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AICost Business Solutions

HR and talent (recruiting, onboarding, employee questions)

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.
Candidate screening and interview scheduling assistantHR help assistant for employee questions and requestsNew-hire onboarding assistant

Candidate screening and interview scheduling assistant

Owner: Talent acquisition / recruiting operations

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Scale: Singtel: about 4,000 hires/year; time-to-hire down 75%, about 5,000 admin hours saved (Workday story)

How the work runs

  1. Candidate applies or chats on the careers site
  2. Assistant answers questions and asks screening questions
  3. AI matches the candidate to suitable roles
  4. Assistant schedules or reschedules interviews and sends reminders
  5. Recruiter reviews the shortlist and decides
  6. Status updated in the ATS

AICost engine chain

3 cost engines 5 governance engines

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

Where cost leaks

  • Every application scored by a large model, including clearly unqualified ones
  • Long SMS/chat threads per candidate
  • Per-hire or per-employee licence pricing

Where risk sits

  • Bias in screening (Mobley v. Workday age-discrimination claim allowed to proceed as collective action in 2025)
  • NYC Local Law 144 and EU AI Act high-risk rules for hiring tools
  • Candidate PII retention

Runs on

  • Workday Recruiting
  • Paradox (Workday)
  • SAP SuccessFactors
  • Greenhouse
  • iCIMS
  • LinkedIn

Evidence

workday.com ↗staffingindustry.com ↗hklaw.com ↗

HR help assistant for employee questions and requests

Owner: HR shared services / people operations

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Scale: IBM AskHR: about 2.1M employee conversations/year, 94% contained, about 80 HR tasks automated (large enterprise, Aug 2025)

How the work runs

  1. Employee asks about pay, leave, benefits or policy
  2. Assistant finds the answer in policies and the employee's own records
  3. Assistant completes simple tasks (letters, address change, leave request)
  4. Sensitive or complex cases go to an HR partner
  5. Conversation logged and gaps fed to policy owners

AICost engine chain

3 cost engines 2 governance engines

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

Where cost leaks

  • Long policy documents loaded into context for each question
  • High chat volume around open enrolment and payroll dates
  • Per-employee licences

Where risk sits

  • Wrong answers on pay, leave or benefits that create legal exposure
  • One employee's data shown to another
  • Sensitive topics (harassment, medical leave) handled by a bot

Runs on

  • Workday
  • SAP SuccessFactors
  • ServiceNow HRSD
  • IBM watsonx Orchestrate
  • Moveworks
  • Microsoft Teams

Evidence

ibm.com ↗

New-hire onboarding assistant

Owner: HR operations / hiring managers, with IT

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How the work runs

  1. Offer accepted in the ATS
  2. Assistant creates onboarding tasks for HR, IT and the manager
  3. Assistant sends paperwork and answers first-week questions
  4. Accounts and equipment requested automatically
  5. Manager notified of progress and gaps

AICost engine chain

4 cost engines 1 governance engines

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

Where cost leaks

  • Assistant kept active and chatty for months per hire
  • Duplicate tools across HR and IT
  • Per-employee licence pricing

Where risk sits

  • Accounts provisioned before checks complete
  • I-9 and tax documents mishandled
  • Wrong policy guidance in the first weeks

Runs on

  • Workday
  • ServiceNow HRSD
  • Moveworks
  • Okta
  • Microsoft Teams

Evidence

moveworks.com ↗oracle.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 →