Insights
Notes on putting AI to work
Field notes from forward-deployed teams on agentic workflows, AI integration and rebuilding software stacks inside real businesses.
AI infrastructureWhy Amazon Bedrock unlocks AI for healthcare and legalSensitive records make AI harder to deploy. Bedrock gives teams a foundation for controlling access, reviewing outputs and getting useful work into production.Read the article
Platform & cloudWhy Cloudflare belongs in your 2027 infrastructure plansApplications, background jobs, files and database connections on a platform a small team can operate. That is a serious advantage when the roadmap keeps growing.
Data foundationsWhy Postgres stands the test of timeYour interface will change. Your AI models will change. The records your business depends on still need to be right.
AI teamsWhat is a forward-deployed engineer?What engineers do when they work directly with customer teams, connect the software and stay through production rollout.
Agentic AIAgentic AI vs. generative AI: what changes in your business?Drafting a reply and resolving a customer’s request need different software. Here’s where the distinction matters.
WorkflowsAgentic workflow examples: from request to resolutionA useful workflow finds the record, takes the permitted action and checks the result. Three examples show the work between those steps.
Data & integrationAI-ready data starts with knowing which customer is whichBefore connecting an agent to your CRM, check what the records mean, which ones belong together and what must stay separate.