Four distinct practices

AI-enabled business processes, built on the systems a business already runs. The work covers agent orchestration, integration between tools that were never meant to talk to each other, evaluations so the behavior can be measured rather than assumed, monitoring, human oversight at the points that matter, and exception handling for everything the happy path does not cover.

Exception handling is where these projects are won or lost. A flow that handles 90% of cases and silently drops the rest creates more work than it removes, so the queue, the owner of that queue and the replay path are part of the build rather than a later phase.

• AI-enabled business processes
• Agent orchestration
• System integrations
• Evaluations
• Monitoring
• Human oversight
• Exception handling

Example of first deliverable. A map of the current process with the automation candidates ranked by value and difficulty, and a build target for the first one.

Case study: four automations for a small business

Models that represent how the business actually works, so data is structured around real needs: what each data asset means, who owns it and whether anyone can prove where it came from. The work covers data ownership, business definitions, semantic modeling, data quality, lineage, access controls and the trusted data that analytics and AI depend on.

• Data ownership
• Business definitions
• Semantic modeling
• Data quality
• Lineage
• Access controls
• Trusted data for analytics and AI

Example of first deliverable. A data maturity assessment with a prioritized remediation plan, or a target model.

Case study: three regulatory reports, partially produced and reconciled by machine

The technical foundation: data pipelines, lakehouses, warehouses, integrations, migrations, networking and access, and reliable data infrastructure.

• Data pipelines
• Lakehouses
• Warehouses
• Integrations
• Migrations
• Networking and access
• Reliable data infrastructure

Example of first deliverable. A target architecture with its sequencing, dependencies and risks.

Case study: efficiencies through data hub centralization

A review of the controls around AI already in use and AI about to roll out, with recommendations for improvement. Each control traces to a recognized standard, typically the NIST AI Risk Management Framework, the EU AI Act, NY DFS guidance and, in California, SB 53, the CCPA / CPRA, and SB 813 and AB 1405 on AI assurance and oversight, where they apply, with sector-specific controls added on top. Obligations are profiled per use case rather than applied as one checklist across all of them, since two use cases in the same organization rarely carry the same ones.

Recommendations carry effort and impact so the organization can sequence them against its own capacity. A report that lists everything as critical gets read once and shelved.

• AI use case inventory
• Control assessment against standards
• Obligation and risk profiling
• Bias and fairness testing
• Drift and output monitoring
• Prioritized recommendations

Example of first deliverable. An assessment of the in-scope use cases with evidence behind each rating and a prioritized set of recommendations.

Case study: scaling AI proofs of concept safely at an insurance carrier