AI should move a number
Before we build, the first question is simple: which revenue, margin, or cost metric should change?
OPERATIONAL AISTRATEGY
BLOGS
Notes from the workbench. Retrieval, evals, orchestration — and the engineering behind them.
We write occasionally, when something we've shipped teaches us a lesson worth keeping. No content calendar. No newsletter funnel.
Small models belong closer to the work
For bounded operational tasks, local inference can reduce latency, cost, and data-governance friction.
EDGE AISLM
The frontier is moving toward finished work
Recent releases are framed around agents, documents, images, and production assets — not just chat.
FRONTIERAGENTS
Systems, not slogans
AI should be operational, measurable, and owned by the client. Sometimes the right answer is a simpler automation layer.
POINT OF VIEWOPERATIONAL AI
A production RAG system starts before the prompt
The model is the final step. The real work is in ingestion, chunking, hybrid retrieval, reranking, citation fidelity, and evaluation.
RAGENGINEERING
The infrastructure race matters. The application layer decides the return.
Frontier funding is accelerating. But adoption still arrives through workflows, not through compute.
INDUSTRYADOPTION
Barge-in is the hardest part of a voice agent
Latency, turn-taking, and graceful interruption decide whether callers trust a voice AI. A checklist from production voice builds.
VOICELATENCYENGINEERING
Manufacturing teams don't lack data. They lack a usable loop.
Work orders, downtime logs, and machine alerts already contain the signal. The gap is between signal and intervention.
MANUFACTURINGOPERATIONAL AI
Returns are a margin problem disguised as a support problem
Manual reviews, duplicate refunds, policy exceptions, and unclear product photos all leak cost. Bounded AI fits this workflow well.
E-COMMERCEOPERATIONAL AI
Sovereign AI is moving from policy language to procurement language
The enterprise question is shifting: can the system run where the data, regulations, and audit obligations already live?
SOVEREIGN AICOMPLIANCE
A forecasting agent is a decision system, not a chart
The model matters. The operational loop matters more: ingest, forecast, explain, alert, and learn from outcomes.
FORECASTINGAPPLIED ML
Evals are not optional
If you can't reproduce yesterday's quality today, you don't have a system — you have a coincidence.
EVALSENGINEERING
RAG vs fine-tuning for enterprise knowledge
When to retrieve, when to fine-tune, and when to do both. A decision framework from shipped enterprise systems.
RAGFINE-TUNINGARCHITECTURE
Retrieval is half the problem
Most AI projects fail at retrieval, not generation. Notes from a year of shipping RAG systems that have to be right.
RAGRETRIEVALEVALS
Evals for LLM hiring pipelines
Candidate evaluation systems have the highest bar for fairness and auditability of any AI use case. What our eval stack actually looks like.
EVALSHIRINGFAIRNESS
Have a problem worth writing about?
The interesting ones become posts later — with permission.
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