Ptally Pricing Team
The consultants who build, launch and manage pricing models for insurers and MGAs on the Ptally platform. They write about pricing practice, rate changes and using AI in actuarial work.
Articles
16Ptally compared: established pricing platforms and new AI entrants
How Ptally compares with established insurance pricing and rating platforms and with newer AI-led options, on upfront cost, implementation, use of AI, model readability and time to launch.
Connect an AI client and find the API reference
How to use the MCP and API pages inside Ptally to connect Claude, ChatGPT or Cursor, and to find every API endpoint with examples.
Building insurance pricing models from US rate filings
How US rate and rule filings can be turned into working, tested pricing models: what a filing contains, where to find it, how AI speeds up extraction, and the limits to plan for.
AI in insurance pricing: from rating guide to live model in days
How AI is changing the way insurers and MGAs build, test and launch pricing models, and why the best results come from pairing AI with experienced pricing consultants.
AI in insurance: where it delivers value today
A practical guide to where AI creates measurable value across insurance, from pricing and underwriting to distribution, claims and operations, and what successful projects have in common.
Manage users and API keys
How to add users, understand roles, find and rotate your API keys, and read the audit log in Ptally settings.
Analyse quotes
How to use the Ptally quotes page: volume, latency, result and validity charts, filtering by project and version, searching quotes and opening a quote's full detail.
How much does an insurance rating engine cost? Licences, implementation and pay as you write
The real costs of an insurance rating engine: licence fees, implementation projects, internal team time, rate changes, hosting and the cost of delay, and how the main commercial models compare.
Approve and publish a version
How a Ptally version moves from draft to approved to published, what each status means for live quotes, and where to see the version history.
How to price pet insurance: rating factors and a worked example
The main rating factors in pet insurance, how they combine into a premium, and a worked example priced by a real rating model, from base rate to Insurance Premium Tax.
Edit a model in a draft version
How to make changes to a pricing model in Ptally: create a draft version, edit a step or rate table, upload a model or CSV, and summarise the change for reviewers.
What to look for in an insurance pricing platform: a buyer's checklist
A practical checklist for insurers and MGAs choosing a pricing platform, covering model build, testing, versioning, explainability, APIs, AI and MCP, performance, commercial terms and support.
Run and read tests
How to run a version's test suite in Ptally, read the results, open a test to see its inputs and expected result, and add tests to a draft.
Rate changes without the risk: a step-by-step process for pricing teams
A step-by-step process for making insurance rate changes safely, from analysis and draft versions to testing, approval, release and monitoring, and where AI fits in.
Price a quote: dry runs and live quotes
How to price a quote from a project or version page in Ptally, the difference between a dry run and a live quote, and how to copy the request as a curl command.
Getting around Ptally: projects, versions and quotes
A tour of the Ptally platform: the projects list, a project's page, a version's model, steps and tests, and where to find quotes, the API and MCP.
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