Opportunities › Uncertainty quantification for language models › Proposed app
Hedgemark
Confidence scores for every claim your language model makes, before users see them
- Score
- 54
- confidence 46
- Evidence
- 3
- items on the page
- Rivals
- 0
- 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: ML teams configure threshold rules, calibrate confidence scores on large datasets, and review quality—analysis and tuning requiring tables and careful review. Sample screens on a computer, in the proposed brand. Their numbers are sample data.
Answer review
2 claims flagged3 of 5 solid
About this appAI proposal
Hedgemark sits between your application and your language model. It splits each answer into individual claims, scores how likely each one is to be right, and returns the answer with those scores attached. Your code can then show, flag, or hold back the shaky parts instead of treating every sentence alike.
It is built for teams that run language models in production on tasks where a wrong answer costs something: support, research summaries, internal knowledge search, document extraction. You set a threshold per task and decide what happens below it: re-ask, cite a source, route to a person, or add a caveat.
Unlike a single overall score, Hedgemark works claim by claim and lets you check its calibration against your own labelled examples. You see how often 80 percent confident claims were actually right, on your data, before you trust it.
- Tagline
- Know which claims to trust.
- Voice
- PlainCarefulDirect
- Why this name
- A hedge is how people admit doubt, and a mark is what you put beside a claim. Together: a visible sign of how sure the model is.
In the first version
Members say which features they would use. It tells PWAS what people want; it changes no score.
Claim-level confidence
Each answer is split into separate claims, each with its own confidence score and a plain label: solid, mixed or doubtful.
No member yet
Drop-in proxy and SDK
Point your existing model calls at one endpoint, or use the TypeScript SDK. The answer comes back with scores attached.
No member yet
Threshold rules
Set what happens below a score per task: add a caveat, re-ask the model, hide the claim, or send it to a reviewer.
No member yet
Calibration check on your data
Upload labelled examples and see a reliability chart showing whether stated confidence matches how often claims were right.
No member yet
Brand identityAI proposal
Check the name against trademark registers before using it.
Palette
#14213DDeep Ink · primary and text#FAF6EEPaper · surface#E09F1FSignal Amber · accent, doubtful#3F7D58Moss · data, solid#B5412FBrick · data, wrong#6B7280Stone · secondary text
Typography
Aa
Hedgemark
PricingAI proposal
The material gives no competitor prices. These tiers are a starting point to test, with usage-based pricing per scored answer suggested by the monetization notes.
Free
Free
- 1,000 scored answers a month
- One project
- Calibration report
Team
$99 a month
- 50,000 scored answers a month
- Threshold rules
- Review queue, 5 seats
- Drift alerts
Scale
$499 a month
- 500,000 scored answers a month
- Unlimited seats
- Priority support
- Audit export
Would you pay Team · $99 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
$190,000
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
- Visible inside customers’ products. Customers can show Hedgemark confidence badges beside answers in their own products. End users see the badge and a link to what the labels mean.
- Do they become customers?
- Viewers are mostly end users, not buyers. Some may be developers at other companies, but that needs testing. The main route is likely direct sales and content to teams.
- First customers
- Small engineering teams of 3 to 15 at software companies shipping an AI support or knowledge-search assistant to paying customers.
- Where to reach them
- Hacker News and developer forums: Post a worked calibration example on a public dataset and invite teams to try the free tier on their own outputs.
- Direct outreach: Message engineering leads at companies advertising AI assistants and offer a free calibration report on their examples.
- First milestone
- Five teams in one vertical run it on live traffic for a month, and three pay for the Team tier.
Why people already pay for this
- Public claimUsers would pay for reliable uncertainty quantification in language models, indicating market demand.
- Public claimLanguage models produce hallucinations and false information, demonstrating the severity of the reliability problem.
- Public claimAn LLM with accurate uncertainty prediction would solve a significant market need, showing market validation.
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 · Claim scoringSampling-based consistency checks plus a calibration modelScores come from comparing repeated model samples and learning from labelled verdicts, which needs a small scoring service.
- This app · Background jobsQueue worker for scoring and drift checksScoring several samples per answer is slow and should run off the request path.
RisksAI inference
- AIScores may not be well calibrated on every domain. If confidence does not track correctness, the product fails its one promise, so the calibration check must be honest and visible.
- CommercialModel providers or existing evaluation tools may add confidence features, and teams may build simple checks in-house. The edge must come from claim-level scoring and calibration on customer data.
- TechnicalSampling several times per answer raises cost and latency. Teams with tight response times may reject the overhead.
- DataCustomers send model inputs and outputs that may contain sensitive text. Retention limits and clear data handling terms are needed before enterprise use.
- CommercialEvidence is only public claims from one forum; willingness to pay is unverified and must be tested with real buyers.
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.