
User preference portability designer
Explicit cross-device preference expectations.
- For
- Multi-device product teams
- Solves
- Preferences behave inconsistently across devices.
- Delivers
- Reviewed preference behavior specification
- Built in
- about 4 weeks of creation time, MVP in 4 days
- Investment
- $13,000 for the MVP, $44,000 for the full product
- Run it
- Inside your business, or as part of your offer to clients
What it does
For multi-device product teams, turn approved settings definitions and device journeys into reviewed preference behavior specification.
- Map preference ownership.
- Compare sync rules.
- Flag conflicting states.
- Link proposed outputs to original source records.
- Capture reviewer corrections and approval.
- Export a versioned reviewed preference behavior specification.
What goes in, what comes out
- Approved settings definitions
- Device journeys
AI drafts, people review. Assumption-driven planning and decision workspace.
- Reviewed preference behavior specification
How it works
The workflow
- InStart with
Approved settings definitions and device journeys
- 1
The buyer creates a project
- 2
Supplies approved settings definitions and device journeys
- 3
Confirms scope and access
- OutFinish with
Reviewed preference behavior specification
AI does the heavy lifting, people stay in charge
AI assists these bounded tasks: map preference ownership; compare sync rules; flag conflicting states. Use only approved settings definitions and device journeys and preserve uncertainty in reviewed preference behavior specification. 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, User preference portability designer, Review and delivery. Place editable drivers and constraints beside a clearly labeled scenario output. Include a baseline view, comparison chart or schedule, and an assumptions history. Let users trace a proposed quantity or date back to its inputs. Keep forecasts distinct from actual results. Open with brief and sources; move into user preference portability designer 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
Scenario versions, baseline reconciliation, constraint checks, assumption ownership, reviewer approvals, plan exports and actual-versus-plan tracking. 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. Read-only operational exports, calendars and finance or inventory records as relevant. Start with plan exports and retain human approval for execution. Begin with uploads and exports of approved settings definitions and device journeys. Any named system or connector is a candidate requiring current access and compatibility checks; no live connection is included by default.
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
Scoping call
Day 1Thirty 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
MVP
4 daysOne buyer segment, one recurring use case; first modules: map preference ownership; compare sync rules. Manual review in the loop. Built by our AI software factory.
- 3
Paid pilot
5 daysAccounts, roles, review states, audit trail and the first integration, hardened for two to three paying pilot customers.
- 4
Full product
9 daysSelf-serve onboarding, billing, monitoring and the wider integration set.
- 5
Run and improve
MonthlyWe 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.
- Pick the riskiest assumption. Here: will multi-device product teams use it to solve "preferences behave inconsistently across devices"?
- Build only what tests it. One team, one use case, a few core modules. People do the rest by hand for now.
- Run a paid pilot. Agree the acceptance criteria, input limits and reviewer responsibilities before starting.
- Measure, then decide. Track undefined synchronization states; 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 approved settings definitions and device journeys. 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: map preference ownership; compare sync rules. Support the third task through an assisted review queue: flag conflicting states. Handle the remaining required functions manually until validated. Include input upload, source references, user correction, a reviewer approval step and export of reviewed preference behavior specification. 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 reviewed preference behavior specification, automate link proposed outputs to original source records; capture reviewer corrections and approval; export a versioned reviewed preference behavior specification. 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 approved settings definitions and device journeys. Professional judgment, physical inspections, live external actions and production certification remain outside this prototype.
What the build depends on. A defensible calculation model, explicit units, constraint validation and representative boundary tests. Advanced forecasting or optimization needs adequate historical data. 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 approved settings definitions and device journeys. Professional judgment, physical inspections, live external actions and production certification remain outside this prototype.
Investment
A planning range to start the conversation, not a quote. You pay per phase, so you can stop after the MVP.
- Phase 1
MVP
One buyer segment, one recurring use case; first modules: map preference ownership; compare sync rules. Manual review in the loop.
- Phase 2
Paid pilot
Accounts, roles, review states, audit trail and the first integration, hardened for two to three paying pilot customers.
- Phase 3
Full product
Self-serve onboarding, billing, monitoring and the wider integration set.
Indicative total, MVP to full product$44,000about 4 weeks of creation time · start with the MVP from $13,000
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.
| Stage | Hosting and infrastructure | AI usage | Total per month |
|---|---|---|---|
| MVP and paid pilotabout 3 customers | $30–$60 | $50–$100 | $80–$160 |
| Full productabout 50 customers | $110–$210 | $350–$700 | $460–$910 |
Run it or resell it
For your own team
Multi-device product teams run it inside the business: approved settings definitions and device journeys in, reviewed preference behavior specification out, reviewed by your people.
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
#91278d - accent
#54c968 - surface
#f1e4f0 - ink
#22201e
- Headings
- Archivo
- Text
- Lora
- 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 a scoped planning setup and review, then USD 200-900 monthly for refreshes within agreed complexity. Data integration and optimization are separately scoped. All ranges are hypotheses. For this buyer, package the first sale around prepare a sample reviewed preference behavior specification from a small authorized set of approved settings definitions and device journeys and the defined reviewed preference behavior specification. Record actual review effort before offering a recurring allowance. The commercial pilot fee is distinct from the platform development budget.
Message to test
Explicit cross-device preference expectations. Demonstrate the result with prepare a sample reviewed preference behavior specification from a small authorized set of approved settings definitions and device journeys for multi-device product 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 reviewed preference behavior specification from a small authorized set of approved settings definitions and device journeys
The first 30 days
- Week 1: interview five prospective buyers from multi-device product teams and inspect how they handle preferences behave inconsistently across devices.
- Week 2: prepare prepare a sample reviewed preference behavior specification from a small authorized set of approved settings definitions and device journeys using authorized or synthetic material.
- Week 3: share the demonstration through product management communities and UX research partners and seek one bounded paid pilot.
- Week 4: measure undefined synchronization states; 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 reviewed preference behavior specification from a small authorized set of approved settings definitions and device journeys and deliver reviewed preference behavior specification. Compare undefined synchronization states; 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
Undefined synchronization states; reviewer correction minutes; buyer acceptance and repeat purchase
Retention and expansion
Build repeat use around reviewed preference behavior specification. Save approved configurations and review decisions with permission, revisit unresolved exceptions and show progress on undefined synchronization states; 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
A validated domain model, customer-approved constraints and forecast or decision history that improves practical planning. For this concept, accumulate permissioned examples and reviewer corrections around explicit cross-device preference expectations. The durable asset is reliable task-specific execution and trusted customer configuration, not access to a general-purpose AI model.
Alternatives and positioning
Spreadsheets, planners, specialist forecasting tools and existing scheduling or configuration software. Position this concept around explicit cross-device preference expectations. 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
Data preparation, domain modeling, validation, scenario computation, reviewer support and ongoing assumption maintenance. Initial validation additionally budgets for representative sample preparation, interviews with multi-device product teams, and buyer-side review of reviewed preference behavior specification. Track model usage, storage, reviewer minutes, exception handling and customer support per accepted deliverable.
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 approved settings definitions and device journeys. 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.