Screenshot of the Course assessment coverage mapper interactive demo
Screenshot of the interactive demo, on sample data

Course assessment coverage mapper

Evidence of what assessments actually test.

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For
Academic quality teams
Solves
Assessments omit some stated learning outcomes.
Delivers
Faculty-approved coverage matrix
Built in
about 3 weeks of creation time, MVP in 3 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 academic quality teams, turn approved outcomes and assessment prompts into faculty-approved coverage matrix.

  1. Link questions to outcomes.
  2. Flag unsupported mappings.
  3. Identify coverage gaps.
  4. Link proposed outputs to original source records.
  5. Capture reviewer corrections and approval.
  6. Export a versioned faculty-approved coverage matrix.

What goes in, what comes out

What the customer puts in
  • Approved outcomes
  • Assessment prompts

AI drafts, people review. Evidence review and quality assurance workspace.

What the customer gets
  • Faculty-approved coverage matrix
02

How it works

The workflow

  1. In
    Start with

    Approved outcomes and assessment prompts

  2. 1

    The buyer creates a project

  3. 2

    Supplies approved outcomes and assessment prompts

  4. 3

    Confirms scope and access

  5. Out
    Finish with

    Faculty-approved coverage matrix

AI does the heavy lifting, people stay in charge

AI assists these bounded tasks: link questions to outcomes; flag unsupported mappings; identify coverage gaps. Use only approved outcomes and assessment prompts and preserve uncertainty in faculty-approved coverage matrix. 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, Course assessment coverage mapper, Review and delivery. Open on a review queue ordered by reviewer-selected priorities. Show each finding beside the original evidence and applicable rule. Provide accept, dismiss and needs-information controls with reasons. A separate report view summarizes confirmed findings and unresolved items, not raw AI flags. Open with brief and sources; move into course assessment coverage mapper 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

Versioned review criteria, evidence links, reviewer decisions, disagreement handling, correction assignments, recheck status and exportable review history. 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

Learning resources, course portals and educator review processes. Source repositories, task trackers and report exports. Keep findings as review proposals until authorized owners accept the resulting actions. Begin with uploads and exports of approved outcomes and assessment prompts. 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

    3 days

    One buyer segment, one recurring use case; first modules: link questions to outcomes; flag unsupported mappings. Manual review in the loop. Built by our AI software factory.

  3. 3

    Paid pilot

    4 days

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

  4. 4

    Full product

    7 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 academic quality teams use it to solve "assessments omit some stated learning outcomes"?
  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 unassessed stated outcomes; 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 outcomes and assessment prompts. 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: link questions to outcomes; flag unsupported mappings. Support the third task through an assisted review queue: identify coverage gaps. Handle the remaining required functions manually until validated. Include input upload, source references, user correction, a reviewer approval step and export of faculty-approved coverage matrix. 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 faculty-approved coverage matrix, automate link proposed outputs to original source records; capture reviewer corrections and approval; export a versioned faculty-approved coverage matrix. 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 outcomes and assessment prompts. Professional judgment, physical inspections, live external actions and production certification remain outside this prototype.

What the build depends on. Evidence coordinates, versioned rules, reviewer decisions and a representative reference set. Measure misses as well as confirmed findings before scaling. 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 outcomes and assessment prompts. 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: link questions to outcomes; flag unsupported mappings. Manual review in the loop.

    $12,500 · about 3 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 4 days of creation time

  3. Phase 3

    Full product

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

    $17,500 · about 7 days of creation time

Indicative total, MVP to full product$42,500about 3 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

Academic quality teams run it inside the business: approved outcomes and assessment prompts in, faculty-approved coverage matrix 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#8f9127
  • accent#5466c9
  • surface#f1f1e4
  • ink#22201e
Headings
Libre Baskerville
Text
IBM Plex Sans
Voice
Encouraging, patient, precise
Selling it to your own clients: the go-to-market playbook

Pricing to test

Test USD 500-2,000 for a defined audit sample and report. Offer recurring review priced by reviewed items and specialist hours. Software-only access can follow a reliable reviewed service. All prices require validation. For this buyer, package the first sale around prepare a sample faculty-approved coverage matrix from a small authorized set of approved outcomes and assessment prompts and the defined faculty-approved coverage matrix. Record actual review effort before offering a recurring allowance. The commercial pilot fee is distinct from the platform development budget.

Message to test

Evidence of what assessments actually test. Demonstrate the result with prepare a sample faculty-approved coverage matrix from a small authorized set of approved outcomes and assessment prompts for academic quality teams. Use a concrete before-and-after example without promising unmeasured savings.

Where to find buyers

Educator associations and specialist training providers

Lead magnet

Prepare a sample faculty-approved coverage matrix from a small authorized set of approved outcomes and assessment prompts

The first 30 days

  1. Week 1: interview five prospective buyers from academic quality teams and inspect how they handle assessments omit some stated learning outcomes.
  2. Week 2: prepare prepare a sample faculty-approved coverage matrix from a small authorized set of approved outcomes and assessment prompts using authorized or synthetic material.
  3. Week 3: share the demonstration through educator associations and specialist training providers and seek one bounded paid pilot.
  4. Week 4: measure unassessed stated outcomes; 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 faculty-approved coverage matrix from a small authorized set of approved outcomes and assessment prompts and deliver faculty-approved coverage matrix. Compare unassessed stated outcomes; 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

Unassessed stated outcomes; reviewer correction minutes; buyer acceptance and repeat purchase

Retention and expansion

Build repeat use around faculty-approved coverage matrix. Save approved configurations and review decisions with permission, revisit unresolved exceptions and show progress on unassessed stated outcomes; 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 domain-specific review rubric and rights-cleared examples of confirmed defects, false alarms and reviewer reasoning. For this concept, accumulate permissioned examples and reviewer corrections around evidence of what assessments actually test. The durable asset is reliable task-specific execution and trusted customer configuration, not access to a general-purpose AI model.

Alternatives and positioning

Manual reviewers, checklists, generic scanning tools and specialist audit services. Position this concept around evidence of what assessments actually test. 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

Document or media processing, model evaluation, expert review, false-positive handling, rechecks and customer-specific rubric calibration. Initial validation additionally budgets for representative sample preparation, interviews with academic quality teams, and buyer-side review of faculty-approved coverage matrix. Track model usage, storage, reviewer minutes, exception handling and customer support per accepted deliverable.

06

Safeguards

Use educator-reviewed content and answer keys. Apply appropriate access and consent for learner records and distinguish completion from demonstrated learning. One organization, one defined input format and one representative pilot batch using approved outcomes and assessment prompts. 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 3 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.

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