Solution Database / Government
Public consultation analyzer
Large submission volumes hide distinct minority perspectives. Auditable synthesis that retains disagreement and source traceability.
01The offer
For policy teams handling public consultations, turn authorized submissions and consultation questions into consultation evidence report. Address the recurring problem: large submission volumes hide distinct minority perspectives. The value hypothesis is a more complete, reviewable deliverable with less repeated preparation; the pilot must establish whether that benefit is real.
- For
- Policy teams handling public consultations
- Takes in
- Authorized submissions and consultation questions
- Delivers
- Consultation evidence report
- Message
- Public consultation analyzer for policy teams handling public consultations. Auditable synthesis that retains disagreement and source traceability. Demonstrate the claim through an analysis of a published historical consultation.
- Lead magnet
- An analysis of a published historical consultation
02How it works
- Classify responses
- Preserve minority views
- Detect duplicates
- Link quoted evidence
- Distinguish frequency from importance
- Export transparent summaries
Workflow
Agree definitions, import authorized data, validate coverage and identifiers, compute transparent measures, group relevant evidence, review findings, assign investigations or improvements, and repeat on a comparable period. Start with authorized submissions and consultation questions and finish with consultation evidence report.
AI and people
Classify text, summarize evidence and propose explanations to investigate. Compute financial or operational measures with deterministic code. Separate observed patterns from causal claims and preserve examples that contradict the summary.
Screens
Key screens: Submission themes, evidence explorer, review log. Open with a compact overview and filters for the relevant period or segment. Let users drill from each theme or metric into underlying records. Keep source definitions and missing-data notes near the result. Use an action panel to assign investigations and record what was learned. In this product, the first view is submission themes, followed by evidence explorer and review log.
Admin
Dataset permissions, field mappings, metric definitions, source drill-down, saved filters, reviewer annotations, recurring reports and action ownership.
03Market gap
Alternatives buyers use today
Analysts, business intelligence dashboards, spreadsheets and general text summarization tools. Differentiate on this specific proposed advantage: auditable synthesis that retains disagreement and source traceability. Test it against the buyer's current method on the same task. Competitor coverage and uniqueness have not been established.
Where this wins
Domain-specific definitions, trusted source mappings and a history connecting findings to actions and observed results. For this solution, build around auditable synthesis that retains disagreement and source traceability. This advantage requires execution and accumulated customer trust; the base model alone is not a defensible asset.
04Why now
Government teams are adopting AI for exactly this kind of repeatable work, and the cost of language and vision models has dropped far enough that a narrow, reviewed workflow pays back quickly. Rising compliance and audit expectations make a documented, reviewable process worth more than an ad hoc one. The buyer already feels the problem: large submission volumes hide distinct minority perspectives.
05Proof & signals
Channels where buyers gather: Public-sector research consultancies. Metrics that prove it works: Reviewer agreement, evidence coverage.
Paid pilot
Analyze one historical period and review findings with the responsible domain owner. Reconcile headline measures, inspect counterexamples and ask the buyer to choose a concrete follow-up action. For this solution, use authorized submissions and consultation questions and evaluate consultation evidence report. Agree success thresholds with the buyer before starting; collect a baseline for reviewer agreement, evidence coverage. A positive signal is payment and repeat use with acceptable quality and delivery cost, not a favorable demo reaction alone.
06Execution plan
MVP
Begin with policy teams handling public consultations and one recurring use case. Build the first two modules: classify responses; preserve minority views. Provide operator assistance for the third module: detect duplicates. Deliver consultation evidence report through a manual review queue. Perform other necessary full-scope functions manually during the pilot. Include all applicable access, accuracy and professional-review controls from the start.
First 30 days
Week 1: interview five prospective buyers in this segment: policy teams handling public consultations. Ask to see a recent example of the problem and their current process. Week 2: prepare this demonstration using authorized or synthetic material: an analysis of a published historical consultation. Week 3: present it through public-sector research consultancies and seek one narrowly scoped paid pilot. Week 4: review reviewer agreement, evidence coverage, total delivery effort and a concrete renewal decision before increasing scope.
After the pilot
After paid pilots establish value, automate the remaining modules: link quoted evidence; distinguish frequency from importance; export transparent summaries. Add one validated source integration, reusable customer configuration and recurring delivery. Expand to additional teams, document formats or languages only after testing the new scope.
Retention
Repeat the same definitions each reporting period and track whether findings lead to useful action. Expand data sources without breaking historical comparability.
Integrations
Official publications, agency document stores and approved service workflows. Read-only business data exports, reporting databases and task trackers. Reconcile source totals before scheduling recurring data refreshes. These are candidate integration categories, not verified supported connectors.
07Investment and running costs
| Phase | Scope | Time | Budget |
|---|---|---|---|
| MVP | One buyer segment, one recurring use case; first modules: classify responses; preserve minority views. Manual review in the loop. | 6 days | $11,000 |
| Paid pilot | Accounts, roles, review states, audit trail and the first integration, hardened for two to three paying pilot customers. | 7 days | $14,000 |
| Full product | Remaining modules: link quoted evidence; distinguish frequency from importance; export transparent summaries. Self-serve onboarding, billing, monitoring and the wider integration set. | 3 weeks | $19,000 |
| Total | $44,000 | ||
| Running | Hosting | AI usage | Total a month |
|---|---|---|---|
| MVP and paid pilot (about 3 customers) | $50–$100 | $80–$160 | $130–$260 |
| Full product (about 50 customers) | $190–$380 | $880–$1,750 | $1,070–$2,130 |
Revenue model to test
Test USD 500-2,000 for an initial analysis of one bounded dataset. Offer USD 250-1,000 monthly for repeat reporting at agreed volume. Data cleanup and specialist analysis are separately priced. These are test ranges.
Cost drivers
Data preparation, reconciliation, classification, expert interpretation, customer-specific definitions and recurring reporting support.
Safeguards
Preserve official source versions, accessibility and audit records. Confirm agency-specific procurement, records and data handling requirements during discovery. Validate source access and reviewer availability during the pilot. Maintain customer-level access, data deletion controls and a record of final approvals.
Take it further
Concept proposal expanded from the 315-solution conversation. Demand, pricing, differentiation, build scope and integration feasibility are hypotheses, not verified market findings. Category link is inspiration rather than evidence of business viability.