Screenshot of the Product edge-case interview planner interactive demo
Screenshot of the interactive demo, on sample data

Product edge-case interview planner

Find neglected situations without synthetic users.

Try the interactive demo Get this built for you

For
Product discovery teams
Solves
Research overfocuses on typical workflows.
Delivers
Researcher-approved edge-case study
Built in
about 4 weeks of creation time, MVP in 4 days
Investment
$12,500 for the MVP, $42,500 for the full product
Run it
Inside your business, or as part of your offer to clients
01

What it does

For product discovery teams, turn consented journey notes and declared constraints into researcher-approved edge-case study.

  1. Identify unusual contexts.
  2. Draft targeted questions.
  3. Plan consented interviews.
  4. Link proposed outputs to original source records.
  5. Capture reviewer corrections and approval.
  6. Export a versioned researcher-approved edge-case study.

What goes in, what comes out

What the customer puts in
  • Consented journey notes
  • Declared constraints

AI drafts, people review. Research evidence workspace with reviewed deliverables.

What the customer gets
  • Researcher-approved edge-case study
02

How it works

The workflow

  1. In
    Start with

    Consented journey notes and declared constraints

  2. 1

    The buyer creates a project

  3. 2

    Supplies consented journey notes and declared constraints

  4. 3

    Confirms scope and access

  5. Out
    Finish with

    Researcher-approved edge-case study

AI does the heavy lifting, people stay in charge

AI assists these bounded tasks: identify unusual contexts; draft targeted questions; plan consented interviews. Use only consented journey notes and declared constraints and preserve uncertainty in researcher-approved edge-case study. Keep model suggestions separate from verified facts. Link factual outputs to authorized input evidence and show missing information explicitly. Use deterministic checks for counts, dates, identifiers and arithmetic where applicable. A designated reviewer validates consequential outputs and signs off the delivered result.

What your team sees

Key screens: Brief and sources, Product edge-case interview planner, Review and delivery. Organize work by research question. Show a source library, an evidence matrix and a draft findings panel with linked quotations. Keep contradictory findings and unanswered questions visible. Allow reviewers to inspect the original context before accepting an interpretation. Open with brief and sources; move into product edge-case interview planner for the detailed task; finish in review and delivery for review and handoff. Show the source record, uncertainty and approval status beside each proposed output.

Accounts and administration

Source provenance, participant consent where applicable, research questions, coding definitions, reviewer disagreements, citations and versioned conclusions. Include organization-scoped access, named project owners, review queues, usage limits, export history and retention settings. Never reuse private customer material for other accounts without permission.

Integrations and data access

Product feedback, authorized interviews, usage exports and requirement records. Permitted research libraries, interview recording imports, citation exports and document editors. Preserve original source metadata throughout the workflow. Begin with uploads and exports of consented journey notes and declared constraints. Any named system or connector is a candidate requiring current access and compatibility checks; no live connection is included by default.

03

How we build it

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: identify unusual contexts; draft targeted questions. 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

    9 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 discovery teams use it to solve "research overfocuses on typical workflows"?
  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 the acceptance criteria, input limits and reviewer responsibilities before starting.
  4. Measure, then decide. Track validated edge-case findings; reviewer correction minutes; buyer acceptance and repeat purchase. Then expand, change course or stop, with evidence instead of opinions.

MVP scope for this solution. Costed pilot: One organization, one defined input format and one representative pilot batch using consented journey notes and declared constraints. Professional judgment, physical inspections, live external actions and production certification remain outside this prototype. Start with one buyer organization and a bounded set of representative inputs. Implement the first two modules: identify unusual contexts; draft targeted questions. Support the third task through an assisted review queue: plan consented interviews. Handle the remaining required functions manually until validated. Include input upload, source references, user correction, a reviewer approval step and export of researcher-approved edge-case study. Authentication, account isolation, deletion controls and basic operational logging are included. Specialized production certification, live write integrations and broader rollout are not included unless explicitly stated.

After the MVP. After paying customers repeatedly accept researcher-approved edge-case study, automate link proposed outputs to original source records; capture reviewer corrections and approval; export a versioned researcher-approved edge-case study. Add one tested read integration, reusable customer configuration and scheduled repeat delivery. Increase supported formats or teams only when evaluation cases and reviewer capacity cover the new scope. One organization, one defined input format and one representative pilot batch using consented journey notes and declared constraints. Professional judgment, physical inspections, live external actions and production certification remain outside this prototype.

What the build depends on. A clear research protocol, source access, citation tracking and qualified interpretation. Interview work also needs relevant participants and consent management. Obtain representative authorized inputs, an agreed review rubric and a buyer-side owner. Specific scope: One organization, one defined input format and one representative pilot batch using consented journey notes and declared constraints. Professional judgment, physical inspections, live external actions and production certification remain outside this prototype.

04

Investment

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

  1. Phase 1

    MVP

    One buyer segment, one recurring use case; first modules: identify unusual contexts; draft targeted questions. Manual review in the loop.

    $12,500 · about 4 days of creation time

  2. Phase 2

    Paid pilot

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

    $12,500 · about 5 days of creation time

  3. Phase 3

    Full product

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

    $17,500 · about 9 days of creation time

Indicative total, MVP to full product$42,500about 4 weeks of creation time · start with the MVP from $12,500

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.

StageHosting and infrastructureAI usageTotal per month
MVP and paid pilotabout 3 customers$30–$60$80–$160$110–$220
Full productabout 50 customers$110–$210$880–$1,750$990–$1,960
05

Run it or resell it

Internally

For your own team

Product discovery teams run it inside the business: consented journey notes and declared constraints in, researcher-approved edge-case study out, reviewed by your people.

For your clients

As part of your offer

Agencies, consultancies and software companies can offer it to their own clients under their brand. We build and maintain it; you sell and deliver it.

Your brand, or this one

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

  • primary#812791
  • accent#89c954
  • surface#efe4f1
  • ink#22201e
Headings
Manrope
Text
Manrope
Voice
Curious, rigorous, user-led
Selling it to your own clients: the go-to-market playbook

Pricing to test

Test USD 750-3,000 for one tightly bounded research question and evidence pack. Participant recruitment, specialist review and licensed data are separately scoped. Repeat tracking can become a retainer. Prices are hypotheses. For this buyer, package the first sale around prepare a sample researcher-approved edge-case study from a small authorized set of consented journey notes and declared constraints and the defined researcher-approved edge-case study. Record actual review effort before offering a recurring allowance. The commercial pilot fee is distinct from the platform development budget.

Message to test

Find neglected situations without synthetic users. Demonstrate the result with prepare a sample researcher-approved edge-case study from a small authorized set of consented journey notes and declared constraints for product discovery teams. Use a concrete before-and-after example without promising unmeasured savings.

Where to find buyers

Product management communities and UX research partners

Lead magnet

Prepare a sample researcher-approved edge-case study from a small authorized set of consented journey notes and declared constraints

The first 30 days

  1. Week 1: interview five prospective buyers from product discovery teams and inspect how they handle research overfocuses on typical workflows.
  2. Week 2: prepare prepare a sample researcher-approved edge-case study from a small authorized set of consented journey notes and declared constraints using authorized or synthetic material.
  3. Week 3: share the demonstration through product management communities and UX research partners and seek one bounded paid pilot.
  4. Week 4: measure validated edge-case findings; reviewer correction minutes; buyer acceptance and repeat purchase, review delivery effort and ask for a repeat purchase. This is a validation schedule, not a promise that the full product can be built in thirty days.

Paid pilot

Agree the acceptance criteria, input limits and reviewer responsibilities before starting. Run prepare a sample researcher-approved edge-case study from a small authorized set of consented journey notes and declared constraints and deliver researcher-approved edge-case study. Compare validated edge-case findings; reviewer correction minutes; buyer acceptance and repeat purchase with the buyer's current process on comparable cases; include corrections, missed issues and reviewer time. Seek payment and repeat use. Stop or revise the scope if data access, accuracy or unit economics fail.

Success metrics

Validated edge-case findings; reviewer correction minutes; buyer acceptance and repeat purchase

Retention and expansion

Build repeat use around researcher-approved edge-case study. Save approved configurations and review decisions with permission, revisit unresolved exceptions and show progress on validated edge-case findings; reviewer correction minutes; buyer acceptance and repeat purchase. Offer a recurring volume allowance after repeat demand; expand to adjacent tasks only when the buyer asks and delivery quality remains acceptable.

Why clients would pick it

Niche research protocols, credible researcher relationships and a rights-cleared evidence archive with consistent interpretation methods. For this concept, accumulate permissioned examples and reviewer corrections around find neglected situations without synthetic users. The durable asset is reliable task-specific execution and trusted customer configuration, not access to a general-purpose AI model.

Alternatives and positioning

Research consultants, internal analysts, literature databases and general search or summarization tools. Position this concept around find neglected situations without synthetic users. Compare it against the customer's current process on the same representative task. This is proposed differentiation; no exhaustive competitor study or uniqueness claim has been established.

Main delivery costs

Researcher time, source access, participant recruitment, transcription, evidence coding, expert review and report revisions. Initial validation additionally budgets for representative sample preparation, interviews with product discovery teams, and buyer-side review of researcher-approved edge-case study. Track model usage, storage, reviewer minutes, exception handling and customer support per accepted deliverable.

06

Safeguards

Use consented research and preserve contradictory evidence. Separate observed user behavior, proposed explanations and untested product assumptions. One organization, one defined input format and one representative pilot batch using consented journey notes and declared constraints. Professional judgment, physical inspections, live external actions and production certification remain outside this prototype. Require appropriate access and publication approval. Preserve source material, label AI drafts and make corrections traceable. Measure false positives and missed cases alongside speed.

Get this solution built

Built for you by our AI software factory, MVP in about 4 days. Tell us about your business and how you want to run it: inside your company, or as part of what you offer your clients. We reply within one working day.

More in Product Development

Bring one process you are sick of. In thirty minutes we will tell you whether it can run itself. Book a call.

© 2026 Nexibeo LimitedFounded 2017contact@nexibeo.com