Service
Fractional CAIO for B2B SaaS
Senior AI leadership for B2B SaaS founders and CTOs. AI feature strategy, build-vs-buy decisions, RAG architecture, LLM cost optimization, AI governance, model selection. For Seed-Series B SaaS launching AI features that actually ship, not chatbot demos.
À qui cela s'adresse
- B2B SaaS CTOs at Seed-Series B whose engineering team can ship code but lacks AI/ML strategy depth
- Founders launching AI features who keep getting "almost-shipped" pilots that never reach production
- PLG SaaS companies adding AI to onboarding, content generation, or workflow automation features
- Vertical SaaS operators (legal-tech, HR-tech, sales-tech) where domain-specific AI is the next moat
- PE-backed SaaS rollups deploying AI features consistently across portfolio companies
- SaaS founders preparing for Series B / C where investors want a defensible AI story
Ce qui est inclus
- AI feature roadmap: prioritisation framework, build-vs-buy decisions, sequencing aligned with funding milestones
- RAG architecture for product features: knowledge base over your customers' data, citation-grounded outputs, refusal scoring
- LLM cost optimization: prompt engineering for token efficiency, caching strategies, eval-driven model downgrades (GPT-4 → GPT-4o-mini where quality allows)
- Model selection: OpenAI vs Anthropic vs Google Gemini vs Mistral vs open-weight (Llama, Qwen), ongoing benchmarks for your use cases
- AI eval frameworks: offline benchmarks, production monitoring, A/B testing of model swaps without customer disruption
- AI governance: audit logs, refusal scoring, customer data handling, EU AI Act risk-tier classification for your features
- AI talent strategy: when to hire ML engineers vs senior platform engineers vs application engineers, contractor vs FTE
- AI-feature go-to-market: positioning, pricing, packaging, enterprise procurement readiness for AI features
Comment nous collaborons
- 1
SaaS AI audit (1-2 weeks)
Audit your existing AI usage, current LLM spend, customer-facing AI features, eval infrastructure, model risk posture. Deliverable: prioritised AI roadmap and 90-day action plan.
- 2
Engagement start
Embedded with CTO + product within 1-2 weeks. Weekly tech-syncs, bi-weekly product review with CPO/CEO, monthly board-pack contributions on AI traction.
- 3
Ongoing cadence
3-5 days per month, scaling with AI product velocity. Peak load around major AI feature launches and quarterly board reviews.
- 4
Quarterly reviews
Every 90 days: AI feature traction, LLM spend trends, eval results, customer-feedback on AI features, talent gaps. Course-correct explicitly.
- 5
Handover
When you hire a Head of AI / Director of ML, clean handover with all eval frameworks, vendor contracts, decision logs, and team relationships intact.
Résultats attendus
- AI feature ship rate doubled; pilot-to-production gap closed via better eval frameworks and architecture
- 30-50% reduction in LLM spend via prompt engineering, caching, and eval-driven model downgrades
- A defensible AI roadmap your board and investors can scrutinise
- RAG architecture with citation accuracy + refusal scoring for any customer-facing AI feature
- Production AI eval infrastructure (offline + online) catching regressions before customers do
- EU AI Act risk-tier classification and documentation for each AI feature
- Build-vs-buy framework applied to every AI component (typical: 30-50% cost savings vs. buying everything)
- AI feature pricing and packaging that captures value (vs. giving AI away as a free feature)
- Hiring plan for the 1-2 ML engineers you actually need (vs. the 5 you don't)
- Due-diligence-ready AI documentation for Series B / C fundraise or strategic acquisition
Questions fréquentes
How do we know if we need a CAIO vs just adding ML engineers?
Ask: do we have an AI strategy beyond "let's add ChatGPT to our app"? If the answer is no, you need a CAIO before you need ML engineers. ML engineers without a strategy build clever components that don't ship as products. A CAIO sets the strategy that makes ML hiring effective.
Our engineers can do AI work. Why do we need senior AI leadership?
Engineers can implement; they don't typically own AI strategy, eval frameworks, model risk, build-vs-buy decisions, vendor negotiations, or board-level AI narratives. The CAIO does these so engineers can focus on execution. Same reason you have a CTO and a VP Eng, not just senior engineers.
We're at Seed stage. Too early?
Probably yes, for full-engagement Fractional CAIO. At Seed, what you usually need is a 2-week AI readiness sprint to validate your AI feature concept and architecture. That keeps you from spending 6 months building the wrong AI MVP. Discovery sprints at this stage are typically the right scope.
What about LLM cost: we're spending too much on OpenAI?
Very common at Series A. Typical drivers: oversized models for routine tasks (GPT-4 where GPT-4o-mini is fine), no caching layer, no prompt engineering for token efficiency, no eval-driven model downgrades. A CAIO engagement typically pays for itself in 2-4 months on LLM cost optimization alone, before accounting for other value.
Can you help with AI feature pricing and packaging?
Yes. AI feature monetization is currently an underbuilt muscle in most SaaS teams. Common mistake: shipping AI features as part of an existing plan (vs. as a value-priced upgrade). Result: 30-50% margin compression. CAIO engagements include AI go-to-market: positioning, pricing tier design, enterprise procurement readiness.
Will you help us hire ML talent?
Yes. Job-spec design, interview process, technical screening, reference checks, offer negotiation. Just as importantly: when NOT to hire ML talent, and when to use contractors or vendor partners instead. Most Seed-Series A SaaS over-hires ML; we'll be honest about right-sizing.
How do we measure if the CAIO engagement is working?
Three metrics: (1) AI feature ship-rate, features going pilot-to-production per quarter; (2) LLM cost per active user, trending down; (3) Eval pass-rate on production AI, trending up. Reviewed every 90 days. If none of these move in 6 months, the engagement isn't working and we'll have an honest conversation about whether to continue.
Parlons de votre projet
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