Opportunities › AI-powered diabetes data analysis and insights › Proposed app
Tidewise
Plain-language daily briefings from your glucose and pump data, between clinic visits
- Score
- 28
- confidence 52
- Evidence
- 6
- items on the page
- Rivals
- 3
- named in sources
- First version
- 45 d
- estimate
- Type
- Mobile app
- PWA: installs on iPhone and Android
- Market since
- 2006
- Mature market · AI estimate
ScreensConcept
Mobile app: Users check daily glucose briefings, meal responses, and ask quick questions about their data on the go between clinic visits. Sample screens on a phone, in the proposed brand. Their numbers are sample data.
Morning briefing
78% in range
- Overnight lowNone
- Time above range16%
- Time below range6%
- Average glucose142 mg/dL
- Pattern noticedPost-lunch rise
About this appAI proposal
Tidewise brings glucose sensor readings, pump records and data from platforms you already use into one place. Each morning it writes a short briefing: what happened overnight, where you spent time out of range, and which meals went differently than usual. You can then ask follow-up questions in plain language.
It is built for people with type 1 diabetes who go months between clinician reviews, or who want to understand their own patterns. Tidewise explains and suggests questions to bring to your care team. It does not dose insulin or control a pump.
Your data stays under your control: you choose what is imported, you can export or delete it at any time, and nothing is shared without your explicit action.
- Tagline
- Understand your glucose data, one calm briefing at a time.
- Voice
- CalmClearHonest
- Why this name
- Tides rise and fall in patterns you can learn to read, like glucose. Wise suggests understanding, not control.
In the first version
Members say which features they would use. It tells PWAS what people want; it changes no score.
Morning briefing
A short written summary of the last 24 hours: overnight trend, lows and highs, and one pattern worth noticing.
No member yet
Meal response view
Compare how similar meals moved your glucose, with logged carbs, bolus timing and the resulting curve side by side.
No member yet
Ask your data
Chat in plain language: why was I high after lunch this week? Answers cite the readings they come from.
No member yet
One place for many sources
Combine sensor, pump and existing diabetes data platform feeds into a single timeline.
No member yet
Brand identityAI proposal
Check the name against trademark registers before using it.
Palette
#12465ADeep Tide · primary#F2F7F5Sea Foam · surface#14262EInk · text#E8664DCoral · accent / alerts#3F9B7AKelp · in-range data#E9D8A6Sand · highlight
Typography
Aa
Tidewise
ABCDEFGHIJKLMNOPQRSTUVWXYZ 0123456789
PricingAI proposal
The material gives no competitor prices. These tiers are a starting point to test, based on the freemium and caregiver-tier ideas in the opportunity.
Free
Free
- One data source
- Daily briefing
- 7-day history
- Export and delete
Plus
$9 a month
- All data sources
- Ask your data chat
- Meal response view
- Clinic visit reports
- Pattern alerts
Family
$15 a month
- Everything in Plus
- Up to 3 caregiver viewers
- Caregiver alert sharing
Would you pay Plus · $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
$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
- Shared outputs. Users share the visit report and caregiver summaries with clinicians and family. Each report carries a small Tidewise mark and a link to see how it was made.
- Do they become customers?
- Clinicians and caregivers who see a report may become users themselves, but this is unproven. Test whether report recipients click through and whether caregivers convert to Family plans.
- First customers
- Adults with type 1 diabetes who use a CGM and pump and already share data through an open-source platform, and who see an endocrinologist at most twice a year.
- Where to reach them
- Hacker News and open-source forums: Share a build log and a demo with sample data in the communities where the original discussion happened.
- Type 1 diabetes online communities: Offer a free beta to members and ask for feedback on the briefing and meal view.
- Diabetes educators: Invite a few educators to try the visit report with their patients.
- First milestone
- Fifty beta users open the briefing on at least five days a week for a month, and ten of them pay for Plus.
Why people already pay for this
- VerifiedHacker News post received 64 points and 61 comments
- Public claimType 1 diabetics sometimes go extended periods without professional clinician review of their diabetes management data.
- Public claimExisting tools do not adequately serve the need for AI-powered analysis and insights across integrated diabetes device data.
- AI inferenceThere is demand for diabetes management tools that provide AI analysis, daily briefing summaries, and meal response analysis.
- AI inference
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 · AI analysisHosted language model API with glucose-specific promptsWrites briefings and answers questions from structured data; needs strict guardrails and cited readings.
- This app · Device data connectorsImporters for cloud CGM, pump and open-source platform feedsCombining sources is the core need; each source has its own format and access rules.
RisksAI inference
- RegulatoryGlucose insight and predictive alerts may be treated as a regulated medical device function depending on wording and claims. Needs legal review; keep it educational and never recommend doses.
- PlatformAccess to manufacturer data may be restricted, delayed or change terms. Reliable ingestion could depend on third-party APIs or user-run platforms.
- AIThe model could give a wrong or misleading explanation of a pattern. Answers need cited readings, uncertainty language and clear limits on safety-critical advice.
- DataHealth data is sensitive. Storing it in the shared cloud stack conflicts with the privacy and self-hosting preference seen in the evidence, which is only an inference.
- CommercialUsers may expect this free from open-source communities, and manufacturers may add similar AI features to their own apps.
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.