Banks, consultants and PE deal teams will pay most for retrieved, source-cited charts — provenance is the product
Asked (summary):
Which professional workflows producing articles, reports, memos or presentations with charts would benefit most from an API that retrieves real source-cited charts for AI agents instead of generating them? Rank the workflows by willingness to pay and name the buyer.
Evidence: 20 consolidated high-priority workflow rows (distilled from a 100-row relevance shortlist and 2,000+ candidate pages across finance, consulting, regulated industries and publishing), spanning 16 independent hosts. Willingness-to-pay scores (0–10) are directional market-prioritization judgments, not survey results; prices quoted are market signals, not rate cards. The chart below consolidates narrow variants into eight ranked workflow categories.
Willingness to pay by consolidated workflow
Directional WTP score, 0–10 · buyer named on each bar · colour marks how much charting in the workflow uses externally published sources (blue) versus proprietary internal data (orange)
Where retrieval beats generation: the launch zone
Willingness to pay vs external published-chart intensity (directional). Upper right is the best launch zone; pharma regulatory and ESG have budget but mostly chart proprietary internal data.
Capital-markets teams already pay $12,000–$25,000 per research seat and small errors can invalidate multibillion-dollar deal documents — rubrics explicitly grade data provenance. ctacquisitions.com · arxiv.org
Consulting engagements run $50,000–$500,000+ (up to $3,750 per page) and “the credibility of the source matters as much as the analysis itself.” userintuition.ai
In PE diligence, every extracted value must link back to its exact source location for analyst validation and LP integrity — a hard requirement retrieval satisfies and generation cannot. v7labs.com
Rights are the gate for client-facing output: a platform whose charts cannot legally appear in a client deck or LP report is “wasted spend,” so licensing metadata must travel with every asset. forage.ai
Method: 20 consolidated candidate workflow rows (primary evidence) ranked by directional willingness-to-pay score (0–10), distilled from a 100-row assessed shortlist drawn from 2,000+ candidate evidence rows across three web scans (finance/consulting/intelligence; pharma/legal/ESG/audit; journalism/corporate/policy/academic). Scores are market-prioritization judgments, not survey data; dollar figures are market signals from industry and vendor pages, not audited price lists. Narrow investment-banking and financial-research variants were consolidated into eight categories for the visuals; external-chart intensity in the matrix is an editorial judgment from the rows' provenance notes. Rationale and adoption-signal columns were cut for space; each row links its source page.