Document processing and customer operations AI for ACMES, a Mexican insurance broker.
ACMES operates across complex commercial insurance lines and customer-facing service channels. Volume is high. Documents are dense, unstructured, and policy-specific. Customer operations scale with book size, not with headcount availability. Every workflow touches sensitive data, and every AI decision must be defensible under CNSF oversight and internal audit standards.
Before Saptiva AI, ACMES — like most regulated enterprises in the region — had evaluated hyperscaler-based AI solutions and found the same gap: the technology could do the work, but the governance framework could not authorize it to run.
ACMES approached Saptiva AI with two concrete, measurable bottlenecks and one non-negotiable condition.
Document processing at volume. Policies, endorsements, and claims documents were being manually reviewed and keyed into downstream systems. The work was accurate, but slow. Each additional line of business meant more headcount.
Customer operations at scale. First-tier inquiries — policy questions, coverage clarifications, claims status — required a growing team of human agents. Response times lengthened as book size grew.
The condition: AI had to run under ACMES's compliance framework, not despite it. Data could not leave approved boundaries. Every decision had to be auditable. Any vendor relationship had to satisfy internal risk review before a single document passed through the system.
The deployment is deliberately small — two applications, one orchestration engine, one embedded Forward Deployed Engineer. Not a platform rollout. Not a multi-year transformation. Two workflows, in production, fast.
Each workload enters through a Saptiva Studio application carrying its metadata. FrIdA evaluates the ACMES policy against that metadata — residency, data class, model eligibility, audit requirements — then dispatches to the approved compute. Every step is logged.
The policy is authored in ACMES's own repository, reviewed by ACMES compliance, versioned like any production configuration. Non-compliant routes don't run. Compliant ones write an auditable record of the decision. This is the difference between "AI that passed review" and "AI that is continuously reviewable."
A Saptiva AI Forward Deployed Engineer landed on day one inside the ACMES team — physically, operationally, and organizationally. Not on a call. Not on a ticket queue. In the room.
Discovery, scoping, environment provisioning, the first workflow running end-to-end, compliance review, and production cutover all took place inside fourteen days against a signed SLA. The FDE stayed for a capability-transfer period after cutover. They left when the ACMES team could operate, extend, and audit the applications without us. Not before.
Consistent with ACMES's disclosure standard, outcomes are described qualitatively. Specific metrics, volumes, and internal KPIs are not disclosed on this page — they remain ACMES's to share on their terms.
Quote from ACMES leadership — to be added following review and disclosure approval.
Customer quotes are published only with explicit written approval. In the interim, this page carries full architectural and delivery detail — which is what serious enterprise buyers read first anyway.
Several categories of information are deliberately absent from this case study. They are not missing by oversight. They are withheld out of respect for ACMES's confidentiality obligations to their own clients, and to the regulators who oversee them.
Sophisticated insurance and financial services buyers recognize this posture as the same standard they hold their own vendors to. A case study that claimed to disclose every detail of a major regulated customer would be less credible, not more.
If you operate in insurance, banking, or any regulated industry in Latin America — and you're accountable for a workflow that needs production AI under a real compliance framework — a Forward Deployed Engineer will respond within 48 hours.
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