From pilot to production — what the work looks like.

Challenge

Finance keyed vendor invoices by hand, reconciled them against purchase orders and contracts, then manually produced customer invoices — slow, error-prone, and hard to audit at month-end.

Approach

An agentic intake pipeline extracts line items from vendor invoices, matches them to POs and contract terms, and drafts the corresponding customer invoices — with the AI proposing values, never finalizing them.

Controls that made it safe

• Deterministic validation (format, PO existence, totals, contract rules) before any posting
• Provenance on every extracted field — source document and confidence
• Human review for high-value or low-confidence items
• Immutable audit trail of every posted entry

Outcome

Challenge

A core operational workflow spanned several systems and required staff to copy data between them, chase approvals, and handle exceptions by hand — capacity-limited and inconsistent.

Approach

The end-to-end workflow is mapped and augmented with agents that handle routine activities—reading inputs, updating records, and routing approvals—while exceptions are escalated to a human review queue.

Controls that made it safe

• Risk-tiered automation: routine auto-handled, consequential steps gated
• Every action scoped to verified identity and least privilege
• Reversibility — any step can be paused and rolled back
• Full decision log for traceability

Outcome

Challenge

Onboarding a new customer or service meant coordinating setup across CRM, billing, and operational systems — days of manual steps, with frequent rework when something was misconfigured.

Approach

Provisioning is orchestrated as a guided, largely automated workflow in which agents assemble configurations, validate them against business rules, and activate them across systems once all checks have passed.

Controls that made it safe

• Validation gate before activation — no half-provisioned states
• Tenant-scoped data access throughout
• Human approval for non-standard configurations
• Audit trail from request to activation

Outcome

Challenge

Specialists spent hours reading dense regulatory, and operational documents to determine what applied and what action to take — a bottleneck where mistakes carry real compliance risk.

Approach

An LLM-assisted layer interprets the source text and proposes the applicable rules and required actions, surfaced to specialists with citations — accelerating interpretation without removing human judgment.

Controls that made it safe

• Provenance over confidence — every interpretation cites its source passage
• Deterministic checks against authoritative reference data
• Human-in-the-loop for any consequential determination
• Immutable record of what was interpreted and decided

Outcome

Challenge

Regulated organizations often rely on periodic, manual evidence gathering and spreadsheet-based controls, making audits disruptive, costly, and vulnerable to gaps in documentation.

Approach

AI agents continuously collect control evidence from operational systems, map it to regulatory requirements, and surface exceptions with clear explanations and remediation recommendations. The AI assembles audit packages and highlights risk, while accountable owners retain approval authority.

Controls that made it safe

• Traceable evidence linking every finding to authoritative source records
• Deterministic policy checks and versioned control definitions
• Human review and sign-off for exceptions, attestations, and regulatory submissions
• Immutable audit logs capturing evidence collection, decisions, and corrective actions

Outcome

Challenge

A legacy estate — monolithic, hard to change, expensive to run — needed to become cloud-native and AI-ready, but a multi-year rewrite is risky and slow with traditional methods.

Approach

A pragmatic, phased migration to cloud-native microservices, accelerated with AI-assisted development and quality gates — keeping the legacy system stable in a transition-state architecture while new modules ship.

Controls that made it safe

• Quality gates (tests, scans) on every AI-assisted change
• Strangler-fig transition — legacy stays stable while functionality moves
• Observability and rollback at each cutover
• Measurable coverage and defect tracking

Outcome

Challenge

Years of customizations, undocumented integrations, and embedded business rules made ERP modernization risky, with stakeholders concerned about disrupting critical operations or losing institutional knowledge.

Approach

Specialized agents analyze source code, configurations, and process artifacts to reconstruct business capabilities, map dependencies, and propose a phased migration roadmap that preserves essential functionality while retiring technical debt.

Controls that made it safe

• Human approval of all capability maps, dependency analyses, and migration waves
• Traceable provenance linking every recommendation to source systems and artifacts
• Deterministic impact analysis before decommissioning or refactoring components
• Versioned decision records and immutable modernization audit trails

Outcome

Challenge

Integration failures and supply-chain disruptions often surfaced only after customers were affected, leaving operations teams to piece together root causes and coordinate manual recovery under significant time pressure.

Approach

AI agents continuously monitor business events, correlate signals across systems and partners, and recommend recovery actions while coordinating notifications, rerouting, and contingency workflows. The AI proposes and orchestrates responses, with humans retaining authority over material decisions.

Controls that made it safe

• Deterministic guardrails governing automated actions and escalation thresholds
• Explainable recommendations with traceable evidence from source systems and partner feeds
• Mandatory human approval for customer-impacting or high-value interventions
• Immutable logs of detections, decisions, and recovery actions for post-incident review

Outcome

Challenge

Critical expertise was scattered across documents, emails, and the memories of long-tenured employees, making answers inconsistent and increasing the risk of AI hallucinations as teams sought to scale knowledge access.

Approach

An enterprise retrieval layer grounds AI responses in approved content, while knowledge stewards review and curate sources, resolve conflicts, and continuously improve coverage. The AI synthesizes information but never speaks without attributable evidence.

Controls that made it safe

• Provenance on every answer, with citations to authoritative documents and records
• Human curation of source content and approval of high-impact knowledge domains
• Deterministic policies restricting responses when evidence is incomplete or conflicting
• Immutable logs of queries, sources consulted, and content updates

Outcome

Challenge

Customers expected immediate, accurate answers around the clock, yet support teams struggled to maintain consistency across channels while protecting sensitive information and avoiding AI-generated misinformation.

Approach

A retrieval-grounded assistant answers questions using a curated knowledge base, cites its sources, and seamlessly hands off complex or high-risk interactions to human agents. Agentic workflows continuously improve content coverage while keeping final accountability with the business.

Controls that made it safe

• Responses grounded in approved sources with visible citations
• Explicit refusal boundaries for out-of-scope or unsupported requests
• PII redaction and prompt-injection defenses on every interaction
• Automatic escalation to human support for sensitive or low-confidence cases

Outcome

Challenge

Once in production, AI systems can drift in quality, cost, and behavior, yet many organizations lack objective ways to detect regressions, measure hallucinations, or validate changes before deployment.

Approach

An evaluation and observability framework combines golden datasets, automated regression tests, and live telemetry to continuously assess answer quality, safety, latency, and cost. Model and prompt updates are promoted only after meeting predefined acceptance criteria.

Controls that made it safe

• Versioned evaluation suites with deterministic pass/fail thresholds
• Production monitoring and alerting for quality, safety, and cost anomalies
• Human review of material degradations and release approvals
• Immutable records of prompts, models, evaluations, and deployments

Outcome

Challenge

Multiple AI pilots had emerged across the organization, yet they lacked a common governance model, clear ownership, and a practical path from experimentation to enterprise production.

Approach

An AI readiness assessment evaluates initiatives by business value, risk, and technical feasibility, establishing a model inventory, risk-tiering framework, approval gates, and a phased roadmap that prioritizes high-impact, production-ready capabilities.

Controls that made it safe

• Standardized intake and risk assessment for every AI use case
• Documented governance policies, approval workflows, and accountable owners
• Stage gates requiring evidence of value, safety, and operational readiness
• Transparent portfolio metrics and decision records for ongoing oversight

Outcome

Challenge

Prior authorization is a manual bottleneck — clinicians and payer staff spend days matching requests against clinical guidelines and plan rules. Patients wait, denials get appealed, and administrative cost compounds.

Approach

An agent reads the request and clinical documentation, retrieves the applicable evidence-based guidelines and plan policy, and proposes a medical-necessity decision with a confidence score. High-confidence standard approvals clear automatically; denials and low-confidence cases route to the right clinician, who decides.

Controls that made it safe

• Confidence-tiered routing — only high-confidence standard approvals auto-clear; denials and low-confidence always escalate to a human
• Shadow mode before go-live — AI results compared against human decisions, not submitted, until accuracy is proven
• Grounded in approved clinical guidelines and plan policy, with citations on every recommendation
• Immutable, timestamped audit trail (HIPAA / signed BAA); PHI-scoped, private-cloud deployment

Outcome

Challenge

Adverse-event case processing dominates drug-safety cost — roughly half to two-thirds of pharmacovigilance budgets — is deadline-bound and heavily regulated, and case volumes keep rising.

Approach

Agents intake adverse-event reports, code them, and draft case narratives grounded in the source documents; safety experts review and retain sign-off on all serious cases and regulatory submissions.

Controls that made it safe

• Risk-based governance proportional to seriousness (per CIOMS Working Group XIV) — routine cases automated, serious cases human-signed
• Offline validation against representative datasets, then parallel / shadow running before live use
• Version-controlled, traceable audit trail of every AI action
• Continuous post-deployment monitoring with drift detection that triggers re-training or decommission

Outcome

Challenge

Caseworkers must apply sprawling, frequently-changing federal, state, and county program rules. Answers come out inconsistent, onboarding is slow, and errors carry real consequences for citizens.

Approach

An agent gives caseworkers real-time, plain-language, cited answers to complex program policy — grounded in verified, current government policy. The caseworker keeps the decision.

Controls that made it safe

• Every answer grounded in verified, current government policy with cited sources
• AI proposes / caseworker decides — the determination stays with the human
• Refusal boundaries — the agent declines when policy is ambiguous or out of scope rather than guessing
• Auditable record of sources consulted and guidance given

Outcome

Challenge

Underwriters spend hours gathering and keying submission data, and standard and renewal risks queue behind complex ones — slowing quotes and constraining capacity.

Approach

AI ingests the submission, enriches and structures it, and proposes a price and decision for standard and renewal risks in minutes, routing complex risks to human underwriters. AI stays behind the human interaction.

Controls that made it safe

• Human-in-the-loop split — AI drafts standard and renewal; underwriters own complex risk and final authority
• Quote produced ready for underwriter review, never bound autonomously
• Grounded in the carrier’s rating rules and appetite, with provenance on enrichment data
• Audit trail from submission to quote; least-privilege access to source systems

Outcome

Challenge

Rule-based screening buries analysts in false positives — the vast majority of alerts — inflating cost and slowing onboarding while genuine risk hides in the noise.

Approach

Agents triage screening alerts, gather corroborating intelligence — entity resolution, relationships, geography, adverse-media relevance — and propose a disposition with cited evidence; analysts confirm true matches and own consequential decisions.

Controls that made it safe

• Human-in-the-loop for every consequential disposition (true-match confirmation, SAR triggers)
• Provenance and citations on every adverse-media hit and match rationale
• Immutable audit trail of alerts, evidence, and decisions for examiners
• Refusal and escalation boundaries when evidence is thin or conflicting

Outcome

Challenge

Claims triage is slow and manual, and a large share of subrogation-recovery opportunities are never pursued — an estimated multi-billion-dollar annual leakage.

Approach

Agents triage incoming claims, propose straight-through settlement for standard low-risk claims, and flag missed subrogation-recovery opportunities with supporting evidence; adjusters approve consequential payouts and pursue recovery.

Controls that made it safe

• Straight-through only for standard, low-value / low-risk claims; adjuster approval for consequential payouts
• Deterministic policy and coverage validation before any settlement proposal
• Provenance on every recovery flag and an immutable claim audit trail
• Risk-tiering — value and complexity thresholds gate what can auto-settle

Outcome