Conversational intake and response library console

For

Marketing and research teams collecting structured responses from customers, leads or staff

Solves

Response data sits in several rented form, chat and survey tools, so teams cannot search it, tag it or reuse it without manual exports.

Delivers

Approved conversational intake form and tagged response library

Built in

about 4 weeks of creation time, MVP in 5 days

Investment

$14,000 for the MVP, $47,500 for the full product

Run it

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

Reduce tool subscriptions and manual data handling while keeping collected responses in one searchable library.

1. Generate forms or questions from a written objective.
2. Present questions in a chat-like conversational format.
3. Support multiple respondent languages.
4. Save partial responses for later continuation.
5. Show a progress indicator to respondents.
6. Accept voice note answers.
7. Transcribe voice responses into text.
8. Answer natural-language questions about results.
9. Edit conditional question paths by drag-and-drop.
10. Report responses in real time.
11. Offer pre-designed templates by content type.
12. Adjust tone and style of generated text.
13. Correct grammar and improve writing style.
14. Suggest live edits for clarity and engagement.
15. Export collected data to Google Sheets.
16. Train the assistant on business instructions and data.
17. Embed forms or chats into websites.
18. Categorize and tag responses automatically.
19. Search responses with filters and AI suggestions.
20. Store responses in secure cloud storage.
21. Compare the reviewed result with the recorded baseline and value assumptions.
22. Capture corrections and named-owner approval before consequential use.
23. Export a versioned approved conversational intake form and tagged response library with source references and unresolved questions.

Everything these tools do, in one app

• AI form generation Creates forms or questions automatically from a description or objective.Found in ZINQ AI, Formshare, Opinion Stage AI and 1 more
• Conversational chat interface Presents questions in a chat-like, interactive format.Found in Formless, ZINQ AI, PingPolls v1.0 and 2 more
• Multilingual support Supports multiple languages for respondents or content.Found in Formless, TheySaid 2.0, Formshare
• Progress saving Saves partial responses so respondents can continue later.Found in Formless, PingPolls v1.0
• Progress indicator Shows a progress bar to indicate completion status.Found in Formless
• Voice note input Allows respondents to answer using voice recordings.Found in PingPolls v1.0
• Automatic transcription Transcribes voice responses into text for analysis.Found in PingPolls v1.0
• AI result querying Lets users ask questions about survey results in natural language.Found in PingPolls v1.0
• Branching logic editor Configures conditional question paths via drag-and-drop.Found in PingPolls v1.0
• Real-time analytics Provides live reporting and tracking of responses.Found in Opinion Stage AI
• Customizable templates Offers pre-designed templates for various content types.Found in Aden, Opinion Stage AI
• Tone adjustment Adjusts the tone or style of generated or edited text.Found in TheySaid 2.0, Yapz
• Grammar and style corrections Automatically corrects grammar and improves writing style.Found in TheySaid 2.0, Yapz
• Real-time editing suggestions Provides live suggestions to improve clarity and engagement.Found in Aden, TheySaid 2.0
• Data export to Google Sheets Automatically sends collected data to Google Sheets.Found in Botsheets Chat
• Custom AI training Allows training the AI with specific instructions or business data.Found in Formless, Botsheets Chat
• Website integration Enables embedding forms or chats into websites.Found in Formshare, Opinion Stage AI
• Data categorization and tagging Automatically organizes and tags data for retrieval.Found in Records
• Advanced search Provides search with filters and AI-driven suggestions.Found in Records
• Secure cloud storage Stores data securely in the cloud.Found in Records

What goes in, what comes out

The workflow

1. In
   Start with

   Plain-language objective, brand tone rules and target audience

2. 1

   Confirm the buyer's problem and scope

3. 2

   Collect a plain-language objective

4. 3

   Brand tone rules and target audience

5. 4

   Then follow this sequence: 1

6. Out
   Finish with

   Approved conversational intake form and tagged response library

AI does the heavy lifting, people stay in charge

Use AI to interpret permitted inputs, suggest structured mappings and generate candidate outputs for the three stated task modules. Use deterministic code for arithmetic, schema validation, hard constraints and reproducible tests. Review source-linked explanations and uncertainty before accepting results. One approved form schema and one respondent language set; final consent, privacy and data-use checks remain human. A model suggestion is never a verified fact, professional decision or authorization to act.

What your team sees

Primary screens: Objective and audience brief, Form and chat builder, Response library and search. Use a thumbnail gallery for projects, a large central editing canvas, and a right-hand panel for branching logic, tone rules and comments. Let users compare form versions side by side. Display draft, changes requested and approved states. Provide a respondent preview link with progress saving. Make the task-specific outcome approved conversational intake form and tagged response library visible beside its evidence, review state and value baseline.

Accounts and administration

Project ownership, form versions, respondent comments, approval states, usage allowances, response 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

Customer-owned response data, authorized brand guidelines and permitted research sources. Cloud response 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: generate forms or questions from a written objective; present questions in a chat-like conversational format. 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

   2 weeks

   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 marketing and research teams collecting structured responses from customers, leads or staff use it to solve "response data sits in several rented form, chat and survey tools, so teams cannot search it, tag it or reuse it without manual exports"?
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: Completed responses per published form and reviewer hours per accepted response set.
4. Measure, then decide. Track completed responses per published form and reviewer hours per accepted response set; 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 approved form schema and one respondent language set; final consent, privacy and data-use checks remain human. Implement one approved input format, a bounded representative case set and the first two task modules: generate forms or questions from a written objective; present questions in a chat-like conversational format. Support the third module with operator review: support multiple respondent languages. 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 approved conversational intake form and tagged response library. Retain the explicit scope boundary: One approved form schema and one respondent language set; final consent, privacy and data-use checks remain human.

What the build depends on. Response upload and preview, asynchronous generation jobs, editable version history, reviewer access and tested export formats. High-fidelity production requires specialist research QA. Obtain representative authorized cases, baseline measurements, qualified reviewers and a buyer-side decision owner. Specific limitation: One approved form schema and one respondent language set; final consent, privacy and data-use checks 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 response package. Offer a monthly production allowance after repeat demand. Quote complex multilingual or voice-heavy intake separately. These are test prices, not market benchmarks. Package the initial sale as one bounded approved conversational intake form and tagged response library. 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 tool subscriptions and manual data handling while keeping collected responses in one searchable library. Demonstrate a concrete approved conversational intake form and tagged response library using the buyer's approved example and show the baseline, corrections and actual delivery effort.

Where to find buyers

Marketing and research teams collecting structured responses from customers, leads or staff professional communities; specialist consultants serving this buyer; permissioned partner introductions; practical demonstrations at relevant trade or practitioner events.

Lead magnet

A reviewed sample approved conversational intake form and tagged response library from a small authorized input set, with a transparent calculation of completed responses per published form and reviewer hours per accepted response set and no promised savings.

The first 30 days

1. Week 1: interview five marketing and research teams collecting structured responses from customers, leads or staff and inspect a recent example of response data sits in several rented form, chat and survey tools, so teams cannot search it, tag it or reuse it without manual exports.
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 completed responses per published form and reviewer hours per accepted response set, 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: Completed responses per published form and reviewer hours per accepted response set. 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

Completed responses per published form and reviewer hours per accepted response set; accepted-output rate; material error rate; reviewer correction time; actual repeat purchase.

Retention and expansion

Repeat the workflow when the buyer again needs approved conversational intake form and tagged response library. 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 form schemas, tagging rules and review examples, together with reliable delivery for a narrow research niche. Build a permissioned library of representative task cases, reviewer corrections and verified operating constraints for marketing and research teams collecting structured responses from customers, leads or staff. Repeatable delivery and useful integrations matter more than access to a base model.

Alternatives and positioning

Formless, ZINQ AI, Aden, TheySaid 2.0, PingPolls v1.0, Yapz, Formshare, Opinion Stage AI, Botsheets Chat and Records. Compare this product with the buyer's present method on completed responses per published form and reviewer hours per accepted response set. Offer a bounded paid workflow instead of claiming broad autonomous expertise. Market uniqueness and competitor coverage are not verified.

Main delivery costs

Generation attempts, transcription 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 approved conversational intake form and tagged response library. Track cost per accepted output, including correction work, unsuccessful cases and support.

Preserve respondent consent, source attribution, quotation accuracy and usage permissions. Respondents approve substantive changes and publication scope. One approved form schema and one respondent language set; final consent, privacy and data-use checks remain human. Keep all consequential actions under authorized human control and do not fabricate missing inputs, permissions, professional judgments or market evidence.