What changed for our clients

The broker had approved a major AI budget with an IPO on the horizon. Pilots were stalling before production, tools sprawled with no shared context or guardrails, AI governance was very new and regulatory monitoring across 55 countries was too dynamic for rules.

We ran a five-part transformation across people, process, technology, governance and execution, built on one governed agentic platform: orchestration, shared context and ontology, evals, guardrails and observability. Agents for compliance monitoring, submissions, capacity marketing and client intelligence run across the broking value chain.

The business had just gone live on SAP and Monday.com, alongside ServiceNow, web and mobile. Testing happened in production and QA was manual and reactive. There was no PMO, so initiatives were funded without a shared view of ROI.

We ran a 12-week, AI-led QE assessment covering test coverage, risk and automation, maturity benchmarking and tool selection, which led to a target-state QE blueprint, rollout roadmap and ROI case. Alongside it, we set up a PMO with a weighted value-scoring model, prioritization matrix and portfolio value dashboard that charts every initiative against customer outcomes.

The network handled 8,000+ prior authorizations a month through offshore, manual workflows. That created CMS compliance exposure, ~12% rework from eligibility errors, 48–72 hour turnarounds, and nurse practitioners spending ~15 hours a week on admin.

We developed three AI agents that take on eligibility, prior-authorization and pharmacy work in place of the offshore manual process. Staff review the exceptions, and patient data stays inside the client’s network.

Claims, repricing, provider, contract and EDI data sat in separate systems, with no governed, repeatable way to onboard data domains. A new application needed certified out-of-network claim repricing data, with HIPAA-ready controls from day one.

We built an AI-ready lakehouse foundation on Oracle AI Data Platform and OCI, using federation-first connectivity (zero-copy catalog, JDBC and GoldenGate change capture), bronze, silver and gold layers, data contracts and automated quality gates. AI accelerators helped build profiles, mappings and tests faster, with human-in-the-loop approval. The first certified data product feeds the new application and an Oracle Analytics savings view.

SAP, the commerce platform, the CDP, ServiceNow and Monday.com ran as separate silos. Customers were treated as new at every touchpoint: web, app, support and repair. Repair records didn’t match, warranty reimbursement for retail partners was slow, and testing happened in production.

We built a Customer 360 profile that joins transactions, CDP, service, warranty and operations data. An AI orchestration layer above the existing systems drives workflow optimization, AI-generated test cases, automated PMO artifacts and agent-powered customer experiences. Quality engineering and the PMO are built around the customer, so journeys are tested across systems.

Facilities teams at multi-site enterprises had no reliable way to hire trade service providers. Search, referrals and directories were unscreened and unaccountable. The start-up faced a two-sided cold start, liability risk from poorly vetted providers, and enterprise expectations of SOC 2-grade security.

We started with a phased MVP: one service and curated, ops-assisted onboarding, then grew it into a full marketplace. We developed buyer, vendor and admin portals covering RFPs, contracts, projects, compliance documents and payments. RFP scoping and vendor matching were AI-assisted, with human review, on a cloud-native, SOC 2-ready AWS platform.

A backend-heavy, tenant-driven ETL platform was complex, custom-built and dependent on tribal knowledge. It slowed down at peak load, needed frequent manual recovery and relied on reactive, fragmented monitoring.

We delivered a reuse-first re-platform to OCI and Oracle APEX, with a tenant-centric operations dashboard and a rebuilt orchestration engine on Autonomous Database. About 30% of Python moved to PL/SQL, with the rest on OCI Functions. Event-driven alerting runs through OCI Alarms, Functions and Notifications, storage moved to OCI Object Storage and a risk-based test approach covers every tenant.

A US health data and analytics firm released three or four times a year. Every deployment was manual, orchestrating one could take four hours and backing out a bad release took around 45 minutes.

We rebuilt delivery around automated pipelines on their cloud platform: infrastructure in code, scans on each build that halt the release on critical findings, and releases that reverse themselves when health checks fail.

Growing through acquisitions, the broker had fragmented CRMs with no unified lead tracking. Win and loss data wasn’t captured at group level, placement and claims status was invisible to client teams, and renewals were managed from spreadsheets and broker memory. It also faced a CRM buy-or-build decision.

We developed a vendor-neutral platform decision across Salesforce, HubSpot and Dynamics, and built a single client identity through entity resolution across every entity, with API and event-stream integration to policy admin, placement and compliance tools. Six explainable ML models, one per lifecycle stage, covered lead scoring through renewal risk and wallet-share gaps, and stayed in place if the CRM changes.

Requirements were ambiguous and prone to defects. Testing was manual, happened after the sprint and wasn’t trusted, so releases were slow, too many defects reached production and technical debt grew with no coverage safety net.

We moved quality to the start of delivery. BDD acceptance criteria were written with the business during refinement, and automation was built in-sprint. We rebalanced the test pyramid toward fast unit and integration tests, and added coverage and security gates in CI/CD that block a merge when it falls short.