This page describes Femto Software Factory C.A.'s typical approach to integrating AI solutions into insurer and broker environments. The final design always depends on each customer's regulatory context, existing architecture, and internal policies.
Solutions attach to the customer ecosystem via REST or gRPC APIs, event queues, and batch connectors when the core cannot expose synchronous interfaces.
Flows are modeled as workflows with clear stages: intake, validation, assisted decision, downstream recording, and internal notifications.
We do not promise an overnight replacement of policy admin or legacy cores; we prioritize controlled coexistence and gradual migration when the customer roadmap allows it.
An orchestration layer coordinates calls to models, rules engines, and master data services.
Versioned contracts and segregated environments reduce the risk of uncoordinated changes.
Idempotent patterns for financial or reserving operations to avoid duplication on retries.
Logging of relevant inputs (without storing unnecessary sensitive data), intermediate decisions, and executed actions.
Correlation identifiers across channels to reconstruct a claim or underwriting request journey.
Retention aligned with the customer, with export paths for internal audit teams.
Role matrices aligned to business and IT teams, with segregation of duties for critical approvals.
Access and configuration change logs for decisioning components.
Periodic access policy reviews when new datasets or integrations are introduced.
Minimization: only fields required for the task enter prompts or automated summaries.
Masking or tokenization when the environment supports it; encryption in transit as a baseline practice.
Approval flows for personal data use in training or fine-tuning, always subject to customer policy.
Explicit separation between versioned business rules, models, and the layer that executes actions (payments, reserves, communications).
Human-in-the-loop thresholds when financial or reputational exposure requires it.
Regression testing on representative cases before promoting changes to production.
Customer public cloud, dedicated infrastructure, or hybrid deployments based on data residency and connectivity.
Isolated VPC execution when security policy requires it.
Final topology is documented jointly with the customer's infrastructure and compliance teams.
Volume, latency, and error metrics per integration; bounded alerting to avoid operational fatigue.
Distributed tracing on critical flows to diagnose bottlenecks between AI and legacy systems.
Shared dashboards with business teams to monitor decision quality and SLAs.
Secret management outside source code; rotation aligned to customer policy.
Dependency review and planned upgrades to reduce attack surface.
Access controls for training or fine-tuning datasets when applicable.
We do not list generic certifications here; credentials depend on each project and vendor context.
