AI Architecture
Hybrid architectures for extracting structured data from invoices, contracts, and regulatory documents. Combining Vision LLMs, rule engines, and human-in-the-loop validation for governed automation.
The bottleneck is no longer the AI — it is your ability to instruct it. A practitioner's assessment of Windsurf, Cursor, Claude Code, OpenAI Codex, and Google Antigravity, with a focus on the human skills that separate productive teams from frustrated ones.
Tail spend is not a sourcing problem. It is a decisioning problem—thousands of low-value choices that are too numerous for human intervention and too variable for static workflows. That is precisely where agent-based systems fit.
Stop reviewing NDAs from scratch. Architect a Legal Agent that enforces your playbook automatically.
Don't use LLMs for math. Use a hybrid architecture for governed AP automation.
Autonomous sourcing agents, tail spend decisioning, and supplier evaluation — architectural patterns for transforming so...
Contract redlining agents, clause extraction, and compliance automation — technical architectures for legal teams lookin...
Autonomous supplier risk detection, real-time monitoring, and predictive disruption alerts — multi-source intelligence p...
Patterns for building autonomous AI systems that plan, reason, and execute multi-step enterprise workflows. From single-...
Automating document-heavy workflows? Let's discuss hybrid extraction architectures.
// Subscribe
One concise email when a new perspective drops. No spam, no fluff.
Unsubscribe anytime.