Insights on development, AI automation, and digital strategy
A VPAT documents how your product conforms to accessibility standards. Enterprise and government buyers will not sign without one.
MCP servers give AI models real capabilities, which makes them a real attack surface. Here is how to harden your MCP setup before production.
One cut fiber line can close a business for a day. Internet failover costs less than one outage. Here are the real options.
Parsing free-form LLM text breaks in production. Structured outputs and function calling give you schema-valid JSON every time.
Prompts alone will not keep an LLM in bounds. Production guardrails check inputs and outputs with code, models, and policy.
SOC 2 compliance roadmap for small SaaS startups: Trust Service Criteria, Type I vs Type II audits, required evidence, realistic costs, and timelines.
Your employees are already using unauthorized AI tools. Here is what shadow AI is, the liability it creates, and how to govern it without killing productivity.
A practical small business AI governance framework for 2026. Build policies, control costs, manage risk, and prove ROI without enterprise overhead.
An operational framework for evaluating LLM outputs with deterministic checks, model judges, human review loops, and regression test suites.
A practical framework for measuring AI return on investment in small businesses, covering cost tracking, time savings, revenue impact, and risk.
Deploy reliable LLM systems in production. Implement schema validation, multi-model fallback chains, token cost controls, latency caching, and prompt defense.
What happens after you deploy AI? A practical guide to the operational realities of production AI systems, covering token cost monitoring, latency profiling, output quality drift, prompt version management, and incident response.
