One layer between every AI application and every compute environment. frIdA routes each workload by residency, latency, cost and policy. It enforces compliance at execution and logs every decision for audit.
Cloud, hybrid, on-premise or air-gapped, under one policy framework. Forged in LatAm, not limited to it.
We don't watch the model and write a report afterward. frIdA sits in the path. Every workload gets a verdict, a cited rule, and a sealed record before it ever reaches production. Here is a single credit request, the way the platform sees it.
workload: credit.adverse_action_notice
tenant: BANK_MX
contains: applicant PII · decision rationale
residency: MX.sovereign
frameworks: CNBV · CONDUSEF
pii_scan: enabled
route: SOVEREIGN · MX
output: es-MX · PII masked · disclosures ✓
record: decision_id · timestamp · policy_v · route · model
export: regulator-ready
Where your organization builds, operates and governs all of it. Savant, RAGster and Dictaminador are how it ships. In production in weeks.
Decides where every workload runs and enforces policy at execution. Nobody sits at it. It runs whether you are watching or not.
Where it runs: On-prem · Public cloud · Air-gapped
What it routes: Any model · KAL · sovereign
Deployment modes and the security posture go deeper on their own pages: Deploy Anywhere → · Security & Compliance →. Both are what frIdA does, not separate products.
Every workload is scored against the same four signals before it executes. The route is not a default someone set once. It is recomputed per request, and the reasoning is logged.
Every engagement ships with a structured enablement program. Run inside your organization. Your team gets autonomous on what we deployed.
Every module maps to a specific capability you deployed. Your team learns to operate, extend, and audit the applications without us. Autonomy is the exit criterion.
New module each cycle. Enablement closes in weeks, not the months enterprise software procurement takes. Each module opens the next use case.
If the only reason you still need Saptiva AI is that your team can't operate the platform, we haven't delivered. Enablement is how dependency becomes capability.
ACMES's team audits their own policy files. Ibero's faculty extends their own Studio apps. The exit criterion isn't training completed. It's capability owned.
Every architectural decision traces back to one of these. Our customers cannot afford for them to be otherwise.
Enforced at execution time by frIdA, not by contract. A workload that violates your residency rule doesn't route. The platform refuses to execute. Residency is architectural, not administrative.
You choose the model, the cloud, the deployment mode. Swap the LLM and frIdA routes around it. Change the cloud and the same policy applies. If we can only keep you by trapping you, we haven't earned the relationship.
Every routing decision, every policy evaluation, every execution produces an immutable record: readable, exportable, regulator-ready. If something goes wrong, you can prove what the platform did and why.
Production from day one. Our embedded engineers land inside your team, ship the first use case in two weeks, and stay until it runs. We optimize for what still runs eighteen months later.
In production at Rappi, Banco Invex, MultiMoney, Universidad Iberoamericana, and ACMES. KAL, Mexico's national LLM, runs on the platform under Mexican jurisdiction.
If you're evaluating AI infrastructure for a regulated institution and want to go below the overview, our engineering team responds within 48 hours. Not a sales sequence.