Shipping AI to Production: A Practical Playbook

Most AI projects die in the gap between a convincing demo and a dependable system. The model is rarely the problem. Evaluation, integration, and adoption are where budgets quietly disappear.

Start with the problem, not the model

Pick a costly, repetitive, high-volume workflow — the kind that follows rules and eats hours. That is where automation pays off. The choice of model comes last. When you do choose, the major providers publish clear guidance; Anthropic's documentation is a good place to understand what production-grade prompting and tool use actually involve.

Build the unglamorous 80%

A prototype that works on ten clean examples is not a system. Production means messy inputs, edge cases, and a way to know the output is good at scale.

• Evaluation — how do you measure quality without reading every response by hand?
• Guardrails — what happens when the model is confidently wrong?
• Integration — does it fit the workflow your team already uses?
• Economics — does the per-request cost still make sense at 100x the volume?

No vaporware. No science projects. Start with constraints, ship to production, and train the team to actually use what you build.

Roll out in measured bets

Build a proof-of-concept in weeks. Measure it against the human baseline. If it clears the bar, harden it with evaluation and guardrails, then expand. Each small bet pays for the next — and your data stays yours throughout.

That is exactly how our AI solutions engagements work: highest-impact use case first, production-ready, with your team trained to own it.