Opportunities › AI credit refunds for incorrect outputs › Proposed app
Tallyback
Check AI outputs, log wasted spend, and file evidence-backed credit claims
- Score
- 32
- confidence 42
- Evidence
- 4
- items on the page
- Rivals
- 2
- named in sources
- First version
- 35 d
- estimate
- Type
- Desktop app
- PWA: runs in the browser, installs on Mac and Windows
- Market since
- no product yet
- New market · AI estimate
ScreensConcept
Desktop app: Reviewing AI output quality, analyzing spend, and filing claims involve dashboards and ledgers—back-office analytics work. Sample screens on a computer, in the proposed brand. Their numbers are sample data.
Wasted spend
October$184.20
About this appAI proposal
Tallyback sits between your team and the AI services you pay for. It runs cheap automatic checks on answers that matter (tests, schemas, source checks, a second-opinion pass) and marks each call as passed, failed or unsure.
Every failed call goes into a wasted-spend ledger with the prompt, the output, the failed check and the cost. You see what mistakes cost you each month, and you get an evidence pack to send to a provider when you ask for a credit.
It does not promise that providers will refund you. It documents the case, caps verification spend, and shows which models and tasks fail most. You bring your own keys.
- Tagline
- Pay for answers that hold up.
- Voice
- PlainFairPrecise
- Why this name
- A tally counts what was wasted; back means getting it back. Two plain words, easy to say, and it describes the ledger and claim idea.
In the first version
Members say which features they would use. It tells PWAS what people want; it changes no score.
Automatic output checks
Attach checks to a task: run tests, validate a JSON schema, confirm quoted sources exist, or ask a cheaper model to review.
No member yet
Wasted-spend ledger
Each failed call is logged with its cost, so you can see what incorrect answers cost per project, model and week.
No member yet
Claim evidence packs
Export a one-page record of the prompt, output, failed check and cost, ready to send with a credit request.
No member yet
Verification budget caps
Set a ceiling on what checking may cost per task, so verifying a result never costs more than the work itself.
No member yet
Brand identityAI proposal
Check the name against trademark registers before using it.
Palette
#1F5B46Ledger Green · primary#FAF6EEPaper · surface#1F2A24Ink · text#D98E04Amber · accent#2F6F8FSlate Teal · data#B23A2EFlag Red · failed status
Typography
Aa
Tallyback
PricingAI proposal
The material gives no competitor prices for checking or refunds. These tiers are a starting point to test, not based on market data.
Free
Free
- 500 checked calls a month
- Wasted-spend ledger
- Schema and test checks
Team
$29 a month
- 20,000 checked calls a month
- Second-opinion checks
- Evidence pack export
- Verification budget caps
Business
$99 a month
- 150,000 checked calls a month
- Reliability board
- Shared workspaces
- Priority support
Would you pay Team · $29 a month?
Your answer tells PWAS which prices people accept. It changes no score.
No answers yet.
What it could be worthEstimate
What a software business making this much sells for today, from real deals. Pick a monthly revenue.
If it makes
Worth about
$194,000
Sale range $132,000 to $263,000
$120,000 a year in revenue
From 87 software businesses listed and sold on Empire Flippers in the last year, revenue vetted by the broker: they sell for about 19× their monthly revenue, most between 13× and 26×.
Measured on profit, the middle deal is 32× monthly net profit: about 2.7 years of profit.
An estimate, not an offer or financial advice. A real price depends on profit, growth, churn, how much of the work is the owner's, and the buyer.
How it would growAI proposal
How the product would reach new customers through its own use, and how the first ones would be found. A plan to test, not a result.
- Built-in loop
- Shared outputs. Users export evidence packs and reliability reports and send them to providers, managers or clients. Each export carries a small Tallyback mark and a link to the public report page.
- Do they become customers?
- Recipients are mostly provider support staff, who will not buy. Managers and teammates who receive reports might sign up. Test whether report views turn into trials.
- First customers
- Small developer teams of 3 to 15 people who run AI coding or data extraction agents on API keys and already feel the cost of retries.
- Where to reach them
- Hacker News: Share a Show HN post and a free ledger tool that imports usage exports, building on the thread where users complained about paying twice.
- Developer communities: Post worked examples of wasted-spend reports in AI coding and agent forums and Discord servers.
- First milestone
- Ten teams use the free ledger for a month and five move to a paid Team plan.
Why people already pay for this
- AI inferenceExisting subscription models lack error-based refund mechanisms
- VerifiedHacker News post received 21 points and 23 comments
- Public claimUsers must pay or lose usage to verify AI mistakes
- Public claimPaying twice for the same task feels unfair
Tech stack
Every proposal builds on the same PWA base, installable from the browser on any phone or computer. Dashed boxes are what this app adds.
- AppNext.js and TypeScript
- PWAManifest and service worker
- UITailwind CSS
- Data and authSupabase Postgres
- PaymentsStripe
- EmailResend
- HostingVercel
- This app · Proxy gatewayEdge proxy for AI provider APIsRecords each call, its cost and output without changing how the user's code calls the provider.
- This app · Background jobsQueue for check runsChecks and second-opinion passes run after the call and must retry safely.
RisksAI inference
- CommercialProviders may not honour claims. If credits are never granted, the value falls back to cost visibility and caps, which must be worth paying for alone.
- AIAutomatic checks can be wrong. A second-opinion model may pass bad answers or fail good ones, so users may distrust the ledger.
- PlatformProvider terms may limit proxying or reselling usage data, and providers could add their own error credits or usage reports.
- DataStoring prompts and outputs holds sensitive customer data. Retention limits, redaction and encryption are needed from day one.
- TechnicalMany tasks have no objective check. Coverage may be limited to code, structured data and sourced facts at first.
Everything marked AI proposal or AI inference was written by a model from the evidence on the opportunity page. The proposed name must be checked against trademark registers before it is used.