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AICost Industry Solutions

Insurance (P&C and life)

3 AI automations this industry runs today, each with the process, the AICost engine chain that prices and governs it, and the evidence behind it.

Cost engine Governance engine Each step priced and controlled. Click any engine to open its calculator or playground.
Claims FNOL, triage and fraud flagsCommercial underwriting submission intake and triageClaims and policyholder communications drafting

Claims FNOL, triage and fraud flags

Owner: Claims operations (adjusters, claims managers), SIU

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Scale: Bain: genAI could cut P&C loss-adjusting expenses 20-25% and leakage 30-50%

How the work runs

  1. First notice of loss by phone, app or email; capture photos and documents
  2. Coverage verification and severity triage (fast-track vs complex)
  3. Fraud scoring and SIU referral
  4. Claim file summarization for the adjuster
  5. Settlement, payment and subrogation or recovery

AICost engine chain

4 cost engines 3 governance engines

Each step priced and controlled. Click any engine to open its calculator or playground.

Where cost leaks

  • Vision and LLM calls on every photo and document of every claim
  • Re-summarizing long files at each touch
  • CAT event surges multiplying volume overnight

Where risk sits

  • Wrongful denials or lowball settlements (bad faith)
  • Biased fraud flags
  • No consumer notice of AI use as the NAIC bulletin expects

Rules that apply

  • NAIC Model Bulletin on AI (24 jurisdictions adopted by March 2025)
  • state unfair claims settlement practices acts
  • NY DFS Circular Letter No. 7 (2024)
  • EU AI Act (life/health risk pricing is high risk)
  • GDPR

Runs on

Guidewire ClaimCenterDuck Creek ClaimsSapiensCCC Intelligent SolutionsShift TechnologyTractable

Evidence

bain.com ↗guidewire.com ↗quarles.com ↗

Commercial underwriting submission intake and triage

Owner: Commercial lines underwriting, underwriting assistants and operations

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How the work runs

  1. Broker emails a submission (ACORD forms, SOV, loss runs)
  2. Extract and clear the submission; check appetite
  3. Enrich with third-party data
  4. Prioritize and route to an underwriter with a risk summary
  5. Underwriter quotes or declines; data goes to the policy admin system

AICost engine chain

4 cost engines 3 governance engines

Each step priced and controlled. Click any engine to open its calculator or playground.

Where cost leaks

  • Large SOV spreadsheets and loss runs processed for submissions that are out of appetite
  • Paid third-party enrichment on every submission
  • Re-extraction on broker resubmissions

Where risk sits

  • Proxy discrimination in risk selection
  • Extraction errors that misprice risk
  • Vendor data used without the diligence the NAIC bulletin requires

Rules that apply

  • NAIC AI Model Bulletin
  • state rating and unfair discrimination laws
  • NY DFS Circular Letter No. 7
  • Colorado SB21-169 (life insurers)

Runs on

Guidewire PolicyCenterDuck Creek PolicySapiensSalesforceHyperexponentialFederato

Evidence

wtwco.com ↗events.datos-insights.com ↗

Claims and policyholder communications drafting

Owner: Claims operations and customer service

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Scale: Allstate: AI drafts most of about 50,000 daily claims communications, reviewed by a claims rep (The Register, Feb 2025)

How the work runs

  1. Event in the claim triggers a communication (status, document request, decision)
  2. LLM drafts the email or letter in company language
  3. Adjuster reviews and edits
  4. Send and log in the claim file

AICost engine chain

4 cost engines 3 governance engines

Each step priced and controlled. Click any engine to open its calculator or playground.

Where cost leaks

  • Full claim file in context for short status notes
  • Re-drafting loops on edits
  • Premium models for template-level messages

Where risk sits

  • Wrong coverage statements in writing (bad faith evidence)
  • Missing required regulatory notices
  • Reps approving without review

Rules that apply

  • State unfair claims practices and required-notice rules
  • NAIC AI Model Bulletin (consumer notice)
  • GLBA privacy
  • EU AI Act Art. 50 transparency for chat

Runs on

Guidewire ClaimCenterDuck CreekSalesforceMicrosoft Azure OpenAI

Evidence

theregister.com ↗insurancebusinessmag.com ↗

Reference chains are a starting point. Your volumes, models and vendors change the numbers; the engines recompute them from live prices.

Your process is not on the list, or runs differently? These chains are a starting point. We assemble the right engines for your workflow, your models and your vendors, and you run it over MCP or in the calculators.

Business solutions → Build my chain
📖 Data sources & methodology 172 text models · 9 embeddings · 44 vision · 62 audio · 8 vector DBs across 10 vendor pages · last verified 2026-10-15

Methodology

  • All prices are USD per 1 million tokens, current as of 2026-10-15.
  • Vendor-published values have no mark. Inferred/extrapolated values are marked with * and listed below.
  • Batch API discounts are 50% off standard rates across providers that offer Batch mode.
  • Prompt caching discounts vary by provider (typically 80-90% off cached input tokens).
  • Regional data-residency surcharges (Anthropic 1.1x, OpenAI 1.1x, Google regional tiers) are NOT included in base rates.
  • Long-context pricing tiers apply when input exceeds model threshold.
  • Embedding prices are input-only (no output tokens generated).

Primary sources

Last-verified date is the most recent successful daily snapshot (aicost_pricing_snapshots) or, when no snapshot exists yet, the latest successful crawler run (aicost_crawler_runs). 10 of 10 vendors are currently verified. Aggregator services (TokenCost, AI Pricing Guru, etc.) are not listed.

Anthropic
2026-10-14
https://www.anthropic.com/pricing
Daily snapshot since Sep 2023 · 700 days captured
Anthropic Docs
2026-10-14
https://platform.claude.com/docs/en/about-claude/pricing
Daily snapshot since Sep 2023 · 700 days captured
OpenAI
2026-10-14
https://openai.com/api/pricing/
Daily snapshot since Sep 2023 · 701 days captured
Google AI
2026-10-14
https://ai.google.dev/gemini-api/docs/pricing
Daily snapshot since Dec 2023 · 676 days captured
Google Vertex
2026-10-14
https://cloud.google.com/vertex-ai/generative-ai/pricing
Daily snapshot since Dec 2023 · 676 days captured
DeepSeek
2026-10-14
https://api-docs.deepseek.com/quick_start/pricing
Daily snapshot since May 2024 · 615 days captured
xAI
2026-10-14
https://x.ai/api
Daily snapshot since Nov 2024 · 533 days captured
Mistral
2026-10-14
https://mistral.ai/pricing
Daily snapshot since Dec 2023 · 674 days captured
Cohere
2026-10-14
https://cohere.com/pricing
Daily snapshot since Sep 2023 · 700 days captured
Voyage AI
2026-10-15
https://docs.voyageai.com/docs/pricing

Inferred values (marked with * in calculator tables)

Derived from industry conventions, not directly published by the vendor. Typical conventions: cached input = 10% of base (90% off), Batch API = 50% of base (50% off).

Vendor / Model Field Why it’s inferred
Anthropic — Claude Sonnet 4.6 cachedInput Derived at 10% of input rate — Anthropic publishes 90% cache-hit discount on this tier.
Anthropic — Claude Sonnet 4.5 cachedInput Derived at 10% of input rate; same 90% cache-hit convention as Sonnet 4.6.
Anthropic — Claude Sonnet 4.5 batchInput Derived at 50% of standard input — Anthropic documents uniform 50% Batch discount.
Anthropic — Claude Sonnet 4.5 batchOutput Derived at 50% of standard output — Anthropic documents uniform 50% Batch discount.
Anthropic — Claude Haiku 4.5 cachedInput Derived at 10% of input rate — Anthropic 90% cache-hit discount convention.
OpenAI — GPT-5.4 Mini cachedInput Derived at 10% of input — OpenAI documents automatic 90% discount on cache hits across GPT-5.x tier.
OpenAI — GPT-5.4 Nano cachedInput Derived at 10% of input — OpenAI 90% cache-hit convention.
OpenAI — GPT-5.4 Nano batchInput Derived at 50% of input — OpenAI Batch API uniform 50% discount.
OpenAI — GPT-5.4 Nano batchOutput Derived at 50% of output — OpenAI Batch API uniform 50% discount.
OpenAI — GPT-5.4 Pro cachedInput Derived at 10% of input — OpenAI 90% cache-hit convention.
OpenAI — GPT-5.4 Pro batchInput Derived at 50% of input — OpenAI Batch API uniform 50% discount.
OpenAI — GPT-5.4 Pro batchOutput Derived at 50% of output — OpenAI Batch API uniform 50% discount.
OpenAI — GPT-5.2 cachedInput Derived at 10% of input; no residency uplift.
OpenAI — GPT-5.2 batchInput Derived at 50% of input.
OpenAI — GPT-5.2 batchOutput Derived at 50% of output.
OpenAI — GPT-5 cachedInput Derived at 10% of input.
OpenAI — GPT-5 batchInput Derived at 50% of input.
OpenAI — GPT-5 batchOutput Derived at 50% of output.
OpenAI — GPT-5.5 Pro cachedInput Derived at 10% of input — OpenAI does not publish a cached rate for *-pro models; using the family convention.
OpenAI — GPT-5.5 Pro batchInput Derived at 50% of input.
OpenAI — GPT-5.5 Pro batchOutput Derived at 50% of output.
OpenAI — GPT-5.2 Pro cachedInput Derived at 10% of input — pro-tier convention.
OpenAI — GPT-5.2 Pro batchInput Derived at 50% of input.
OpenAI — GPT-5.2 Pro batchOutput Derived at 50% of output.
OpenAI — GPT-5.1 batchInput Derived at 50% of input.
OpenAI — GPT-5.1 batchOutput Derived at 50% of output.
OpenAI — GPT-5 Pro batchInput Derived at 50% of input.
OpenAI — GPT-5 Pro batchOutput Derived at 50% of output.
OpenAI — GPT-5 Nano cachedInput Derived at 10% of input.
OpenAI — GPT-5 Nano batchInput Derived at 50% of input.
OpenAI — GPT-5 Nano batchOutput Derived at 50% of output.
Google — Gemini 3 Flash cachedInput Derived at 10% of input — Google caching discount convention ~90%.
Google — Gemini 3.1 Flash-Lite cachedInput Derived at 10% of input — Google caching convention.
Google — Gemini 3.1 Flash-Lite batchInput Derived at 50% of input — Google Batch API uniform 50% discount.
Google — Gemini 3.1 Flash-Lite batchOutput Derived at 50% of output — Google Batch API uniform 50% discount.
Google — Gemini 2.5 Pro cachedInput Derived at 10% of input.
Google — Gemini 2.5 Flash cachedInput Derived at 10% of input.
Google — Gemini 2.5 Flash-Lite cachedInput Derived at 10% of input — Google caching convention.
Google — Gemini 2.5 Flash-Lite batchInput Derived at 50% of input — Google Batch API uniform 50% discount.
Google — Gemini 2.5 Flash-Lite batchOutput Derived at 50% of output — Google Batch API uniform 50% discount.
xAI — Grok 4 (legacy) cachedInput Extrapolated at 25% of base.

Pricing is cross-verified against the LiteLLM community registry when available. Daily snapshots are kept in aicost_pricing_snapshots; every change is logged to aicost_price_changelog with old & new values for full audit trail. Read the full methodology →