Director of Enterprise Architecture

Differentiator

AI & architecture case studies

• Agentic statement spreading for commercial lending

  • C#
  • Azure AI Foundry
  • GPT-5.6-Sol
  • Schema-bound JSON output
  • Deterministic verification loop

  Lenders receive large document packages: financial statements, tax returns, and supporting files. Analysts historically re-keyed figures into spreading tools by hand. Statement periods had to balance; when they did not, teams traced line items to the right chart-of-accounts entries through slow, manual research. The work consumed skilled analyst time and invited transcription and mapping errors.

  Document packages that once drove hours of manual spreading and reconciliation can load in minutes with balanced statements and math already verified. The analyst role shifts to brief verification: confirming results, refining a few line names or placements, instead of building the spread from scratch.

• Agent-ready engineering standards at scale

  • Cursor rules & skills
  • Angular schematics
  • CI hooks
  • Review standards

  As Baker Hill scaled Angular and C# delivery, with engineers adopting AI-assisted coding along the way, teams felt the same friction from several directions at once: review churn, inconsistent patterns across repos, slow onboarding to “how we build here,” and rework when agent-generated changes did not match architecture or product conventions.

  Stronger pattern consistency and higher-quality AI-assisted pull requests, with less review churn before code reaches “good enough.” Teams reach acceptable quality faster, which also cuts wasted agent iteration and token use. The same corpus laid groundwork for an agentic SDLC pipeline that can reason about Baker Hill’s frameworks, processes, and environments, not just isolated snippets.