Opportunities › Local-first automation for finance workflows › Proposed app
Ledgerloom
Design a finance workflow once with AI, then re-run it monthly in your browser
- Score
- 25
- confidence 45
- Evidence
- 8
- items on the page
- Rivals
- 2
- named in sources
- First version
- 45 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: Finance professionals design workflows on a canvas, write SQL, review audit-ready reports, and manage reconciliations—complex data work at a desk. Sample screens on a computer, in the proposed brand. Their numbers are sample data.
Bank reconciliation
Runs locally- 1 Load bank CSV
About this appAI proposal
Ledgerloom is a workflow builder for finance teams. You describe a recurring task, such as a monthly reconciliation or a variance report, and an AI model drafts the steps and the SQL once. After that, the workflow runs in your browser with no AI call and no per-run token charge.
Every step sits on a visual canvas with its SQL and a preview of the data it produces, so you can read and question each step before you trust it. Pull data from QuickBooks, FRED economic series and stock prices, then export an audit-ready record of what ran.
It is for finance operations staff, analysts and M&A practitioners who rebuild the same Excel and M code each month, or who dislike cloud automation they cannot inspect.
- Tagline
- Design it once. Run it every month. Check every step.
- Voice
- PrecisePlainspokenCalm
- Why this name
- A ledger is the finance record; a loom weaves repeated threads into one dependable cloth, like steps woven into a workflow.
In the first version
Members say which features they would use. It tells PWAS what people want; it changes no score.
Design once, run free
The AI is used only to draft a workflow. Saved workflows run locally in the browser, so monthly re-runs carry no token cost.
No member yet
Visual canvas with real SQL
Each step shows its SQL and a data preview, so you see exactly what happens to the numbers at every stage.
No member yet
Data stays in your browser
Files and query results are processed on your device. Only your description of the task is sent for the design step.
No member yet
QuickBooks, FRED and stock data
Connect accounting records, economic series and market prices as sources instead of exporting and pasting by hand.
No member yet
Brand identityAI proposal
Check the name against trademark registers before using it.
Palette
#12372AInk Green · primary#F6F3EAPaper · surface#1C1F1DLedger Text · text#C48A1ABrass · accent#7FA28CSage · data#B2432FBrick · exception
Typography
Aa
Ledgerloom
ABCDEFGHIJKLMNOPQRSTUVWXYZ 0123456789
PricingAI proposal
The material gives no competitor prices. These tiers are a starting point to test; low cost after the design step is what could support them.
Free
Free
- 2 saved workflows
- Local runs
- Manual file import
- Limited AI drafts
Pro
$29 a month
- Unlimited saved workflows
- QuickBooks, FRED, stock data
- Audit-ready exports
- More AI drafts
Team
$24 a month
- Per seat, 3 seat minimum
- Shared workflow library
- Review and approval of changes
- Everything in Pro
Would you pay Team · $24 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. Audit exports and run reports go to reviewers, auditors and managers. Each carries a small note naming the tool and a link to view the workflow's steps.
- Do they become customers?
- Reviewers are often finance people who build their own workflows, so some could become users. Test whether recipients open the link and sign up.
- First customers
- Fractional CFOs and small finance teams of 1 to 5 people who close books monthly in QuickBooks and rebuild reconciliations in Excel.
- Where to reach them
- Hacker News and finance forums: Share a demo of one reconciliation built and re-run with zero tokens, and ask for workflows to automate.
- QuickBooks user communities: Offer a free monthly reconciliation template and invite users to try it.
- Direct outreach to fractional CFOs: Offer a free setup call to build their most repeated monthly workflow.
- First milestone
- Ten finance users each save a workflow and re-run it for a second month, and five pay for Pro.
Why people already pay for this
- VerifiedHacker News post received 43 points and 16 comments
- Public claimFinance professionals spend significant time on repetitive workflows and need to re-run them monthly with minimal overhead.
- Public claimExisting tools lack transparency into workflow execution logic and data handling.
- Public claimFinance users require audit-ready exports for compliance or reporting purposes.
- Public claim
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 · In-browser SQL engineWebAssembly SQL database (e.g. DuckDB-Wasm)Runs the saved SQL on the user's device so re-runs need no server or token.
- This app · LLM accessHosted LLM API for workflow design onlyUsed once per draft to generate steps and SQL; usage is capped per plan.
RisksAI inference
- TechnicalLarge ledgers may exceed browser memory or speed, and in-browser SQL differs from the dialects finance users already know.
- AIAI-drafted SQL can be wrong while looking plausible. Users must review each step, so previews and diffs must make errors easy to spot.
- PlatformQuickBooks and market data APIs have terms, rate limits and approval steps, and browser-side calls may need a proxy for secrets.
- CommercialFree tools and spreadsheets are the default. Teams may not pay unless audit exports clearly save review time.
- RegulatoryAudit-ready claims must hold up with real reviewers, and handling financial data may bring customer security questionnaires.
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.