AI Strategy
Choosing your first AI use case: a project that actually ships
Most first AI projects die because the use case was picked for excitement, not fit. A framework for choosing, scoping and de-risking round one.
Read article →Insights
The XORLabs blog.
Field notes on agentic AI, RAG, LLM engineering and what it actually takes to ship production AI.
Articles
AI Security
Securing LLM applications: prompt injection, leakage and guardrails that hold
LLM apps have an attack surface traditional reviews miss. The threat model and the layered defences that have actually held in production.
Read article →AI Economics
The LLM bill: cost and latency engineering for AI products that scale
Model routing, caching, prompt hygiene and unit economics — the engineering that keeps an AI feature profitable when adoption takes off.
Read article →Retrieval & Knowledge
RAG in production: why retrieval quality decides whether your AI is trusted
Most RAG failures are retrieval failures. Corpus prep, structure-aware chunking, hybrid search, enforced grounding and separate evaluation.
Read article →Food-Tech
AI in food-tech: forecasting demand, fighting waste
Perishable demand forecasting, menu intelligence, delivery logistics and food safety — where AI moves the P&L in an industry of single-digit margins.
Read article →Telecom
AI in telecom: from alarm storms to self-optimizing networks
Alarm correlation, predictive maintenance, grounded customer support and field copilots — with the change-control guardrails carrier networks require.
Read article →Healthcare
AI in healthcare: cutting the paperwork, keeping the clinician
Ambient documentation, prior authorization, intake triage and revenue cycle — the administrative wins delivering value today, and the PHI guardrails they need.
Read article →Fintech
AI in fintech: fraud, risk and compliance without the black box
Real-time fraud, explainable credit decisions, AML agents and compliance copilots — engineered for the scrutiny regulators bring.
Read article →AI in the SDLC
AI across the SDLC: every phase of delivery, rewritten
From requirements to maintenance, every phase of the lifecycle now has an AI-shaped seam running through it. A phase-by-phase field guide.
Read article →Agentic AI
Agentic AI in production: what actually ships in 2026
Most agent demos never reach production. Here's the architecture, guardrails and evaluation discipline that get autonomous agents past the prototype.
Read article →Developer Experience
From autocomplete to coworker: a day with an AI-augmented developer
The unit of developer work has moved up a level. An honest tour of a 2026 workday — briefs, reviews, debugging and the skills that still matter.
Read article →Quality Engineering
AI code review and testing: raising the bar while shipping faster
First-pass review bots, curated test suites and eval gates in CI — how to use AI for quality without rubber-stamping your way to production.
Read article →IT Operations
AIOps grows up: from alert fatigue to self-healing IT
Helpdesk agents, incident copilots and runbook automation with approval gates — how AI is rewiring day-to-day IT operations.
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