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Policy-driven agents, document intelligence, and enterprise automation — from the team building it.
78 articles
Policy as code encodes infrastructure rules in programming languages for checkpoint enforcement. Policy-driven automation compiles plain English business rules into deterministic execution plans for end-to-end workflows. They solve different problems: one governs deployments, the other governs business decisions in regulated industries.
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How MightyBot went from meeting assistants to production AI agents processing real financial transactions. The year agentic AI became real.
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AI hallucinations are a system-design problem in enterprise workflows. Learn how source evidence, structured extraction, policy validation, confidence routing, and audit trails prevent unsupported AI outputs.
How autonomous AI automates construction loan draw processing with 99%+ accuracy. Built Technologies and MightyBot deployed Draw Agent for construction lending, cutting review time by 95%.
A production AI agent case study: how Built Technologies and MightyBot deployed Draw Agent for construction lending in three months using daily sprints, AI exoskeleton architecture, progressive rollout, and 99%+ accuracy.
How AI assistants improve team meetings, project management, and cross-functional coordination. Practical strategies for modern distributed teams.
How top revenue teams use AI agents for call prep, follow-up, and pipeline management. Practical use cases with measurable impact, not hype.
Enterprise AI adoption works when teams pick a measurable workflow, govern the agent, connect real data, start with human review, and scale only after production proof.
PhD-level AI agents can score well on expert benchmarks, but business value depends on workflow design, context, tools, evals, supervision, and domain-specific reliability.
Transform Uncertainty into Opportunity: Thriving with AI at Work
Product insights vanish because they are scattered across too many channels. Learn why customer feedback gets lost and how AI-powered capture, categorization, and routing solve the problem at scale.
AI agents need context engineering, not just larger context windows. Learn how retrieval, tools, memory, policies, permissions, evals, and audit trails make agents reliable in production.