AI & product
AI in your product
AI is on the table, whether your product is live or in launch. « We need AI » pressure, fuzzy agency feature, experiments without guardrails. Risk: cost, data, UX, and debt without clear business value.
My angle is product: identify prerequisites, risks, and dependencies upfront, define AI scope to avoid drift and limit hallucinations, then build vs buy and API integration (Claude, OpenAI, etc.) where classic code is not enough. Not MLOps or model operations: I work on usage in the product, not running the machine.
Fractional, phased mandate: mapping, guardrails, POC, then handover or steering your devs or agency on the build. Half-days for framing, full days for a POC, sometimes two days a week if I code a critical integration. Rhythm set on the call.
Frame AI in your product Book a callLinkedInhello@lucrousseau.com
The trap
AI feature because the market asks
Without clear criteria, you ship a chatbot or auto-summary nobody uses, or cannot maintain. Data, cost, quality, fallback when the model changes, unanswered until incident.
Rushing in multiplies surprises: forgotten prerequisites, technical dependencies, scope creep, hallucinations in prod. The stake is to frame before build: where AI creates value, what must be in place, and how to limit risk.
AI in the product, not ML infra
I work on product and integration: decisions, LLM APIs, user experience, and guardrails. Not model DevOps/MLOps (training, GPUs, ML ops pipelines).
Before you rush in
- Prerequisites, risks, and dependencies (data, third-party APIs, team) identified upfront
- AI scope defined: which features, which journeys, what is out of scope
- Build vs buy and success criteria before committing to months of dev
In the product, without drift
- Anti-hallucination guardrails: context, sources, limits on what the model may assert
- Claude, OpenAI, Anthropic APIs: where to wire, what to send, UX fallback if the answer drifts
- LLM content by profile, fast POC, then spec and steering devs or agency
How we work
Fractional, phased, product angle
Not a parallel AI lab or MLOps integrator: fractional mandate focused on product usage. Half-days for framing; full day for a POC; two days a week possible on a targeted integration.
Prerequisites, risks, and scope
Before build: dependencies, data available, legal constraints, what the team must have in place. Written AI scope so it does not swell every sprint.
Build vs buy and guardrails
Model API, partner, or in-house feature: risk, cost, and timeline trade-offs. Data, PII, fallback, human review, and anti-hallucination limits defined before prod.
LLM APIs in the product
Claude, OpenAI, or other: where to wire in the SaaS, data contracts, async and cache, what the user sees. Where classic code cannot meet the need.
Content and journeys by profile
Pages, blocks, or answers adapted to profile via LLM, with editorial and technical guardrails. Not a generic chatbot bolted onto the site.
POC or steering your devs
I ship product POCs on high-leverage topics or steer your devs or agency: phases, AI backlog, clear prerequisites, without micro-managing tickets.
The path
Four typical steps in a product AI mandate
Mapping and use cases
Hypotheses, journeys, prerequisites and dependencies listed, AI scope and go/no-go per feature. Deliverable: where AI helps, where it does not, and what to prepare before coding.
Guardrails and build vs buy
Risks, data, token costs, fallback, compliance, anti-hallucination strategy (context, sources, validation). Build vs buy documented before the dev team commits.
Product POC and prerequisites
Flows, API integration, LLM content by profile: test in days, acceptance criteria. Brief ready for your devs or agency, not an isolated demo.
Handover and optional follow-up
Spec, prioritized AI backlog, pass-off or ongoing steering of the build with your devs. Fractional follow-up for coherence, without a permanent role.
Compare
Fast AI feature vs product AI framing first
This comparison is about AI in the product (usage, APIs, build vs buy). For day-to-day PM/PO needs, see Need a PM or PO, without full-time. AI-first launch or MVP: see also Launch a SaaS or MVP. WordPress product already live: Archaic WordPress: headless or Laravel by phases.
| Dimension | AI feature shipped fast | Product AI framing then targeted build |
|---|---|---|
| Value | Hard to measure; impressive demo. | Hypothesis and metric defined before code. |
| Build vs buy | Default choice or vendor hype. | API, partner, or in-house trade-off with costs and risks. |
| Production risk | Discovered in prod (data, cost, quality). | Prerequisites, risks, and dependencies identified before build. |
| Hallucinations / quality | Fixed after user complaints or press. | Scope, context, sources, and guardrails defined upfront. |
| Scope | MLOps, model ops, or generic chatbot. | Product usage, API integration, targeted content and journeys. |
| My role | Vendor black box or scattered experiments. | Product-tech trade-offs; POC or dev steering, not daily manager. |
How I build
Technical foundations
Product-side AI: use cases, LLM APIs in the SaaS, content by profile, POC before prod. Not MLOps or model operations.
Objections
Common questions
No. No model training, ML ops pipelines, GPUs, or model infra operations. My scope: usage in the product, decisions, LLM APIs, integration, and SaaS-side guardrails.
Yes, on high-leverage topics: POCs and targeted integrations (Claude, OpenAI, etc.), spec for your devs or agency. Long-term run stays with your team, with a clear product frame.
Often no. Need a PM or PO, without full-time covers prioritization and day-to-day rituals. This work covers where AI fits, build vs buy, APIs, risks, and POC before prod. The two complement each other.
Both. I code product POCs (APIs, flows, LLM content) when needed. I steer your devs or agency: phases, AI backlog, prerequisites, without taking every ticket.
No. Your devs or agency keep operational pace. I clarify use cases, guardrails, and product integration; I do not manage people or day-to-day tickets.
By framing scope: which questions the product may handle, which sources or data the model receives, what you show or hide, fallback and human review when risk requires it. We test in a POC before prod, not by hoping « the model will behave ».
Often no. We start from journey and user job: targeted summary, contextual help, content generation by profile, etc. A bolt-on widget without criteria often ends unused or expensive.
Depends on the phase. Half-days for mapping and workshops. A full day for an API or LLM content POC. Two days a week if I code a critical integration. Set on the call.
Product criteria and guardrails stay stable even when the model changes: that is what avoids rebuilding on every vendor announcement.
The agreed rhythm structures deep work, not whether you can reach me.
Ongoing, I make time to reply on Slack or your preferred channel. If an emergency lands outside scheduled days, I stay reachable and step in under a shared urgency frame we define up front (scope, priority, response time), not a vague 24/7 on-call promise.
Next step
Pressure to add AI without a frame? One call to see where it fits in the product, build vs buy, and what deserves a POC before prod.
LinkedInhello@lucrousseau.comNext step
Let's talk about your context
A 30-minute call to see if fractional support (product, technical, or both) fits your situation in Quebec.
- EmailReply within 24–48 hourshello@lucrousseau.com
- LinkedInBackground or messageView profile
Not sure which profile fits? Browse situations or take the two-question quiz.