Zigzagging...
Synchronizing Ledger Data
Zigzagging...
Synchronizing Ledger Data
Case Ledger is the decision layer for AI adoption — a searchable directory of verified use cases, deployable agents & workflow templates, priced in outcomes, not promises.
Every operator asks the same question — has anyone actually shipped this, and did it work? — and gets the same answer: a slide deck, a vendor case study, a LinkedIn post. Case Ledger replaces the anecdote economy with source-backed records.
A GraphRAG-powered search layer turns a plain-language question into a shortlist of comparable, deployable records. No framework diagrams required.
Search semantically across 4,200+ verified cases. The graph understands industries, functions, maturity, and budget — so "claims triage for mid-market P&C" returns the right neighborhood, not the right words.
Every case is a structured record: baseline, intervention, outcome, cost, caveats, sources. ROI is normalized, not marketing. Failures are recorded alongside wins so the sample isn't survivor-biased.
Cases are indexed across industry, function, workflow, and maturity — so the same record surfaces whether you're searching as a CRO, a claims VP, or an automation architect.
ROI claims are only as good as the evidence under them. Case Ledger publishes its methodology, versions its models, and tags every record with its sourcing grade — so skepticism is a feature, not an objection.
Each record is graded A (primary — SEC filings, earnings transcripts, first-party interviews), B (verified secondary), or C (self-reported, flagged). The grade rides alongside the ROI.
We translate every outcome into a common unit — hours saved, error reduction, revenue captured, risk averted — using an open model with a 14-page defense. Your finance team can challenge the inputs.
We index what didn't ship, what rolled back, and what quietly underperformed. A directory that only lists winners isn't a directory — it's a brochure.
Agents, flows, and prompt packs — vendor-neutral, forkable, versioned. The research and the artifact live in the same record, so validation and deployment aren't separate projects.
Pre-built automation agents, n8n flows, and prompt packs are being indexed and validated against ledger records. Every artifact ships with its source case, performance assumptions, and a verified ROI range — not a marketing claim.
Browse automations →A lighter version of the full calculator, running on the same ROI model. Adjust the core assumptions here, then open the full tool when you want to tune the deeper cost profile.
Lighter assumptions, same calculator engine
Effective automation rate: 58.0% in the base scenario.
This light version still models labor capture, rollout ramp, and quality drag from automation escapes.
Included default assumptions
Annual software cost
$36,000
Annual maintenance cost
$18,000
Error rate
3.0%
Cost per error
$85
Automation escape rate
0.9%
Open the full calculator if you want to tune software, maintenance, error-cost, and exception-handling assumptions directly.
Projected outcome
Steady-state net annual benefit
$298,106
Estimated payback
12.0 months
Year 1 net return
-$1,118
Year 1 ROI / multiple
-0.2% / 0.92x
Steady-state labor benefit
$541,070
Net quality benefit after escapes
$124,236
Year 1 absorbs implementation cost and rollout drag. The steady-state figure is the mature run-rate, not an instant day-one outcome.
4,217 verified AI use cases, indexed by outcome. Free to search. Paid to ship.
Search verified AI use cases, compare evidence quality, unlock deeper ROI data, and buy automation packs when a workflow is ready to deploy.
Each record links to agents, workflow templates, and prompt sets that implement it — vendor-neutral, forkable, versioned. Go from "interesting" to pilot-ready without starting from a blank document.
AI skill that converts YouTube videos into short-form scripts and carousel draft...
Every record has an immutable snapshot ID. Link it in a memo, cite it in a board deck — and anyone clicking through sees exactly what you saw.