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AICost Business Solutions

Finance and accounting (AP/AR, close, FP&A)

3 AI automations this team 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.
Touchless invoice processing in accounts payableMonth-end close: reconciliations, journal entries and variance commentaryCollections and cash application in accounts receivable

Touchless invoice processing in accounts payable

Owner: AP manager / controller

See a chain run live Build this chain for our stack
Scale: Genpact client reached about 80% touchless, cycle time from 3+ days to under 1 day

How the work runs

  1. Supplier invoice arrives by email, portal or EDI
  2. AI reads the invoice and pulls header and line data
  3. AI matches to PO and receipt and codes GL and tax
  4. Exceptions (price, quantity, missing PO) go to an AP clerk with a suggested fix
  5. Invoice posted to the ERP and scheduled for payment

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

  • Re-processing the same invoice on every exception round
  • Multi-page invoices sent in full to vision models
  • Human review time on low-confidence extractions

Where risk sits

  • Duplicate or fraudulent invoices paid (vendor bank-detail fraud)
  • Wrong GL or tax coding that hits the audit
  • Segregation-of-duties gaps when the agent approves and posts

Runs on

  • SAP
  • Oracle
  • NetSuite
  • Coupa
  • Basware
  • Tipalti
  • Genpact AP Suite

Evidence

genpact.com ↗deloitte.com ↗baincapitalventures.com ↗

Month-end close: reconciliations, journal entries and variance commentary

Owner: Controller / accounting and FP&A teams

See a chain run live Build this chain for our stack
Scale: one CFO cut monthly journal-entry prep from 20 hours to 2, per Bain Capital Ventures

How the work runs

  1. Close checklist kicks off
  2. AI matches bank and sub-ledger items and flags breaks
  3. AI prepares recurring and accrual journal entries for approval
  4. AI drafts flux and budget-vs-actual commentary
  5. Accountant and FP&A review, edit and sign off
  6. Package goes to CFO and auditors

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 trial balance and transaction detail sent to the model for every commentary draft
  • Commentary regenerated many times during review
  • Close add-on licences priced per entity or per user

Where risk sits

  • Wrong journal entries posted under SOX controls
  • Invented explanations for variances in board or investor materials
  • Material non-public financial data leaving the company

Runs on

  • NetSuite
  • SAP
  • Oracle
  • BlackLine
  • FloQast
  • HighRadius
  • Anaplan

Evidence

baincapitalventures.com ↗floqast.com ↗highradius.com ↗

Collections and cash application in accounts receivable

Owner: AR / credit and collections team

See a chain run live Build this chain for our stack

How the work runs

  1. AI ranks overdue accounts by risk
  2. AI drafts and sends dunning emails and answers customer replies
  3. AI matches incoming payments and remittances to open invoices
  4. Disputes and short-pays routed to a collector
  5. Cash posted in the ERP and DSO tracked

AICost engine chain

4 cost engines 2 governance engines

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

Where cost leaks

  • Reading every customer email thread in full on each pass
  • Remittance PDFs processed repeatedly
  • Platform pricing tied to invoice or payment volume

Where risk sits

  • Aggressive or wrong collection messages to key customers
  • Payments applied to the wrong account
  • Customer financial data exposure

Runs on

  • HighRadius
  • SAP
  • Oracle
  • NetSuite
  • Salesforce
  • Billtrust

Evidence

highradius.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.

Industry 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 →