Source-linked onboarding and feature adoption assistant

For

Product marketing and customer success teams guiding users through onboarding and feature adoption

Solves

Users stall during onboarding and feature adoption because guidance is scattered across tools and no one sees where each user actually gets stuck.

Delivers

Reviewed guidance and adoption actions linked to evidence

Built in

about 4 weeks of creation time, MVP in 5 days

Investment

$14,500 for the MVP, $49,500 for the full product

Run it

Inside your business, or as part of your offer to clients

Guide users through product onboarding and feature adoption with one owned assistant instead of several rented subscriptions.

1. Provide real-time spoken guidance through tasks.
2. Read the user's screen to understand context.
3. Automate clicks and multi-step browser tasks.
4. Handle branching conversational support.
5. Keep agenda and context across a full session.
6. Deploy with zero-code embed or training materials.
7. Record sessions and surface struggle points.
8. Operate at any hour across time zones.
9. Assist in multiple languages with switching.
10. Enforce configurable guardrails and guide-only mode.
11. Accept custom knowledge bases and transcripts.
12. Update coverage from each session's edge cases.
13. Provide a stop button to halt agent actions.
14. Offer SDKs and pre-built UI templates.
15. Personalize journeys and segment audiences.
16. Build and deploy custom campaign agents.
17. Connect to marketing and analytics tools.
18. Combine warehouse and product analytics per account.
19. Draft contextual guidance from actual behavior.
20. Require human approval before sending messages.
21. Measure feature adoption after each nudge.
22. Compare the reviewed result with the recorded baseline and value assumptions.
23. Capture corrections and named-owner approval before consequential use.
24. Export a versioned reviewed guidance and adoption actions linked to evidence with source references and unresolved questions.

Everything these tools do, in one app

• Real-time voice guidance Provides spoken instructions to walk users through tasks in real time.Found in Obi, Skippr AI
• On-screen awareness Sees the user's screen to understand context and guide them accurately.Found in Obi, Skippr AI
• Browser automation Clicks, navigates, and completes multi-step tasks directly in the user interface.Found in Skippr AI
• Conversational support Handles multi-step and branching workflows, adapting to user intent and questions.Found in Obi
• Session memory Keeps agenda and context across a full session so information doesn't reset.Found in Skippr AI
• Zero-code setup Deploys quickly without coding, using existing training materials or simple embed code.Found in Obi, Skippr AI, Quest
• Session recordings and analytics Records sessions and provides insights on where users struggle and what helps them succeed.Found in Obi
• 24/7 availability Provides assistance at any hour, supporting users across time zones.Found in Obi
• Multilingual support Assists users in multiple languages, with real-time switching in some tools.Found in Obi, Skippr AI
• Configurable guardrails Allows setting restrictions on agent actions, such as requiring approval or guide-only mode.Found in Skippr AI
• Knowledge base uploads Accepts custom knowledge bases and transcripts to improve agent accuracy.Found in Skippr AI
• Learning loop Updates from every session so new edge cases are covered for future users automatically.Found in Skippr AI
• Stop button Provides a control to halt the agent's actions at any time.Found in Skippr AI
• SDKs and UI templates Offers software development kits and pre-built UI components for embedding engagement features.Found in Quest
• AI-powered insights Uses AI to personalize user journeys and segment audiences for tailored experiences.Found in Quest
• Custom AI agents Enables building and deploying AI agents for campaign execution and lifecycle management.Found in Quest
• Seamless integrations Connects with over 75 marketing and analytics tools.Found in Quest
• Unified account understanding Combines warehouse data and product analytics to build a behavioral picture of each account.Found in Userlens
• Contextual message drafting Writes guidance tailored to an individual user's actual product behavior, following set parameters.Found in Userlens
• Human approval gate Requires manual review and approval before any message is sent to a user.Found in Userlens
• Behavior-change measurement Measures whether users adopted the target feature after receiving a nudge.Found in Userlens

What goes in, what comes out

The workflow

1. In
   Start with

   Product knowledge, session context and account behavior

2. 1

   Confirm the buyer's problem and scope

3. 2

   Collect product knowledge

4. 3

   Session context and account behavior

5. 4

   Then follow this sequence: 1

6. Out
   Finish with

   Reviewed guidance and adoption actions linked to evidence

AI does the heavy lifting, people stay in charge

Use AI to interpret permitted inputs, suggest structured mappings and generate candidate outputs for the stated task modules. Use deterministic code for arithmetic, schema validation, hard constraints and reproducible tests. Review source-linked explanations and uncertainty before accepting results. Guardrails, approval gates and the stop button remain under operator control; final adoption decisions and user messaging remain human. A model suggestion is never a verified fact, professional decision or authorization to act.

What your team sees

Primary screens: Knowledge and guardrail setup, Live session console, Adoption review and delivery. Use a session list for accounts, a large central live view of the user's screen and agent actions, and a right-hand panel for knowledge sources, guardrails and comments. Let users compare guided and unguided sessions side by side. Display draft, changes requested and approved states. Provide a client preview link with comments anchored to the relevant session step. Make the task-specific outcome reviewed guidance and adoption actions linked to evidence visible beside its evidence, review state and value baseline.

Accounts and administration

Project ownership, asset versions, client comments, approval states, usage allowances, revision limits, download history and a rights record for supplied material. Add organization access boundaries, named reviewers, usage caps, data retention controls, export logs and explicit approval for external actions.

Integrations and data access

Product analytics, warehouse data, help-desk and marketing tools. Cloud asset storage, design-file import/export and publishing destinations. Start with file exchange and validate destination specifications before promising direct publishing. Start with authorized file exchange. Validate current provider access, usage rights and schema behavior before promising a connector.

We build with our own AI software development factory, so most implementations take days to a few weeks of creation time, not months. You see working software at every step, and exact timing depends on availability.

1. 1

   Scoping call

   Day 1

   Thirty minutes on your process, your data and how you want to run it: for your own team, or for your clients. You get a fixed scope and price for the MVP.

2. 2

   MVP

   5 days

   One buyer segment, one recurring use case; first modules: provide real-time spoken guidance through tasks; read the user's screen to understand context. Manual review in the loop. Built by our AI software factory.

3. 3

   Paid pilot

   6 days

   Accounts, roles, review states, audit trail and the first integration, hardened for two to three paying pilot customers.

4. 4

   Full product

   10 days

   Self-serve onboarding, billing, monitoring and the wider integration set.

5. 5

   Run and improve

   Monthly

   We host, monitor and improve it for a fixed monthly fee, or hand it over to your team. How the retainer works.

Why we start with an MVP

An MVP, or minimum viable product, is the smallest version that your users can actually work with. It is not a cheap version of the full solution. It is a test, built to answer the questions that decide whether the rest is worth building.

1. Pick the riskiest assumption. Here: will product marketing and customer success teams guiding users through onboarding and feature adoption use it to solve "users stall during onboarding and feature adoption because guidance is scattered across tools and no one sees where each user actually gets stuck"?
2. Build only what tests it. One team, one use case, a few core modules. People do the rest by hand for now.
3. Run a paid pilot. Agree quality and outcome thresholds before the pilot using this measure: Adopted-feature rate per guided session and support contacts after guidance.
4. Measure, then decide. Track adopted-feature rate per guided session and support contacts after guidance; accepted-output rate; material error rate; reviewer correction time; actual repeat purchase. Then expand, change course or stop, with evidence instead of opinions.

MVP scope for this solution. Pilot scope: One product surface and one approved knowledge set; final adoption decisions and user messaging remain human. Implement one approved input format, a bounded representative case set and the first two task modules: provide real-time spoken guidance through tasks; read the user's screen to understand context. Support the remaining modules with operator review: automate clicks and multi-step browser tasks; handle branching conversational support; keep agenda and context across a full session; deploy with zero-code embed or training materials; record sessions and surface struggle points; operate at any hour across time zones; assist in multiple languages with switching; enforce configurable guardrails and guide-only mode; accept custom knowledge bases and transcripts; update coverage from each session's edge cases; provide a stop button to halt agent actions; offer SDKs and pre-built UI templates; personalize journeys and segment audiences; build and deploy custom campaign agents; connect to marketing and analytics tools; combine warehouse and product analytics per account; draft contextual guidance from actual behavior; require human approval before sending messages; measure feature adoption after each nudge. Include source references, corrections, basic organization access, approval states, export and value measurement. Use managed operator assistance for unresolved exceptions. The cost estimate covers this narrow prototype, not unrestricted multi-tenant scale, complex production integrations, specialist certification or physical operations.

After the MVP. Once paid pilots prove usefulness, automate repeatable reviewed steps and add one verified source integration. Expand supported inputs and case volume only after new evaluation cases pass. Build reusable customer configurations and recurring value reports around reviewed guidance and adoption actions linked to evidence. Retain the explicit scope boundary: One product surface and one approved knowledge set; final adoption decisions and user messaging remain human.

What the build depends on. Asset upload and preview, asynchronous generation jobs, editable version history, reviewer access and tested export formats. High-fidelity production requires specialist creative QA. Obtain representative authorized cases, baseline measurements, qualified reviewers and a buyer-side decision owner. Specific limitation: One product surface and one approved knowledge set; final adoption decisions and user messaging remain human.

A planning range to start the conversation, not a quote. You pay per phase, so you can stop after the MVP.

Running costs per month

A rough indication of monthly hosting and AI model costs once it is live, not tested. Real costs depend on usage, file sizes and the models chosen.

Your brand, or this one

Run it under your own brand, or start from this concept style.

Selling it to your own clients: the go-to-market playbook

Pricing to test

Test a USD 300-1,500 fixed pilot for one defined product surface. Offer a monthly production allowance after repeat demand. Quote complex multi-product or multilingual rollouts separately. These are test prices, not market benchmarks. Package the initial sale as one bounded reviewed guidance and adoption actions linked to evidence. Recurring fees must specify volume, review depth and integration support. For exchanges, test a disclosed coordination or successful-service fee rather than holding customer funds. Reprice only after measuring real delivery labor; platform-build cost is separate from a commercial pilot fee.

Message to test

Guide users through product onboarding and feature adoption with one owned assistant instead of several rented subscriptions. Demonstrate a concrete reviewed guidance and adoption actions linked to evidence using the buyer's approved example and show the baseline, corrections and actual delivery effort.

Where to find buyers

Product marketing and customer success teams professional communities; specialist consultants serving this buyer; permissioned partner introductions; practical demonstrations at relevant trade or practitioner events.

Lead magnet

A reviewed sample reviewed guidance and adoption actions linked to evidence from a small authorized input set, with a transparent calculation of adopted-feature rate per guided session and support contacts after guidance and no promised savings.

The first 30 days

1. Week 1: interview five product marketing and customer success teams guiding users through onboarding and feature adoption and inspect a recent example of users stall during onboarding and feature adoption because guidance is scattered across tools and no one sees where each user actually gets stuck.
2. Week 2: prepare a consented or synthetic demonstration of the task modules.
3. Week 3: seek one bounded paid pilot with agreed baseline and acceptance criteria.
4. Week 4: measure adopted-feature rate per guided session and support contacts after guidance, reviewer effort and repeat-purchase interest. This is a demand-validation plan, not a thirty-day full-product delivery promise.

Paid pilot

Agree quality and outcome thresholds before the pilot using this measure: Adopted-feature rate per guided session and support contacts after guidance. Continue only if the buyer accepts the actual output, the intended job outcome improves without unacceptable errors, and measured delivery cost fits willingness to pay. Revise or stop if access is unavailable, qualified review cannot be provided, or apparent savings disappear after corrections and support. Use held-out cases when comparing model quality; use a properly reviewed comparison design before making causal claims. Record missing cases and negative results alongside successful outputs.

Success metrics

Adopted-feature rate per guided session and support contacts after guidance; accepted-output rate; material error rate; reviewer correction time; actual repeat purchase.

Retention and expansion

Repeat the workflow when the buyer again needs reviewed guidance and adoption actions linked to evidence. Retain permissioned settings and reviewed examples, report realized value honestly, and sell increased volume or adjacent approved workflows only after contribution margin and quality remain acceptable.

Why clients would pick it

A reusable library of approved guidance, guardrail settings and review examples, together with reliable delivery for a narrow onboarding niche. Build a permissioned library of representative task cases, reviewer corrections and verified operating constraints for product marketing and customer success teams guiding users through onboarding and feature adoption. Repeatable delivery and useful integrations matter more than access to a base model.

Alternatives and positioning

Obi, Skippr AI, Quest and Userlens, plus generic screen-recording and help-desk tools. Compare this product with the buyer's present method on adopted-feature rate per guided session and support contacts after guidance. Offer a bounded paid workflow instead of claiming broad autonomous expertise. Market uniqueness and competitor coverage are not verified.

Main delivery costs

Session processing, storage, reviewer hours, client revision rounds and licensed source assets. Additional initial validation requires representative authorized sample preparation, buyer interviews, buyer-side evaluation and bounded validation of reviewed guidance and adoption actions linked to evidence. Track cost per accepted output, including correction work, unsuccessful cases and support.

Preserve user consent, source attribution, message accuracy and usage permissions. Operators approve substantive guidance and messaging scope. One product surface and one approved knowledge set; final adoption decisions and user messaging remain human. Keep all consequential actions under authorized human control and do not fabricate missing inputs, permissions, professional judgments or market evidence.