Opportunities › Model routing transparency for AI-assisted coding › Proposed app
Routewise Ledger
See which AI model answered each coding request, and how each one performed
- Score
- 32
- confidence 42
- Evidence
- 5
- items on the page
- Rivals
- 1
- named in sources
- First version
- 30 d
- estimate
- Type
- Desktop app
- PWA: runs in the browser, installs on Mac and Windows
- Market since
- 2023
- Emerging market · AI estimate
ScreensConcept
Desktop app: Engineers review detailed AI model performance logs, side-by-side comparisons, and team analytics during coding work at their machine. Sample screens on a computer, in the proposed brand. Their numbers are sample data.
Timeline
Auto mode, today148
About this appAI proposal
Routewise Ledger keeps a log of every AI coding response made in automatic model mode. For each answer you see which model produced it, the task type, the time taken and whether you kept, edited or retried the result.
It is for developers and small engineering teams who use AI IDEs with automatic model selection and want to debug a weak answer, check that routing is consistent, and decide when to pick a model by hand.
Unlike the IDE's own chat view, which users report does not show the chosen model, the Ledger sits beside it as a timeline you can search, compare and share with your team.
- Tagline
- Know which model answered. Decide what to trust.
- Voice
- ClearPlainspokenPrecise
- Why this name
- Route points to model routing; Ledger says every answer is recorded and can be checked.
In the first version
Members say which features they would use. It tells PWAS what people want; it changes no score.
Model tag on every response
Each logged answer shows the model that handled it, so a poor result can be traced to its source in seconds.
No member yet
Retry with a chosen model
Copy the prompt with a pinned model suggestion to retry the same request and compare the two answers.
No member yet
Task-type breakdown
Group responses by task, such as refactor, test writing or explain, and see which model handled most of each.
No member yet
Side-by-side comparison
Put two answers to the same prompt next to each other with model, time and your keep or discard verdict.
No member yet
Brand identityAI proposal
Check the name against trademark registers before using it.
Palette
#0F4C5CDeep Teal · primary#F6F3ECPaper · surface#1B2528Ink · text#E8A33DAmber · accent#5B8C5AMoss · data#B5482FBrick · warning
Typography
Aa
Routewise Ledger
PricingAI proposal
The material gives no competitor prices. These tiers are a starting point to test, based on the per-seat subscription idea in the opportunity.
Solo
Free
- One developer
- 30 days of history
- Model tag timeline
Pro
$9 a month
- Unlimited history
- Compare view
- Task breakdown
- Share single cases
Team
$19 a month
- Per seat
- Shared workspace
- Team usage and routing views
- Export to CSV
Would you pay Pro · $9 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. Developers publish a link to a single response record, with model and verdict, in bug reports and issue threads. Readers see the Ledger name on the shared page.
- Do they become customers?
- Readers are developers in the same issue threads and could become users. Test whether shared links lead to installs and whether those people then pay.
- First customers
- Engineering teams of 5 to 30 developers who are evaluating or newly rolling out an AI IDE with automatic model selection.
- Where to reach them
- GitHub issue threads: Reply in open issues about hidden model selection, offering the free tier to the people who reacted.
- Developer communities: Post a short write-up of routing findings from real logs in forums and newsletters for AI coding users.
- First milestone
- Five teams using the Ledger weekly and three paying for Team seats within 60 days.
Why people already pay for this
- VerifiedOpen GitHub issue has 43 reactions and 5 comments
- Public claimDevelopers using auto model selection in AI IDEs cannot see which model was chosen for a given response
- Public claimKnowing which model handled a response is important for debugging quality issues
- Public claimLack of transparency in automatic model selection reduces developer confidence in the automation
- 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 · IDE captureEditor extension or local proxy that records model usedA PWA cannot see IDE responses; a small companion must send each response's model tag and timing.
- This app · ChartsLightweight charting libraryTask breakdown and routing consistency views need simple charts.
RisksAI inference
- PlatformThe IDE may not expose the chosen model to extensions or proxies, so capture may be impossible or break with updates.
- CommercialThe IDE vendor could add a model label itself, removing the main reason to pay; the value must rest on comparison and team views.
- DataLogging prompts and code raises privacy concerns; teams may require storing only metadata, not content.
- TechnicalJudging quality by task type needs reliable task classification, which may be noisy at first.
- CommercialDemand evidence is one GitHub issue with 43 reactions; willingness to pay is untested.
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.