Screenshot of the Public consultation plain-language response studio interactive demo
Screenshot of the interactive demo, on sample data

Public consultation plain-language response studio

Explain a technical position without changing its substance.

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For
Organizations responding to policy consultations
Solves
Technical submissions are inaccessible to wider stakeholders.
Delivers
Approved public-facing policy summary
Built in
about 4 weeks of creation time, MVP in 5 days
Investment
$11,500 for the MVP, $39,000 for the full product
Run it
Inside your business, or as part of your offer to clients
01

What it does

For organizations responding to policy consultations, turn approved policy positions and supporting evidence into approved public-facing policy summary.

  1. Extract approved arguments.
  2. Link supporting evidence.
  3. Draft plain-language variants.
  4. Preserve qualifications.
  5. Compare consistency.
  6. Export reviewed summaries.

What goes in, what comes out

What the customer puts in
  • Approved policy positions
  • Supporting evidence

AI drafts, people review. Source-based content workspace with editorial delivery.

What the customer gets
  • Approved public-facing policy summary
02

How it works

The workflow

  1. In
    Start with

    Approved policy positions and supporting evidence

  2. 1

    The buyer creates a project

  3. 2

    Supplies approved policy positions and supporting evidence

  4. 3

    Confirms scope and access

  5. Out
    Finish with

    Approved public-facing policy summary

AI does the heavy lifting, people stay in charge

Rewrite from approved evidence with tracked changes. 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: Position map, Audience drafts, Approval comparison. Use a project list and editorial calendar beside a document editor. Keep original material and supporting passages in a collapsible side panel. Show outline, draft, review and approved stages. Provide tracked edits, comments, version comparisons and an export preview that reflects the final delivery format. Open with position map; move into audience drafts for the detailed task; finish in approval comparison for review and handoff. Show the source record, uncertainty and approval status beside each proposed output.

Accounts and administration

Client workspaces, source permissions, editorial assignments, change history, reviewer comments, approval gates, revision allowances and export templates. 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

Approved company facts, permitted media sources and publication workflows. Document storage, word processor export, content management systems and approved publishing channels. Pilot with uploads and downloadable drafts before adding write integrations. Begin with uploads and exports of approved policy positions and supporting evidence. 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

    5 days

    One buyer segment, one recurring use case; first modules: extract approved arguments; link supporting evidence. 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 organizations responding to policy consultations use it to solve "technical submissions are inaccessible to wider stakeholders"?
  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 meaning-preserving edits and reader comprehension. Then expand, change course or stop, with evidence instead of opinions.

MVP scope for this solution. Costed pilot: Communication adaptation; no legal or lobbying strategy advice. Start with one buyer organization and a bounded set of representative inputs. Implement the first two modules: extract approved arguments; link supporting evidence. Support the third task through an assisted review queue: draft plain-language variants. Handle the remaining required functions manually until validated. Include input upload, source references, user correction, a reviewer approval step and export of approved public-facing policy summary. 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 approved public-facing policy summary, automate preserve qualifications; compare consistency; export reviewed summaries. 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. Communication adaptation; no legal or lobbying strategy advice.

What the build depends on. Document parsing, a source-linked editor, tracked revisions, reviewer workflow and reliable document export. Rich presentation or print output needs format-specific QA. Obtain representative authorized inputs, an agreed review rubric and a buyer-side owner. Specific scope: Communication adaptation; no legal or lobbying strategy advice.

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: extract approved arguments; link supporting evidence. Manual review in the loop.

    $11,500 · about 5 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.

    $11,500 · about 6 days of creation time

  3. Phase 3

    Full product

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

    $16,000 · about 2 weeks of creation time

Indicative total, MVP to full product$39,000about 4 weeks of creation time · start with the MVP from $11,500

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$70–$140$100–$200
Full productabout 50 customers$110–$210$700–$1,400$810–$1,610
05

Run it or resell it

Internally

For your own team

Organizations responding to policy consultations run it inside the business: approved policy positions and supporting evidence in, approved public-facing policy summary 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#6f2791
  • accent#72c954
  • surface#ede4f1
  • ink#22201e
Headings
Fraunces
Text
Inter
Voice
Articulate, timely, composed
Selling it to your own clients: the go-to-market playbook

Pricing to test

Test USD 400-1,500 for a tightly scoped initial content package. Convert repeated work to a monthly retainer with explicit deliverable and revision limits. Specialist review and substantial research are separately scoped. Prices are hypotheses. For this buyer, package the first sale around adapt one approved submission and the defined approved public-facing policy summary. Record actual review effort before offering a recurring allowance. The commercial pilot fee is distinct from the platform development budget.

Message to test

Explain a technical position without changing its substance. Demonstrate the result with adapt one approved submission for organizations responding to policy consultations. Use a concrete before-and-after example without promising unmeasured savings.

Where to find buyers

Trade associations and public affairs advisors

Lead magnet

Adapt one approved submission

The first 30 days

  1. Week 1: interview five prospective buyers from organizations responding to policy consultations and inspect how they handle technical submissions are inaccessible to wider stakeholders.
  2. Week 2: prepare adapt one approved submission using authorized or synthetic material.
  3. Week 3: share the demonstration through trade associations and public affairs advisors and seek one bounded paid pilot.
  4. Week 4: measure meaning-preserving edits and reader comprehension, 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 adapt one approved submission and deliver approved public-facing policy summary. Compare meaning-preserving edits and reader comprehension 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

Meaning-preserving edits and reader comprehension

Retention and expansion

Build repeat use around approved public-facing policy summary. Save approved configurations and review decisions with permission, revisit unresolved exceptions and show progress on meaning-preserving edits and reader comprehension. 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

Customer-approved terminology, reusable structures, source libraries and editorial feedback tied to a specific audience and recurring publishing workflow. For this concept, accumulate permissioned examples and reviewer corrections around explain a technical position without changing its substance. The durable asset is reliable task-specific execution and trusted customer configuration, not access to a general-purpose AI model.

Alternatives and positioning

Writers, editors, agencies, internal document templates and general-purpose chat tools. Position this concept around explain a technical position without changing its substance. 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

Research and interview time, transcription, model usage, factual verification, subject-matter review, editing and revisions. Initial validation additionally budgets for policy expert and reader review. Track model usage, storage, reviewer minutes, exception handling and customer support per accepted deliverable.

06

Safeguards

Verify public facts and quotations. Keep publication authority explicit and preserve the original context behind media and reputation findings. Communication adaptation; no legal or lobbying strategy advice. 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 5 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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