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AI Adoption Advisory for Engineering Teams
AI adoption advisory helps engineering teams turn LLMs from expensive experiments into measured leverage by choosing the right model per task, integrating AI into existing workflows (not side projects), setting guardrails and evals, and keeping token spend near ~$50/engineer/month instead of runaway bills.
What is AI adoption advisory?
AI adoption advisory is senior engineering guidance on where LLMs actually help your product and team — and where they waste money. It is not a pitch for autonomous agents, a model benchmark blog post, or a generic 'AI transformation' deck.
The work covers four pillars: model selection (right model for the job), workflow integration (AI inside how engineers already ship), guardrails (boundaries, evals, cost controls), and cost discipline (token spend that stays sane as usage grows).
The four-pillar framework
Most teams fail AI adoption in predictable ways: they pick the trendiest model, bolt on a chatbot, skip evals, and discover the bill three months later. This framework is what I use with my own team of 12 engineers shipping production AI systems.
- Model selection — route simple tasks to smaller models; reserve frontier models for tasks that need reasoning depth. One LLM call often beats a fragile multi-agent chain.
- Workflow integration — AI belongs in code review, test generation, incident triage, and spec drafting — not in a separate 'AI team' silo.
- Guardrails — define what the system must never do, add eval sets for regressions, and log prompts/outputs for debugging.
- Cost — track spend per engineer and per feature; ~$50/engineer/month is a realistic target when tooling is chosen deliberately (reference from my operating team).
What you get in an engagement
Engagements are advisory — brain, not hands. I do not write your production code; I make your engineers more effective with architecture feedback, adoption roadmaps, and unblocks when something is stuck.
- Audit of current AI usage: what's working, what's burning cash, what's blocked on architecture.
- Prioritized adoption roadmap for 90 days with one measurable outcome.
- Model and vendor recommendations tied to your stack (TypeScript, Python, etc.) and latency/cost constraints.
- Guardrail and eval templates your team can run in CI.
- Weekly call plus async for seed → Series A; embedded 1 day/week for post–Series A with heavier eng load.
When this is not a fit
Honest disqualification saves everyone time. This advisory is wrong if you need someone to write production code as the primary deliverable, want autonomous agent hype builds, or need full-time CTO coverage.
Proof points
- Media-localization platform: ~18k hours of content processed daily
- AI twin systems: ~75% reduction in manual response load
- Reference token spend: ~$50/engineer/month with deliberate tooling choices
FAQ
Isn't AI just expensive hype right now?
Expensive for teams that use it carelessly. Used well, it is the cheapest senior engineer you will hire — on my team that is about $50 per engineer per month with deliberate model routing and workflow integration.
Do you recommend a specific LLM vendor?
No single vendor wins every task. Advisory matches model to job: Anthropic, OpenAI, Google Gemini, DeepSeek, and others each have sweet spots. The decision depends on latency, cost, context length, and whether you need tool use or vision.
How is this different from hiring an AI consultant?
I am currently Head of Engineering running twelve engineers on production AI — media localization at ~18k hours/day processed, dubbing pipelines, AI twin systems with ~75% manual load reduction. You get operating experience, not slideware.
How fast can we start?
Discovery call this week, one-page scope within a few days, start the following week.
Next step
30-minute discovery call — fit and disqualify honestly. Scope doc within a few days if we proceed.