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AICost Industry Solutions

Pharma and biotech

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.
Pharmacovigilance case intake and ICSR processingClinical study report (CSR) and regulatory document draftingMLR (medical, legal, regulatory) content review

Pharmacovigilance case intake and ICSR processing

Owner: Drug Safety / PV operations (case processing, safety physicians), often with an outsourced PV FSP partner

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Scale: Case volumes growing 10-15% a year; PV teams put 40-85% of budget into case processing (The Pharma Letter)

How the work runs

  1. Intake of adverse event reports from email, call-center transcripts, literature, social media, partner feeds and E2B
  2. Triage: validity check (4 minimum criteria), duplicate check, seriousness and expectedness
  3. Data entry and MedDRA/WHODrug coding into the safety database
  4. Case narrative drafting and causality assessment
  5. Medical review and QC by a safety physician
  6. Expedited submission (E2B R3) to regulators within 7/15-day clocks

AICost engine chain

5 cost engines 5 governance engines

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

Where cost leaks

  • Long multi-page source documents (literature, medical records) pushed through large-context models for every case, including duplicates
  • Re-running extraction and narrative generation on every follow-up report instead of only processing the changes
  • Validation and revalidation effort (GxP) after each model or prompt change

Where risk sits

  • Missed or downgraded serious cases that break 15-day expedited timelines
  • Hallucinated narrative details or wrong MedDRA coding in a regulated record
  • Model drift without change control under Part 11 / GxP

Rules that apply

  • FDA 21 CFR 312.32 / 314.80 expedited reporting
  • 21 CFR Part 11 and GxP computerized-system validation
  • EU GVP Module VI
  • ICH E2B(R3)
  • GDPR for patient data
  • FDA Jan 2025 draft guidance on AI credibility for regulatory decision-making

Runs on

Oracle Argus SafetyOracle Safety One IntakeVeeva Vault Safety (AI agents)ArisGlobal LifeSphere (NavaX)IQVIA Vigilance

Evidence

thepharmaletter.com ↗oracle.com ↗ppd.com ↗

Clinical study report (CSR) and regulatory document drafting

Owner: Medical Writing and Regulatory Affairs (submission teams), with Biostatistics and Clinical

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Scale: CSR timelines still 6-15 weeks from final TLFs to approval in a 5-company survey (Applied Clinical Trials, Jan 2025)

How the work runs

  1. Ingest the protocol, SAP and final TLFs (tables, listings, figures)
  2. Generate a first-draft CSR from the TransCelerate template (synopsis, results sections, patient narratives)
  3. Check source-to-text consistency against TLF numbers
  4. Medical writer and cross-functional review cycles
  5. QC, publishing and eCTD Module 2/5 assembly

AICost engine chain

6 cost engines 3 governance engines

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

Where cost leaks

  • Whole TLF packages (hundreds of pages) sent to the model per section and per revision
  • Regenerating full documents on every review cycle instead of only changed sections
  • Use of premium reasoning models for boilerplate sections

Where risk sits

  • Numbers in the text that do not match the TLFs (efficacy or safety misstatement)
  • Unclear authorship and audit trail for regulator questions
  • Confidential unblinded data exposed to non-validated tools

Rules that apply

  • ICH E3 (CSR structure)
  • 21 CFR Part 11 and GxP for regulated records
  • FDA Jan 2025 draft guidance on AI to support regulatory decision-making
  • EMA reflection paper on AI in the medicinal product lifecycle

Runs on

Veeva Vault RIM / Vault SubmissionsCertara (CoAuthor)YseopSAS outputsMedidata (source data)Microsoft Word / DocuBridge publishing

Evidence

appliedclinicaltrialsonline.com ↗certara.com ↗fda.gov ↗

MLR (medical, legal, regulatory) content review

Owner: Commercial / Brand marketing and Medical Affairs content teams, MLR reviewers, Promotional Review Committee

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How the work runs

  1. Agency or brand team uploads promotional or medical content with references
  2. Claims extraction and linking each claim to its supporting reference
  3. Pre-review checks: fair balance, ISI present, label consistency, brand rules
  4. Medical, legal and regulatory reviewer annotation and decision
  5. Approval, expiry tracking and FDA Form 2253 submission

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

  • Every version of every asset (including localized variants) re-reviewed end to end by the model
  • Large reference PDFs re-embedded or re-read per review
  • Agent loops on long video and interactive assets

Where risk sits

  • Off-label or unsubstantiated claims missed by AI pre-check, leading to an OPDP untitled or warning letter
  • Reviewers rubber-stamping AI suggestions
  • Generated content reusing unapproved claims

Rules that apply

  • FDA OPDP rules on promotional labeling (21 CFR 202.1, fair balance)
  • Form FDA 2253
  • EU and national advertising codes (EFPIA, ABPI)
  • 21 CFR Part 11 for approval records

Runs on

Veeva Vault PromoMats (AI agents)Veeva MedCommsIQVIAAdobe Experience Manager

Evidence

intuitionlabs.ai ↗veeva.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 →