A short intake form feeds an AI that generates a tailored plan, report, or account setup grounded in your playbook — delivered in-app, by PDF, or email on day one.
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monthly retainer
Who this is for
You run product or growth at a SaaS or services company. New-user activation is stuck below 35% and the culprit is obvious: generic onboarding that ignores everything the user just told you at signup. Building a real AI onboarding personalization engine from scratch would cost $100k+ and six months. This retainer skips that.
The pain today
- Activation rate below 35% despite a polished signup flow
- Users answered 8 questions at signup — the app ignored all of it
- Generic welcome checklist is the same for a solo freelancer and a 200-person ops team
- Typeform → spreadsheet → human-drafted plan doesn't scale past 50 new signups per week
- Users churn in week one because they never reach their first 'aha' moment
The outcome you get
- Questionnaire answers map to a personalized plan in real time — no human in the loop
- Every recommendation traces back to your playbook, not generic AI advice
- Output delivered in-app, as a PDF, and/or as a drip email sequence
- A/B-tested activation lift tracked from day one of rollout
- Analytics on which input combinations produce the highest downstream retention
Why activation stalls at the questionnaire
AI onboarding personalization lives or dies on input quality. Most SaaS onboarding collects user data and then ignores it — the same checklist loads regardless of what the user said. That's the gap I close.
The AI questionnaire pipeline has three moving parts: the input schema (what you ask and why), the generation layer (how answers map to recommendations), and the delivery pattern (how output reaches the user). Each part has a way to break. A poorly designed schema produces generic output even with a perfect LLM. A perfect schema with a hallucinating LLM produces confident nonsense. Good generation with a buried delivery pattern means the user never reads the plan.
I scope all three in the first two weeks — usually with one to two sessions with your product and CS leads to understand where current onboarding fails and what a 'perfect first week' looks like for your best-fit users.
Questionnaire answers map to a personalized plan in real time — no human in the loop
Input schema: 8 questions beats 30
The sweet spot is 8 to 12 questions. Below that, the output lacks enough signal to differentiate. Above that, completion rates drop and the quality gain flattens.
The questions that matter are the ones whose answers actually change the recommendation. For a project management SaaS: team size, primary use case, current tool, and top goal for the next 90 days. For a coaching platform: experience level, specific objective, available time per week, and biggest past obstacle. Each question earns its place by forking the output in a meaningful way.
I workshop the input schema with your team in week one. The first draft usually has 20 questions; we cut to the 10 that carry the most differentiation weight. That pruning conversation also doubles as playbook documentation — it forces your team to articulate which user signals actually predict success.
30+: Active users.
Grounding the LLM in your playbook
A model with no context about your product generates plausible-sounding plans that your CS team would never recommend. The fix is retrieval-augmented generation: the pipeline retrieves the right sections of your documented playbook based on the user's questionnaire answers, then the LLM fills in specifics and generates a coherent output.
Playbook formalization takes 2 to 3 weeks. I interview your product, CS, and CX leads, review your best-performing customer cohorts, and extract the implicit recommendations into explicit rules. The result is a structured knowledge base — recommended paths by user profile (industry × company size × primary goal), templated plan sections, and reference case examples.
Hallucination testing runs before any production traffic: 50+ synthetic test personas routed through the pipeline, outputs reviewed against what your team would actually recommend. Confidence thresholds route low-confidence outputs to a human CS review queue rather than delivering them directly. For high-ticket products where a wrong recommendation costs more than the latency, that routing is worth it.
Delivery patterns that users actually read
Three patterns, often combined. In-app: the personalized plan loads on the user's first dashboard visit — task list pre-populated, suggested integrations highlighted, next steps ordered by their stated priority. PDF: a branded report the user can share internally to get team buy-in. Email drip: each message focuses on one plan step, sent over the user's first 7 to 14 days.
The combination that tends to move activation most is in-app for immediate engagement plus email drip for reinforcement. PDF adds value for B2B products where the buying champion needs to sell the tool internally.
Interactive elements help retention beyond the initial plan: the user marks items complete, the plan surface updates, and the system logs which items get skipped — that signal feeds back into playbook refinement over time. Static output is a floor, not a ceiling.
What I learned building Instill
Instill is a self-initiated AI product I built and run — a prompt library that works across AI tools via MCP. Users arrive with a job to do, the system surfaces the right skills (prompts) for that job, and the user acts. Thirty-plus active users, 1,000-plus skills saved, 45-plus projects powered.
The core mechanic is identical to AI onboarding personalization: match user context to relevant content, in the right specificity, with a clear next action. Building Instill through real iteration taught me where personalization breaks in practice — output too generic (not enough signal), output too narrow (over-fitted to one user type), too many assumptions baked in before the user has established a pattern. The fixes are always in the schema and the retrieval layer, not the LLM itself.
Measuring activation lift
Personalization has to move a metric. The standard measurement is activation rate: the percentage of new signups who complete a defined set of key product actions within the first 7, 14, or 30 days.
I set up an A/B test from day one of rollout — personalized onboarding to 50% of new signups, generic to 50%, measured over a minimum of four weeks. That window is enough to see a first-cohort signal without waiting months for statistical significance.
If your signup volume is under 200 per month, I adjust to a sequential rollout with before/after comparison rather than a concurrent split. Either way, the output of the test is a clear number: activation rate before, activation rate after, and the revenue implication if the lift holds at current signup volume.
Activation lift compounds. Users who activate in week one stay longer, refer more, and upgrade at higher rates. The ROI math for personalized onboarding is strong when activation is genuinely the bottleneck — but if signups are thin, improving personalization won't move the needle. I'll say so in scoping.
Pricing and timeline
AI personalized onboarding runs on the AI Automation retainer at $3,999/mo. First version timeline: 4 to 6 weeks to workshop the playbook, finalize the input schema, wire the generation pipeline, and ship the delivery pattern to production.
The retainer continues after launch because the work isn't done at shipping. Activation data informs playbook refinement, new user segments get added, and delivery patterns iterate. LLM API costs run $50 to $500/mo depending on signup volume and output length.
14-day money-back guarantee, cancel anytime, Work Made for Hire — you own 100% of the code and playbook on exit.
Recent proof
A comparable engagement, delivered and documented.
An AI knowledge base your whole team uses via MCP
A personal library for Skills, Agents, and Rules, built once, used across Claude, Cursor, and any MCP-compatible AI tool. Save your best workflows once. Run them anywhere.
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Frequently asked questions
The questions prospects ask before they book.
Most teams haven't. The formalization process is structured interviews with your product, CS, and CX leads — typically 3 to 5 sessions — where I surface the implicit rules your best humans already follow. Past successful-customer cohorts are a gold mine: patterns in what those users did in week one tend to reveal exactly which inputs predict activation. Your team reviews and refines the draft before it feeds the LLM pipeline.
Before launch, I run 50-plus synthetic test personas through the pipeline and compare outputs to what your team would actually recommend. Low-confidence outputs route to a human CS review queue rather than delivering directly. Users can also edit their questionnaire inputs mid-flow and regenerate. For high-ticket products where a wrong recommendation has real cost, the human review gate is on by default.
First production version ships 4 to 6 weeks after kickoff. I set up an A/B test from day one of rollout, and a first-cohort signal is usually visible within 4 weeks of live traffic. The cleaner your existing activation tracking, the faster the measurement. If you don't have activation event tracking set up, that's week-one infrastructure — it takes a few days and is worth doing regardless of this project.
Yes. Services businesses use it to draft personalized engagement plans after customer intake calls — the questionnaire happens during or right after the sales close, and the AI generates a first-week plan that reflects the specific customer context. The workflow is nearly identical to a SaaS flow but the delivery is often PDF plus email rather than in-app, since there's no product dashboard to personalize.
The existing form is a fine input layer — I can wire the pipeline directly to your Typeform, Tally, or survey tool via webhook or API. The work is in the generation and delivery layer, not replacing the form. If the existing form has the wrong questions, we redesign the schema; if it has the right ones already, we keep it and build the AI layer on top.
Yes. For healthcare onboarding with PHI in the questionnaire responses, I use HIPAA-eligible LLM infrastructure — Azure OpenAI with a signed BAA, or a self-hosted model. FINRA-compliant financial advisory content requires additional review layers that I scope per regulatory spec in week one. Regulated verticals add 1 to 2 weeks to the timeline and some infrastructure cost, but the pattern works.