Generative AI services

Teams that implement structured AI guardrails accelerate innovation cycles, cut compliance exceptions significantly, and maintain stakeholder trust as generative AI scales into core workflows.

Organizations that implement policy-as-code enforce governance rules automatically, reduce compliance exceptions dramatically, and scale AI systems with confidence while avoiding manual bottlenecks.

Pharma operations teams that use agent-as-analyst workflows synthesize complex data sources faster, cut manual review time significantly, and improve signal detection and submission quality.

In the quiet engine rooms of regulated operations—pharmacovigilance, finance, compliance, and beyond—back-office decisions used to live in black-box logs: cryptic timestamps, error codes, and terse notes that only the system architect could love.

Underwriters who deploy AI assist agents reduce application review time significantly, issue straight-through decisions on more risks, and achieve higher consistency across portfolios.

Carriers that deploy write-back workflows for legal memos cut documentation cycle times dramatically, free senior adjusters for high-value negotiations, and strengthen defensibility against bad-faith and regulatory challenges.

Every COO knows the picture: tickets pile up after quarter-end, exception cases clog inboxes, PDFs wander between teams, and simple questions escalate because no one has complete context. Meanwhile, customers and internal stakeholders expect hours, not weeks. The risk isn’t just overtime spend—it’s opportunity cost, revenue leakage, compliance fatigue, and talent attrition.

Storms hit, FNOL spikes, and your teams drown in photos, PDFs, emails and estimates. Each missing detail adds a touch, each back-and-forth chips away at satisfaction. Multi-Modal AI changes that slope.

Disputes drag when evidence is scattered, policies are hard to parse, and customers must repeat themselves. Multi-modal AI fixes the experience by turning documents + chat + policy into a single, auditable flow. The assistant ingests photos, PDFs, emails, and call recordings; it retrieves the relevant clause and procedures; and it guides both the customer and adjuster to a clear, policy-aligned outcome—often without escalation. Therefore, handle time drops, recontacts fall, and CSAT improves because the system shows its sources.

Clinicians spend too much time wrestling with documentation, prior authorization, and coding queries while patients wait. Leaders want to reclaim hours without creating quality or compliance gaps. Retrieval-augmented generation (RAG) offers a pragmatic path: pair a capable model with an auditable retrieval layer that only pulls from approved sources—patient charts, order sets, policies, and payer rules—then require the system to show its sources inside every draft. Therefore, documentation gets faster, while risk and rework go down.