Team work-in-progress release controller
Increase finished deliverables without increasing total workload.
- For
- Agency delivery managers
- Solves
- Too many simultaneous projects delay completion.
- Delivers
- Team-approved work-release policy trial
- Built in
- about 3 weeks of creation time, MVP in 3 days
- Investment
- $22,000 for the MVP, $50,000 for the full product
- Run it
- Inside your business, or as part of your offer to clients
What it does
Increase finished deliverables without increasing total workload.
- Model release limits.
- Compare sequencing policies.
- Recommend bounded process experiments.
- Compare the reviewed result with the recorded baseline and value assumptions.
- Capture corrections and named-owner approval before consequential use.
- Export a versioned team-approved work-release policy trial with source references and unresolved questions.
What goes in, what comes out
- Approved project priorities
- Aggregate stage capacity
AI drafts, people review. Assumption-driven planning and decision workspace.
- Team-approved work-release policy trial
How it works
The workflow
- InStart with
Approved project priorities and aggregate stage capacity
- 1
Confirm the buyer's problem and scope
- 2
Collect approved project priorities and aggregate stage capacity
- 3
Then follow this sequence: 1
- OutFinish with
Team-approved work-release policy trial
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. Advisory mode; managers retain client and staffing commitments. A model suggestion is never a verified fact, professional decision or authorization to act.
What your team sees
Primary screens: Constraint and input setup, Scenario comparison, Decision and pilot tracker. 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. Make the task-specific outcome team-approved work-release policy trial visible beside its evidence, review state and value baseline.
Accounts and administration
Scenario versions, baseline reconciliation, constraint checks, assumption ownership, reviewer approvals, plan exports and actual-versus-plan tracking. Add organization access boundaries, named reviewers, usage caps, data retention controls, export logs and explicit approval for external actions.
Integrations and data access
Team updates, calendars, project records and agreed management routines. Read-only operational exports, calendars and finance or inventory records as relevant. Start with plan exports and retain human approval for execution. Start with authorized file exchange. Validate current provider access, usage rights and schema behavior before promising a connector.
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
3 daysOne buyer segment, one recurring use case; first modules: model release limits; compare sequencing policies. Manual review in the loop. Built by our AI software factory.
- 3
Paid pilot
4 daysAccounts, roles, review states, audit trail and the first integration, hardened for two to three paying pilot customers.
- 4
Full product
7 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 agency delivery managers use it to solve "too many simultaneous projects delay completion"?
- 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 quality and outcome thresholds before the pilot using this measure: Cycle-time and contribution changes minus implementation cost.
- Measure, then decide. Track cycle-time and contribution changes minus implementation cost; 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: Advisory mode; managers retain client and staffing commitments. Implement one approved input format, a bounded representative case set and the first two task modules: model release limits; compare sequencing policies. Support the third module with operator review: recommend bounded process experiments. 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 team-approved work-release policy trial. Retain the explicit scope boundary: Advisory mode; managers retain client and staffing commitments.
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 cases, baseline measurements, qualified reviewers and a buyer-side decision owner. Specific limitation: Advisory mode; managers retain client and staffing commitments.
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: model release limits; compare sequencing policies. 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$50,000about 3 weeks of creation time · start with the MVP from $22,000
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
Agency delivery managers run it inside the business: approved project priorities and aggregate stage capacity in, team-approved work-release policy trial 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
#273891 - accent
#c3c954 - surface
#e4e7f1 - ink
#22201e
- Headings
- Fraunces
- Text
- Inter
- Voice
- Practical, organised, candid
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. Package the initial sale as one bounded team-approved work-release policy trial. 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
Increase finished deliverables without increasing total workload. Demonstrate a concrete team-approved work-release policy trial using the buyer's approved example and show the baseline, corrections and actual delivery effort.
Where to find buyers
Agency delivery managers professional communities; specialist consultants serving this buyer; permissioned partner introductions; practical demonstrations at relevant trade or practitioner events.
Lead magnet
A reviewed sample team-approved work-release policy trial from a small authorized input set, with a transparent calculation of cycle-time and contribution changes minus implementation cost and no promised savings.
The first 30 days
- Week 1: interview five agency delivery managers and inspect a recent example of too many simultaneous projects delay completion.
- Week 2: prepare a consented or synthetic demonstration of the three task modules.
- Week 3: seek one bounded paid pilot with agreed baseline and acceptance criteria.
- Week 4: measure cycle-time and contribution changes minus implementation cost, 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: Cycle-time and contribution changes minus implementation cost. 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
Cycle-time and contribution changes minus implementation cost; accepted-output rate; material error rate; reviewer correction time; actual repeat purchase.
Retention and expansion
Repeat the workflow when the buyer again needs team-approved work-release policy trial. 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 validated domain model, customer-approved constraints and forecast or decision history that improves practical planning. Build a permissioned library of representative task cases, reviewer corrections and verified operating constraints for agency delivery managers. Repeatable delivery and useful integrations matter more than access to a base model.
Alternatives and positioning
Spreadsheets, planners, specialist forecasting tools and existing scheduling or configuration software. Compare this product with the buyer's present method on cycle-time and contribution changes minus implementation cost. Offer a bounded paid workflow instead of claiming broad autonomous expertise. Market uniqueness and competitor coverage are not verified.
Main delivery costs
Data preparation, domain modeling, validation, scenario computation, reviewer support and ongoing assumption maintenance. Additional initial validation requires representative authorized sample preparation, buyer interviews, buyer-side evaluation and bounded validation of team-approved work-release policy trial. Track cost per accepted output, including correction work, unsuccessful cases and support.
Safeguards
Confirm owners, decisions and commitments. Keep employee discussion notes access-controlled and avoid covert individual performance inference. Advisory mode; managers retain client and staffing commitments. Keep all consequential actions under authorized human control and do not fabricate missing inputs, permissions, professional judgments or market evidence.