volume. The investment structure is at horse-labs.dev/pricing; scope and metric are defined before we start, with no surprises.

Question 10

How do I start?

Accepted Answer

By requesting access through the form on this page — a corporate e-mail and your team size are enough. We are in a validation phase with selected companies: the founder replies within 1 business day.

Investment structure

Self-host (your infra · roadmap)

Managed governance