Opportunities › Semantic layer for non-technical business users › Proposed app
Plainterm
Ask company data questions in business words and get answers your team can trust
- Score
- 46
- confidence 61
- Evidence
- 6
- 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
- 2012
- Mature market · AI estimate
ScreensConcept
Desktop app: Business users and analysts work with dashboards, explore complex data and run reports at a desk for extended periods. Sample screens on a computer, in the proposed brand. Their numbers are sample data.
Ask
Uses defined metrics only$1.84M
About this appAI proposal
Plainterm is a semantic layer made for people who do not write SQL. Analytics teams define metrics and dimensions once, with strict naming rules and clear meanings. Colleagues then explore the data in business words, through dashboards, exports or a chat box, without a separate BI tool.
It blends data from several sources and sends each query to the fastest compute tier that can answer it. Because every term has one plain definition, answers stay consistent, and LLM questions map to defined metrics instead of guessed columns.
Built for analytics teams tired of ticket queues and for the business users who wait in them. It works on its own, and an API serves the tools you already have.
- Tagline
- Your data, in your words.
- Voice
- ClearPlainCalm
- Why this name
- Plain and term: every metric gets a plain term that business users can say out loud and trust.
In the first version
Members say which features they would use. It tells PWAS what people want; it changes no score.
Strict naming and clear meanings
Every metric and dimension has one name, one definition and one owner. Checks block duplicates and vague labels before colleagues see them.
No member yet
Ask in plain language
Type a question and get a result built only from defined metrics. The query is shown in business terms so people can check it.
No member yet
Automatic data blending
Join sources by declared business keys. Users pick fields; Plainterm works out the joins and warns when a blend could double count.
No member yet
Compute tier routing
Each query goes to the fastest tier that can answer it, such as a warehouse or a fast analytics store. Admins see where each query ran.
No member yet
Brand identityAI proposal
Check the name against trademark registers before using it.
Palette
#0F4C5CDeep Teal · primary#F7F4ECPaper · surface#1B2528Ink · text#E9A23BMarigold · accent#7FA99BSage · data
Typography
Aa
Plainterm
ABCDEFGHIJKLMNOPQRSTUVWXYZ 0123456789
Your data, in your words.
PricingAI proposal
Based on Cube's verified pricing page: a free plan, two paid plans from $40 to $80 a month, and prices on request for larger customers. The 25-user free tier comes from a public claim. Limits are a starting point to test.
Free
Free
- Up to 25 users
- Core semantic layer
- Dashboards and CSV export
Team
$40 a month
- More users and data volume
- Plain-language questions
- API access
- Compute tier routing
Business
$80 a month
- Higher limits
- Multiple sources and blending
- Review workflow for definitions
- Usage and routing 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
- Shared outputs. Users share dashboards, exports and answer links with colleagues. Viewers see the metric definitions behind each number and can ask their own questions.
- Do they become customers?
- Viewers inside one company can become users, but the free tier of 25 users may absorb them. Test whether shared answers lead to a paid team upgrade or to a second team signing up.
- First customers
- Analytics leads at mid-sized companies with a warehouse and a backlog of data requests from sales, finance or operations teams.
- Where to reach them
- Hacker News and data forums: Share a demo of one governed metric answering plain-language questions, and ask analytics leads for pilots.
- Analytics community posts: Write about naming rules and self-service lessons, with a free tier to try.
- Direct outreach: Contact analytics leads who describe ticket queues for data requests and offer a two-week pilot.
- First milestone
- Five analytics teams each put 10 or more business users on it, and two move to a paid plan within 60 days.
Why people already pay for this
- VerifiedHacker News post received 25 points and 16 comments
- Public claimThe creator spent 4 years solving self-service analytics for non-technical business users, identifying a key challenge in balancing expressiveness with ease of use.
- Public claimThe semantic layer design approach makes it work well with LLMs in addition to serving non-technical business users.
- Public claimExisting BI tools that connect to semantic layers represent a dead-end approach, motivating a different product architecture.
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 · LLM providerHosted LLM API with structured outputTurns questions into queries over defined metrics only, not free SQL.
- This app · Query enginesConnectors to a warehouse and a fast analytics storeRouting needs more than one compute tier to send queries to.
RisksAI inference
- TechnicalCorrect automatic joins and tier routing across different engines are hard; a wrong blend gives wrong numbers and loses trust fast.
- AINatural-language questions can be mapped to the wrong metric. Showing the query in business terms and limiting answers to defined metrics reduces but does not remove this.
- CommercialCube and Looker are known options, and the material says the author's approach differs. Buyers may not want to replace a BI tool they already have.
- DataCustomers must give read access to sensitive company data. Security review and per-user permissions could slow the first sales.
- CommercialA free tier of 25 users may cover most small teams, so the paid upgrade reason must be tested.
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.