Source-linked in-app support assistant console

For

Product and support teams running in-app help for a software product

Solves

Users stall inside the product and support answers arrive without the interface state or steps already tried.

Delivers

Source-linked in-app guidance with a support handoff

Built in

about 4 weeks of creation time, MVP in 4 days

Investment

$13,500 for the MVP, $46,000 for the full product

Run it

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

Reduce repeated support contacts while keeping answers tied to approved sources.

1. Answer user questions in real time inside the product.
2. Show guidance in the interface where the task happens.
3. Run live UI walkthroughs that highlight the next element.
4. Learn the product from approved sources without manual scripting.
5. Ground answers in documentation, trained workflows and current interface state.
6. Tailor help to the user's actions and progress.
7. Accept voice and text input and return voice output.
8. Detect UI changes and flag them for admin review.
9. Hand off to support with transcript and steps tried.
10. Match the product's branding and style.
11. Report interactions in an analytics dashboard.
12. Support multiple languages.
13. Generate help articles and release notes from approved material.
14. Offer templates for common help formats.
15. Check grammar and spelling in generated text.
16. Suggest edits while an admin writes.
17. Let several admins work on the same guidance set.
18. Expose an SDK for new actions and tools.

Everything these tools do, in one app

• Real-time Q&A Answers user questions instantly as they arise.Found in HelpBar.ai, Frigade AI, NINA
• In-app guidance Provides assistance directly within the software interface.Found in Frigade AI, NINA
• Live UI walkthroughs Highlights and points to UI elements in real time to guide users step by step.Found in NINA
• Automatic product learning Learns the product automatically without manual setup or ongoing engineering.Found in Frigade AI
• Multi-source grounding Draws answers from approved documentation, trained workflows, and the current interface state.Found in NINA
• Personalized assistance Tailors help based on individual user actions and progress.Found in Frigade AI
• Voice and text input Allows users to speak or type requests and receive voice output.Found in NINA
• UI change detection Flags interface updates for admin review and adjusts guidance paths accordingly.Found in NINA
• Handoff with context Sends full transcript and steps tried to support team when unable to complete a request.Found in NINA
• Customizable interface Matches the branding and style of your website or product.Found in HelpBar.ai
• Analytics dashboard Tracks user interactions to improve responses and support strategies.Found in HelpBar.ai
• Multilingual support Caters to a diverse audience by supporting multiple languages.Found in HelpBar.ai, Orango AI
• Content generation Generates various types of written content such as articles, blogs, and marketing materials.Found in Truva, Orango AI
• Customizable templates Provides templates to suit different writing needs and formats.Found in Truva, Orango AI
• Real-time grammar and spell-check Checks grammar and spelling in real time to ensure polished content.Found in Truva
• Real-time text suggestions Offers suggestions and editing assistance as you write.Found in Orango AI
• Collaboration tools Enables multiple users to work on the same project.Found in Truva
• Customizable SDK Allows creation of new actions and tools for extended functionality.Found in Frigade AI

What goes in, what comes out

The workflow

1. In
   Start with

   Approved documentation, trained workflows and the current interface state

2. 1

   Confirm the buyer's problem and scope

3. 2

   Collect approved documentation

4. 3

   Trained workflows and the current interface state

5. 4

   Then follow this sequence: 1

6. Out
   Finish with

   Source-linked in-app guidance with a support handoff

AI does the heavy lifting, people stay in charge

Use AI to interpret permitted inputs, suggest structured mappings and generate candidate answers and walkthrough steps for the stated task modules. Use deterministic code for source lookup, schema validation, hard constraints and reproducible tests. Review source-linked explanations and uncertainty before accepting results. One product surface and one approved source set; final policy answers and account actions remain human. A model suggestion is never a verified fact, professional decision or authorization to act.

What your team sees

Primary screens: Assistant setup and sources, Live in-app assistant, Admin console and handoff queue. Use a source list with approval states, a central conversation view showing the cited source and interface step, and a right-hand panel for user context, language and escalation. Let admins compare guidance paths side by side after a UI change. Display draft, changes requested and approved states for answers and walkthroughs. Provide a support handoff view with transcript and steps tried. Make the task-specific outcome source-linked in-app guidance with a support handoff visible beside its evidence, review state and value baseline.

Accounts and administration

Project ownership, source versions, answer approval states, language settings, usage allowances, handoff 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 documentation, help center, ticketing and identity systems. Cloud storage, analytics and messaging 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

   4 days

   One buyer segment, one recurring use case; first modules: answer user questions in real time inside the product; show guidance in the interface where the task happens. Manual review in the loop. Built by our AI software factory.

3. 3

   Paid pilot

   5 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 and support teams running in-app help for a software product use it to solve "users stall inside the product and support answers arrive without the interface state or steps already tried"?
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: Resolved in-app questions per support hour and handoffs completed with usable context.
4. Measure, then decide. Track resolved in-app questions per support hour and handoffs completed with usable context; accepted-answer 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 source set; final policy answers and account actions remain human. Implement one approved input format, a bounded representative case set and the first two task modules: answer user questions in real time inside the product; show guidance in the interface where the task happens. Support the third module with operator review: run live UI walkthroughs that highlight the next element. 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 source-linked in-app guidance with a support handoff. Retain the explicit scope boundary: One product surface and one approved source set; final policy answers and account actions remain human.

What the build depends on. Source upload and preview, asynchronous answer jobs, editable version history, reviewer access and tested export formats. High-fidelity in-app guidance requires access to the product interface and specialist QA. Obtain representative authorized cases, baseline measurements, qualified reviewers and a buyer-side decision owner. Specific limitation: One product surface and one approved source set; final policy answers and account actions 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 support allowance after repeat demand. Quote complex multi-product or regulated deployments separately. These are test prices, not market benchmarks. Package the initial sale as one bounded source-linked in-app guidance with a support handoff. 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

Reduce repeated support contacts while keeping answers tied to approved sources. Demonstrate a concrete source-linked in-app guidance with a support handoff using the buyer's approved example and show the baseline, corrections and actual delivery effort.

Where to find buyers

Product and support teams running in-app help for a software product professional communities; specialist consultants serving this buyer; permissioned partner introductions; practical demonstrations at relevant trade or practitioner events.

Lead magnet

A reviewed sample source-linked in-app guidance with a support handoff from a small authorized input set, with a transparent calculation of resolved in-app questions per support hour and handoffs completed with usable context and no promised savings.

The first 30 days

1. Week 1: interview five product and support teams running in-app help for a software product and inspect a recent example of users stall inside the product and support answers arrive without the interface state or steps already tried.
2. Week 2: prepare a consented or synthetic demonstration of the three task modules.
3. Week 3: seek one bounded paid pilot with agreed baseline and acceptance criteria.
4. Week 4: measure resolved in-app questions per support hour and handoffs completed with usable context, 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: Resolved in-app questions per support hour and handoffs completed with usable context. 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

Resolved in-app questions per support hour and handoffs completed with usable context; accepted-answer rate; material error rate; reviewer correction time; actual repeat purchase.

Retention and expansion

Repeat the workflow when the buyer again needs source-linked in-app guidance with a support handoff. 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 answers, interface steps and review examples, together with reliable delivery for a narrow product-support niche. Build a permissioned library of representative task cases, reviewer corrections and verified operating constraints for product and support teams running in-app help for a software product. Repeatable delivery and useful integrations matter more than access to a base model.

Alternatives and positioning

Truva, HelpBar.ai, Orango AI, Frigade AI and NINA, plus the buyer's present mix of help center, live chat and manual walkthroughs. Compare this product with the buyer's present method on resolved in-app questions per support hour and handoffs completed with usable context. Offer a bounded paid workflow instead of claiming broad autonomous expertise. Market uniqueness and competitor coverage are not verified.

Main delivery costs

Model calls, voice processing, storage, reviewer hours, source preparation and support handoff handling. Additional initial validation requires representative authorized sample preparation, buyer interviews, buyer-side evaluation and bounded validation of source-linked in-app guidance with a support handoff. Track cost per resolved question, including correction work, unsuccessful cases and support.

Preserve source attribution, answer accuracy and usage permissions. Named reviewers approve policy answers and account actions. One product surface and one approved source set; final policy answers and account actions remain human. Keep all consequential actions under authorized human control and do not fabricate missing inputs, permissions, professional judgments or market evidence.