Opportunities › Healthcare data standardization and analytics infrastructure › Proposed app
Mapkeep
Track code mappings, data quality and lineage for healthcare warehouses
- Score
- 25
- confidence 41
- Evidence
- 3
- items on the page
- Rivals
- 1
- named in sources
- First version
- 35 d
- estimate
- Type
- Desktop and mobile
- PWA: on computers and phones alike
- Market since
- 2022
- Emerging market · AI estimate
ScreensConcept
Desktop and mobile: Data engineers review mappings and lineage in detailed dashboards at a desk, while reviewers check alerts and approve changes on mobile. Sample screens on a computer and a phone, in the proposed brand. Their numbers are sample data.
Review queue
Claims mart: 3 urgent42 open
About this appAI proposal
Mapkeep is a governance workspace for teams that model healthcare data in a cloud warehouse with dbt. It reads your project's metadata, not patient records, and shows where code mappings, quality tests and lineage stand across every data mart.
Analytics engineers see which source codes are unmapped and which tests fail. Clinical and compliance reviewers approve mappings and sign off on definitions in a phone-friendly app, with a record of who agreed to what and when.
It does not replace your models or your warehouse. Open-source model sets already cover the models themselves. Mapkeep covers the ongoing work around them: review, ownership, change history and reports you can hand to an auditor.
- Tagline
- Every code mapped, every check recorded, every decision signed.
- Voice
- PlainCarefulPrecise
- Why this name
- Map is the core act of healthcare data work, and keep means holding the record over time. Two plain words, easy to say and spell.
In the first version
Members say which features they would use. It tells PWAS what people want; it changes no score.
Unmapped code queue
Lists source codes with no standard match, ranked by how many rows they touch, so reviewers fix the mappings that matter most first.
No member yet
Mapping review and sign-off
Propose, approve or reject a mapping. Each decision keeps the reviewer, the date and the reason.
No member yet
Quality test board
Imports dbt test results and groups failures by data mart, source and owner, with trend lines across runs.
No member yet
Column-level lineage view
Follow a metric back through models to the source feed. See what a changed column would affect before you merge.
No member yet
Brand identityAI proposal
Check the name against trademark registers before using it.
Palette
#1F6F6BDeep Teal · primary#F7F5EFPaper · surface#1F2A2EInk · text#C77D00Amber · accent#3B5B92Slate Blue · data#B33A3ABrick · alert
Typography
Aa
Mapkeep
ABCDEFGHIJKLMNOPQRSTUVWXYZ 0123456789
PricingAI proposal
The material gives no competitor prices. The one named competitor, Tuva Core, is open source, so a free tier is needed. The Team price is a starting point to test, not a market figure.
Starter
Free
- 1 warehouse project
- Quality board and lineage view
- 2 reviewer seats
- 30 days of history
Team
$299 a month
- 3 warehouse projects
- Unlimited reviewers
- Mapping sign-off and audit trail
- Terminology version tracking
- Mobile review alerts
Organization
Contact us
- Unlimited projects
- Single sign-on
- Audit pack exports
- Custom retention
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. Teams share read-only audit packs and mapping status links with compliance officers, clinical leads and external auditors. Each page shows the product name and links back to the project's source workspace.
- Do they become customers?
- Viewers are mostly reviewers and auditors who may lack buying power, though some own governance budgets. Test whether any shared-link viewer starts a workspace or asks for a seat.
- First customers
- Analytics engineering leads at mid-size health plans and provider groups that already run dbt on Snowflake or BigQuery and answer to a compliance or clinical data owner.
- Where to reach them
- dbt community Slack: Share a free sample report built from a public healthcare dbt project and invite teams to try it on theirs.
- Healthcare data meetups: Run short demos on mapping review and sign-off, then offer setup help to three teams.
- LinkedIn outreach: Message analytics leads at health plans about audit-ready terminology records and offer a free pilot.
- First milestone
- Five teams connect a real dbt project, and at least two pay for Team within 60 days of a pilot.
Why people already pay for this
- VerifiedGitHub repository gained 328 stars in 1800 days
- Public claimHealthcare organizations need standardized data models and quality governance for analytics across cloud data warehouses
- AI inferenceThe project is actively maintained and has community engagement
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 · Warehouse metadata ingestdbt artifact parser and information-schema readersReads manifest and run results plus table metadata from BigQuery, Snowflake and Redshift without touching row data.
- This app · Background jobsScheduled worker with a job queuePulls artifacts on a schedule and recomputes lineage and test trends outside web requests.
RisksAI inference
- CommercialOpen-source model sets are free and have a community. Teams may see governance as a spreadsheet or wiki task and not pay for a separate tool. Demand for the review workflow is untested.
- RegulatoryBuyers will ask about HIPAA, business associate terms and security reviews. Even with metadata only, column names or sample values could expose sensitive details, so the product needs strict ingest limits.
- TechnicalWarehouse and dbt projects vary widely. Column-level lineage from artifacts is often incomplete, and each warehouse needs its own connector and testing.
- PlatformDepends on dbt artifact formats and warehouse metadata APIs, which can change. A move by the dbt ecosystem into governance would overlap this product.
- AIIf mapping suggestions are added, wrong matches in clinical code sets carry real harm. Suggestions must stay clearly labeled and need human approval.
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.