Shipping AI Agents to Production: The FDE Playbook
AI agents are easy to demo and brutal to ship. The FDE playbook: evals, guardrails, human escalation, and phased rollout.
Read article → AI & DataFrom AI Pilot to Production: Where Projects Die
Most AI pilots die in the handoff to production — here's the deployment checklist that gets them across the valley of death and into stable operations.
Read article → AI & Data'Chat With Your Data' Is a Trap (Unless...)
Text-to-SQL demos dazzle then lie. Here's the semantic-layer pattern that makes chat-with-your-data trustworthy.
Read article → AI & DataMonitoring AI Agents in the Wild
Agents fail silently. Here's the four-signal monitoring stack that catches drift, cost creep, and quality decay before your customers do.
Read article → AI & DataEntity Resolution: When 'Acme Inc' Has 14 Spellings
Entity resolution dedupe business data: the matching pipeline that turns 14 versions of the same customer into one reliable record.
Read article → AI & DataBuild vs. Buy for AI Features: The Framework
SaaS copilots are fast but generic. Custom AI fits your data but costs more upfront. Here's the decision framework.
Read article → AI & DataMultimodal AI for the Documents You Run On
Receipt photos, scanned contracts, handwritten forms — modern vision-capable models are turning document chaos into structured data.
Read article → AI & DataUsing AI for Data Migration (Carefully)
LLMs are surprisingly good at mapping messy legacy fields. Here's the safe way to use AI for data migration without corrupting your new system.
Read article → AI & DataFrom Spreadsheet to Pipeline: Escaping Excel Hell
The three-phase escape from Excel hell: read-only sync, shadow writes, and the day you finally delete the file.
Read article → AI & DataAI Agents for Customer Support: A Reality Check
The demo was perfect; Tuesday is not. Where AI support agents genuinely deflect tickets, where they torch trust, and the 90-day rollout that works.
Read article → AI & DataEvals: The Unsexy Discipline Behind Reliable AI
LLM features fail politely, in fluent prose. Evals are unit tests for probabilistic systems: real test sets, three scoring methods, and deploy-time gates.
Read article → AI & DataLLM Cost Control: No Surprise Token Bills
Your AI feature 4x'd its API bill and nobody did anything wrong. Four boring levers (routing, caching, prompt diets, budgets) fix it in a week.
Read article → AI & DataBuilding an AI Copilot for Your Ops Team
The demo reads a sample dataset; your orders live in a 14-year-old ERP. How to build an ops copilot that closes that gap.
Read article → AI & DataExtracting Data From Systems That Have No API
No API? No problem. Three proven paths for getting data out of systems that were built before APIs were a thing.
Read article → AI & DataAI Guardrails for Business: Keep Models on the Rails
The three-layer guardrail stack that keeps business LLMs useful instead of famous: output validation, dollar-tiered autonomy, and a real escalation queue.
Read article → AI & DataChoosing a Vector Database Without Losing Your Mind
pgvector covers most business RAG. The four questions that actually decide your vector database, and when Qdrant, Weaviate, or Pinecone earn their keep.
Read article → AI & DataFine-Tuning vs. RAG: When to Use Each, and Why Teams Get It Wrong
Prompts first, retrieval for knowledge that changes, fine-tuning for behavior. The order of operations most AI projects get backwards.
Read article → AI & DataStructured Outputs: The LLM Feature That Matters
How schema-constrained decoding turned LLMs from chat toys into pipeline parts, plus the schema design rules that survive production.
Read article → AI & DataThe AI Readiness Audit: Is Your Data Good Enough?
Before you buy AI anything, run this five-question audit: truth, volume, history, freshness, documentation. Then score it honestly.
Read article → AI & DataBuild Dashboards Executives Actually Open
Dashboards die because they're built for builders. The fix: one number per tile, agreed red thresholds, and a daily email that pushes the numbers.
Read article → AI & DataLLM Ops for Small Teams: Monitoring on a Budget
Three questions — what did it do, what did it cost, was it any good — and a one-day setup that answers them without buying an observability platform.
Read article → AI & DataAI Agent vs. Boring Script: Pick the Right Hammer
Half the 'AI agents' being sold are cron jobs with a marketing budget. Three questions sort which workflows need an agent and which need a script.
Read article → AI & DataYour First Data Pipeline: An FDE's Week-One Build
Pull on a schedule, land it raw, model it once, show it somewhere. The boring starter pipeline that replaces the Monday export ritual in a week.
Read article → AI & DataLLM Document Processing: Invoices, PDFs, Reality
Your invoice pile is structured data wearing a costume. The extract-validate-review pattern, honest accuracy ranges, and when to build vs buy.
Read article → AI & DataRAG for Company Data: A Field Guide
Your RAG pilot failed on document mess, not model choice. The ingestion pipeline, permissions, and eval habits that survive contact with real SharePoint.
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