Policy change tracking tool
Clause-level evidence with wording changes separated from formatting noise.
- For
- Brokers reviewing annual policy revisions
- Solves
- Small wording changes are hard to distinguish from layout changes.
- Delivers
- Policy change report for professional review
- Built in
- about 5 weeks of creation time, MVP in 6 days
- Investment
- $10,000 for the MVP, $40,000 for the full product
- Run it
- Inside your business, or as part of your offer to clients
What it does
For brokers reviewing annual policy revisions, turn prior and current policy documents into policy change report for professional review.
- Align clauses.
- Ignore layout-only changes.
- Highlight changed wording.
- Preserve original text.
- Group affected topics.
- Export review notes.
What goes in, what comes out
- Prior
- Current policy documents
AI drafts, people review. Structured comparison and clarification workspace.
- Policy change report for professional review
How it works
The workflow
- InStart with
Prior and current policy documents
- 1
Define comparison fields
- 2
Upload source versions or offers
- 3
Extract candidate values
- 4
Normalize only agreed units
- 5
Inspect differences
- 6
Resolve questions with reviewers
- 7
Export an evidence-linked comparison
- OutFinish with
Policy change report for professional review
AI does the heavy lifting, people stay in charge
Align document sections and extract proposed comparable fields. Deterministic checks handle units and arithmetic. Preserve original wording and label assumptions. Professional reviewers assess the meaning and significance of differences.
What your team sees
Key screens: Version comparison, clause changes, reviewer notes. Use a side-by-side matrix with the same fields for every document or option. Clicking a value reveals its original passage. Highlight missing, different and uncertain items separately. Provide a clarification queue and reviewer annotations before exporting a decision pack. In this product, the first view is version comparison, followed by clause changes and reviewer notes.
Accounts and administration
Document versions, field definitions, source references, reviewer corrections, unresolved questions, comparison history and exportable matrices.
Integrations and data access
Broker-approved policy documents, case records and carrier requirements. Document repositories, procurement or contract records and spreadsheet exports. Preserve originals and avoid writing back interpretations without approval. These are candidate integration categories, not verified supported connectors.
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
6 daysOne buyer segment, one recurring use case; first modules: align clauses; ignore layout-only changes. Manual review in the loop. Built by our AI software factory.
- 3
Paid pilot
7 daysAccounts, roles, review states, audit trail and the first integration, hardened for two to three paying pilot customers.
- 4
Full product
2 weeksRemaining modules: preserve original text; group affected topics; export review notes. Self-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 brokers reviewing annual policy revisions use it to solve "small wording changes are hard to distinguish from layout changes"?
- 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. Compare a known set already reviewed by a domain expert.
- Measure, then decide. Track meaningful change detection and false alarms. Then expand, change course or stop, with evidence instead of opinions.
MVP scope for this solution. Begin with brokers reviewing annual policy revisions and one recurring use case. Build the first two modules: align clauses; ignore layout-only changes. Provide operator assistance for the third module: highlight changed wording. Deliver policy change report for professional review 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.
After the MVP. After paid pilots establish value, automate the remaining modules: preserve original text; group affected topics; export review notes. 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.
What the build depends on. Document alignment, field provenance, unit normalization, missing-value handling and reviewer correction. Similar-looking documents may contain materially different terms.
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: align clauses; ignore layout-only changes. 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
Remaining modules: preserve original text; group affected topics; export review notes. Self-serve onboarding, billing, monitoring and the wider integration set.
Indicative total, MVP to full product$40,000about 5 weeks of creation time · start with the MVP from $10,000
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 | $50–$100 | $50–$100 | $100–$200 |
| Full productabout 50 customers | $190–$380 | $350–$700 | $540–$1,080 |
Run it or resell it
For your own team
Brokers reviewing annual policy revisions run it inside the business: prior and current policy documents in, policy change report for professional review 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
#27918d - accent
#c95456 - surface
#e4f1f0 - ink
#22201e
- Headings
- Playfair Display
- Text
- Source Sans 3
- Voice
- Reassuring, clear, no small print
Selling it to your own clients: the go-to-market playbook
Pricing to test
Test USD 300-1,500 for one bounded comparison package, then USD 150-600 monthly for recurring volume with review limits. Complex expert interpretation is separately priced. Figures are hypotheses.
Message to test
Policy change tracking tool for brokers reviewing annual policy revisions. Clause-level evidence with wording changes separated from formatting noise. Demonstrate the claim through an annotated annual policy comparison.
Where to find buyers
Insurance legal and product advisers
Lead magnet
An annotated annual policy comparison
The first 30 days
- Week 1: interview five prospective buyers in this segment: brokers reviewing annual policy revisions. Ask to see a recent example of the problem and their current process.
- Week 2: prepare this demonstration using authorized or synthetic material: an annotated annual policy comparison.
- Week 3: present it through insurance legal and product advisers and seek one narrowly scoped paid pilot.
- Week 4: review meaningful change detection, false alarms, total delivery effort and a concrete renewal decision before increasing scope.
Paid pilot
Compare a known set already reviewed by a domain expert. Check meaningful differences, false alarms and missing fields. Measure reviewer time including corrections rather than extraction speed alone. For this solution, use prior and current policy documents and evaluate policy change report for professional review. Agree success thresholds with the buyer before starting; collect a baseline for meaningful change detection, false alarms. A positive signal is payment and repeat use with acceptable quality and delivery cost, not a favorable demo reaction alone.
Success metrics
Meaningful change detection, false alarms
Retention and expansion
Save reviewer-approved comparison fields and recurring document formats. Offer repeat comparisons and explicit updates when the source options change.
Why clients would pick it
A niche comparison schema, reviewed extraction examples and clear handling of the differences that matter to a specific buyer. For this solution, build around clause-level evidence with wording changes separated from formatting noise. This advantage requires execution and accumulated customer trust; the base model alone is not a defensible asset.
Alternatives and positioning
Spreadsheet comparisons, professional reviewers, document diff tools and manual quote or contract review. Differentiate on this specific proposed advantage: clause-level evidence with wording changes separated from formatting noise. Test it against the buyer's current method on the same task. Competitor coverage and uniqueness have not been established.
Main delivery costs
Document parsing, field alignment, source verification, expert interpretation, clarification rounds and changing document formats.
Safeguards
Separate document preparation from coverage, underwriting and claims decisions. Authorized professionals review policy meaning and customer commitments. Validate source access and reviewer availability during the pilot. Maintain customer-level access, data deletion controls and a record of final approvals.