Insights
What we've learned building AI into real operations.
Practical writing from our engineering work — how to choose a workflow worth automating, which AI architecture actually fits the problem, and how to run a proof of concept that has somewhere to go.
What we write about
Notes from getting AI into production.
Most writing about enterprise AI is either vendor material or research. Very little of it addresses the part that actually decides whether a project works: choosing a use case narrow enough to evaluate, getting the data into a state the model can use, deciding where a person must stay in the loop, and knowing when the honest answer is that the idea is not ready.
These pieces are the checklists we use ourselves. They assume you are trying to decide something — which workflow to automate first, whether retrieval or fine-tuning fits the failure you are seeing, whether a pilot has told you enough to justify building. Nothing here is behind a form.
AI agents
How to choose the right workflow for an AI agent
A practical framework for identifying business workflows where an AI agent can create value without introducing uncontrolled operational risk.
· 6 min read
Enterprise AI
RAG vs fine-tuning: choosing the right approach for enterprise AI
When enterprise AI should use retrieval-augmented generation, when fine-tuning is warranted, when both apply, and what neither approach fixes.
· 5 min read
Delivery
Build an AI proof of concept that has somewhere to go
How to design an AI proof of concept around real data, users, evaluation, security, integration and the constraints production will actually impose.
· 6 min read
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