Opportunities › Smell-based quality monitoring for industrial facilities › Proposed app
Nosegate
Train, watch and act on smell-sensor readings across plants, silos and warehouses
- Score
- 42
- confidence 47
- Evidence
- 6
- items on the page
- Rivals
- 0
- named in sources
- First version
- 55 d
- estimate
- Type
- Desktop and mobile
- PWA: on computers and phones alike
- Market since
- 1993
- Mature market · AI estimate
ScreensConcept
Desktop and mobile: QA teams set up sensor zones and train models at desks, while inspectors perform field check-ins on-site to verify alerts and log observations in real time. Sample screens on a computer and a phone, in the proposed brand. Their numbers are sample data.
Zones
Updated 2 min ago2 need a look
About this appAI proposal
Nosegate is the software layer for gas-sensor units placed in food plants, grain stores, warehouses and factories. Sensors send readings through an API. Your QA team labels what normal and bad smells look like, trains a model, and gets alerts when a zone drifts away from normal.
Instead of a fixed threshold or a single-purpose device, each zone gets its own trainable profile: mould in a silo, spoilage in a cold room, an off-note on a line. Retrain it from the phone when the product or season changes, with no hardware redesign.
Readings, labels, alerts and inspector notes sit in one log, and results go out by API or export to the QA and automation systems you already run.
- Tagline
- Catch the smell of trouble before it becomes a loss.
- Voice
- PlainCalmPrecise
- Why this name
- A gate every batch and zone must pass, and a nod to the nose. Two plain syllables joined, easy to say on a plant floor.
In the first version
Members say which features they would use. It tells PWAS what people want; it changes no score.
Zone profiles you train yourself
Label sample readings as normal or a named problem, then train a model for that zone. Retrain when the product, season or room changes.
No member yet
Early drift alerts
Get a push alert when a zone moves away from its normal pattern, before a person would notice it on a round.
No member yet
Classify, not just threshold
Each alert names the likely class, such as spoilage or contamination, with a confidence score and the readings behind it.
No member yet
Inspector check-ins
Log a manual check against a zone. Your notes become labels that make the next model better.
No member yet
Brand identityAI proposal
Check the name against trademark registers before using it.
Palette
#2F6B4FSilo Green · primary#F6F3ECFlour · surface#1F2A24Soil Ink · text#D9822BAmber Grain · accent#3D6A8ASteel Blue · data#B23A2EAlarm Red · alert
Typography
Aa
Nosegate
PricingAI proposal
The material gives no competitor prices. These tiers are a starting point to test. Sensor hardware is sold separately by its maker, and Nosegate prices only the software.
Pilot
Free
- 1 site, up to 3 zones
- 7-day reading history
- Email alerts
- Manual CSV export
Site
$249 a month
- 1 site, up to 20 zones
- Model training and retraining
- Push alerts and webhooks
- 1-year history and audit trail
Multi-site
$799 a month
- Up to 10 sites
- Full API access
- Roles and approvals
- Fleet health reports
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
- No loop in the product. Nosegate reaches customers through direct sales, content and sensor-maker partners. The product is used inside plants, and its alerts are internal, so its own use does not spread it to new buyers.
- Do they become customers?
- Test whether sensor makers or integrators will bundle or recommend it. Test whether a QA lead who sees a pilot works at, or moves to, another site and buys again.
- First customers
- QA managers at mid-size grain storage and food processing sites that already run gas sensors or do scheduled manual smell and spot checks.
- Where to reach them
- Sensor makers and integrators: Offer the app to hardware builders and integrators who need a deployable software layer for their units.
- Industry groups and LinkedIn: Reach grain and food QA managers with pilot case write-ups on drift detection.
- Direct outreach: Email plant QA leads and offer a free three-zone pilot with a set-up call.
- First milestone
- Three grain or food sites run a pilot for 60 days, and at least two pay for the Site tier.
Why people already pay for this
- VerifiedHacker News post received 34 points and 25 comments
- Public claimExisting systems for smell-based monitoring are rigid and laboratory-focused, failing to translate into production-ready deployments that can be integrated and maintained in real industrial environments.
- Public claimThe same hardware and software platform can be retrained for different classification and monitoring tasks without requiring physical redesign.
- AI inferenceIndustrial facilities need to detect early signs of contamination, spoilage, and process anomalies in large-scale storage and production environments.
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 · Sensor ingestHTTPS and MQTT endpoint for device readingsGas-sensor units must push frequent readings reliably, with device keys and retry.
- This app · Time-series storagePartitioned Postgres tables or time-series extensionReadings arrive continuously and need fast range queries and retention rules.
RisksAI inference
- TechnicalGas-sensor readings drift with temperature, humidity and sensor age. Models trained in one room may fail in another, causing false alarms or misses.
- DataUseful models need labelled bad-smell examples, which are rare. Early customers may not have enough to train a reliable profile.
- CommercialCustomers may want hardware and software from one vendor. A software-only product depends on compatible sensor units and partners.
- RegulatoryIf alerts are used for food safety records, plants may need validation and documentation, and a missed alert could carry liability.
- AIClassifier confidence can look more certain than it is. Wrong labels from inspectors may degrade models without being noticed.
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.