Questionnaire in. Personalized plan out.
AI takes new-user input and generates a personalized plan, report, or account setup grounded in your playbook. Monthly retainer delivery.
Who this is for
Product or marketing lead at a SaaS or services company where generic onboarding produces low activation and there's no budget for a full personalization engine.
The pain today
- Activation rate under 30% on new signups
- Cookie-cutter onboarding that doesn't reflect user's stated goals
- Fully custom personalization engines cost $100k+ to build
- Typeform → Google Sheets → human-drafted plan flow doesn't scale
- Users lost in the first week because they don't see the 'why'
The outcome you get
- Questionnaire → AI-generated personalized plan in real time
- Output grounded in your playbook — not generic AI advice
- Delivery via in-app dashboard, PDF, email, or all three
- Typical 20–40% activation rate lift vs generic onboarding
- Analytics on which inputs drive which outputs
Input schema that drives useful output
Personalization quality depends on input quality. A 3-question form produces generic output; a 30-question form produces drop-off. Sweet spot: 8–12 questions covering the dimensions that actually change the recommendation. For a project management SaaS: team size, industry, primary use case, current tool, goals for Q1, blockers. For a fitness app: fitness level, goals (strength/endurance/mobility), available time, access to equipment, injuries. Each question chosen because its answer meaningfully changes output. I workshop the input schema with your team in week 1 — usually 2–3 rounds before we land on the set.
LLM grounding on your playbook
Generic AI output is worthless. The personalization has to reflect your team's expertise. Playbook formalization: documented recommendations per user profile (industry × company size × goal combinations). Content library: templated plan sections, example case studies, recommended integrations. LLM pipeline: retrieves relevant playbook sections based on questionnaire, fills in user specifics, generates coherent plan. Citation requirement: every recommendation traces to a playbook entry (internally tracked, not user-facing). Hallucination testing: run 50+ test personas through pipeline, verify outputs match what your team would recommend.
Delivery patterns
Three delivery modes, often used together. In-app: personalized dashboard with plan visible on first login, task checklist pre-populated, suggested next steps. PDF: branded report the user can share with their team. Email: delivered as a sequence over days/weeks, each email focusing on one plan step. Best onboarding combines all three — in-app for interactive engagement, PDF for shareability, email for drip reinforcement. Interactive elements (user marks plan items complete, plan adapts) increase engagement vs static output.
Case study: Instill
Instill is my self-initiated AI product — a prompt library that works with every AI tool. Users browse, save, and execute skills (prompts) relevant to their work. 30+ active users, 1,000+ skills saved, 45+ projects powered. The mechanics of matching user context to relevant content — the core of personalized onboarding — are identical. Users come with a goal, the system surfaces the right content, the user acts. Operating Instill through iteration has taught me where personalization breaks (too generic, too narrow, too many assumptions) and where it works (right level of specificity, trusted sources, clear next action).
Pricing
AI personalized onboarding fits the AI Automation retainer at $3,000/mo. First-version timeline: 4–6 weeks to workshop playbook, wire questionnaire, train generation, ship delivery pattern. Retainer continues through playbook refinement and delivery-mode iteration — activation metrics inform ongoing tuning. 14-day money-back, cancel anytime, Work Made for Hire. LLM API costs typically $50–500/mo depending on signup volume.
Measuring activation lift
Personalization has to move a business metric. Standard measurement: compare activation rate (percent of new signups completing key product actions in first 7/14/30 days) between personalized and generic onboarding cohorts. A/B test for 4 weeks minimum to collect significant data. Typical lift on well-designed personalized onboarding: 20–40% activation rate increase vs generic. Activation lift compounds — users who activate stay, refer, upgrade. The ROI math for personalization is strong when the signup funnel is already the business bottleneck. If signups are the bottleneck (not activation), personalization isn't the answer; lead gen is.
Recent proof
A comparable engagement, delivered and documented.
A prompt library that works with every AI tool
A home for your best AI prompts. Save them once, then use them in Claude, Cursor, or any AI tool you work with. No more copy-paste.
Frequently asked questions
The questions prospects ask before they book.
- How do I formalize my playbook?
- I interview your product/CS/CX leads, review past customer examples of successful onboarding, and synthesize the implicit playbook into explicit rules. Typical playbook formalization takes 2–3 weeks. Your team reviews and refines. The playbook becomes a living document that evolves with product and customer changes.
- What if the AI gets the personalization wrong?
- Output quality tested against 50+ test personas before production. User can edit their onboarding inputs mid-flow and regenerate. Low-confidence recommendations can be flagged for human CS review before delivery (for high-ticket products where wrong advice costs more than personalization latency).
- Can users edit their personalized output?
- Yes — the output is editable, not immutable. User can remove tasks that don't apply, add custom ones, change priorities. The AI-generated plan is a strong starting point, not a rigid contract. Editing feedback loops into playbook refinement.
- Does it work for services businesses, not just SaaS?
- Yes. Services businesses use it to draft personalized engagement plans for new clients after intake calls. Plans reflect the specific client context rather than a generic template. Workflow similar to AI proposal generation but focused on the onboarding phase rather than the sales close.
- What about HIPAA or regulated onboarding?
- Healthcare and finance onboarding have specific regulatory requirements. HIPAA-eligible LLM infrastructure (Azure OpenAI with BAA, or self-hosted models) supported. FINRA-compliant advisory content requires additional layers. I scope per regulatory spec in week 1.
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