Strategy
How to think about LLM adoption, build vs buy, and where the leverage actually is.
A demo proves a model can do the task once, on inputs someone chose. Production requires it to do the task every time, on inputs nobody chose, inside your systems, at a cost and latency you can live with. Here are the ten places projects stall in between, the early-warning signs, and a pre-production gate you can adopt.
By Amar Mond
A buyer's checklist for AI development proposals: scope, acceptance criteria tied to an evaluation set, who owns the weights, prompts and data, where your cost exposure sits, and who operates the system after go-live. Includes the questions to ask verbatim and the red flags that should slow you down.
By Amar Mond
Technical diligence on an AI startup comes down to five questions: what is actually yours, can you prove it works, do you have the right to the data, what does each request cost, and what breaks if a vendor changes terms. Here is what reviewers ask for, the evidence pack that answers them, and the red flags that slow a round down.
By Amar Mond
Half of the engagements we turn down come from teams trying to use an LLM where a simpler tool would have shipped in a week. Here's the triage we run before agreeing to take a project.
By Amar Mond
Most AI initiatives go through the same three-way decision: buy a SaaS product, build an in-house solution, or hire a firm to do it. Each is right in different contexts. Here's how we triage.
By Amar Mond