Responsible AI Delivered. Results That Held Up.

The Challenge

A regional health system with multiple hospitals and outpatient clinics was processing prior authorization requests manually. Clinical staff spent an estimated 18 to 22 hours per week per site on PA submissions, status checks, and appeals. Denials were rising due to incomplete submissions, and delays in authorization were affecting patient scheduling and satisfaction scores.

Our Approach

We began with a six-week discovery and requirements phase to map every PA workflow variant across payer types and procedure categories. We designed a HIPAA-aligned agent architecture that integrated with the existing EHR via approved APIs. Safety requirements included mandatory human review for any PA requiring clinical judgment and complete audit logging of every AI-generated output.

The agent was built to draft PA submissions, attach supporting documentation from the patient record, check submission completeness before filing, and flag likely denial risk based on payer-specific criteria. Human staff reviewed and approved every submission, the agent handled documentation and routing, not clinical decision-making.

What We Built

• PA drafting agent integrated with EHR and payer portals
• Document completeness validator with pre-submission checks
• Denial risk scorer based on payer-specific criteria
• Human approval workflow with single-click review interface
• Complete audit log for every AI-generated action
• HIPAA compliance documentation for the AI system

The Challenge

A mid-market community bank was generating high alert volumes from its legacy AML transaction monitoring system. Compliance staff spent the majority of their time reviewing alerts that turned out to be false positives. Genuine risk signals were buried in noise. The bank needed a way to prioritize alerts more intelligently without reducing human oversight of flagged transactions.

Our Approach

We built an AI agent that triage-scored incoming alerts before they reached compliance staff. The agent enriched each alert with customer behavior history, peer group comparisons, and prior alert outcomes, then scored the alert's risk priority using explainable factors, not a black-box model. Every scoring decision produced a plain-language explanation that compliance officers could read, evaluate, and document.

The governance work ran in parallel: we developed a model risk management framework for the AI system, documented the scoring methodology for regulatory purposes, and designed review procedures that satisfied the bank's BSA officer requirements.

What We Built

• Alert enrichment agent with customer context aggregation
• Explainable priority scoring with plain-language output
• Compliance officer review interface with decision logging
• Model risk management governance documentation
• BSA officer oversight framework and review procedures
• Regulatory examination documentation package

The Challenge

A specialty manufacturer with a complex supplier network was managing procurement communications manually across email, phone, and supplier portals. Purchasing staff spent significant time on routine communication tasks, status checks, delivery confirmation, discrepancy resolution, that had little strategic value. The manual process also created delays and inconsistencies in supplier follow-up.

Our Approach

We designed a procurement communication agent that handled routine supplier interactions while keeping purchasing staff in control of all substantive decisions. The agent drafted supplier communications, pulled status from supplier portals, flagged exceptions for human review, and updated the ERP system after human approval of any changes. No ERP write-back occurred without purchasing staff confirmation.

Safety design was critical in this engagement, we defined a clear list of actions the agent could take autonomously (read, draft, alert) and actions that required human approval (ERP updates, order modifications, escalation decisions).

What We Built

• Supplier portal monitoring and status extraction agent
• Routine communication drafting with template library
• Discrepancy detection and exception flagging workflow
• Human approval gate for all ERP write-back actions
• Supplier communication audit log
• ERP integration with SAP via approved API connectors

The Challenge

A property and casualty insurer was experiencing bottlenecks in first notice of loss processing. Claims intake required manual data entry from policyholder submissions, coverage verification across multiple systems, and initial triage decisions about claims complexity. Intake staff averaged 35 to 45 minutes per claim, and the volume was growing.

Our Approach

We built a claims intake agent that handled the data collection and verification work while keeping adjusters in control of all coverage and triage decisions. The agent extracted claim details from policyholder submissions, verified coverage against the policy system, checked for potential fraud signals, and presented a structured summary to the intake adjuster, who made all coverage and triage decisions based on the agent's compiled information.

Governance requirements included state insurance regulator documentation, adverse action notification procedures for AI-flagged claims, and a bias audit protocol for the fraud signal detection component.

What We Built

• Claims submission intake and data extraction agent
• Coverage verification with policy system integration
• Fraud signal detection with explainable flag reasons
• Structured adjuster summary interface
• State regulatory compliance documentation
• Bias audit protocol for fraud detection component

The Challenge

A large enterprise's in-house legal department was reviewing hundreds of vendor and partner contracts annually. Initial review, reading for key clause presence, identifying non-standard terms, and flagging missing provisions, was consuming associate attorney time that could be spent on more complex work. The department wanted to accelerate initial review without reducing accuracy or creating privilege risk.

Our Approach

We built a contract review agent that processed incoming contracts against the company's approved playbook, identifying deviations from standard clause language, flagging missing provisions, and highlighting high-risk terms for attorney attention. The agent produced a structured review summary that reduced initial review time while ensuring attorneys reviewed every flagged item before any response was sent.

Privilege and confidentiality design was central to the architecture. The agent operated on an air-gapped deployment with no external data transmission. We documented the data handling architecture for the general counsel and provided a legal AI use policy template for the department.

What We Built

• Contract ingestion and clause extraction agent
• Playbook comparison engine with deviation flagging
• Risk term identification and attorney alert system
• Structured review summary output for attorney use
• Air-gapped deployment with no external data transmission
• Legal AI use policy and data handling documentation

The Challenge

A growth-stage B2B SaaS company had added AI features to its core product under competitive pressure. Enterprise prospects were conducting AI due diligence as part of procurement, asking how the AI worked, who was accountable for its outputs, and what documentation existed. The company could not answer these questions, and deals were stalling at security review. They had the product. They lacked the governance.

Our Approach

We conducted an enterprise AI readiness assessment to inventory all AI features across the product, identify governance gaps, and assess data handling practices against enterprise buyer expectations. We then designed a comprehensive governance framework and produced the documentation that procurement teams were asking for.

The engagement included building out model cards for each AI feature, a transparency policy, data processing documentation for the AI components, an internal AI review process for new features, and a customer-facing AI FAQ that addressed the specific questions that had been blocking enterprise deals.

What We Built

• Enterprise AI governance framework and written policy
• Model cards for each AI-powered product feature
• AI transparency policy and public-facing FAQ
• Data processing documentation for AI components
• Internal AI feature review process and approval gate
• Enterprise buyer due diligence documentation package