Opportunities › AI coding agents with formal requirements specification › Proposed app
Plumbline
Turn vague requirements into precise specs your coding agent can follow.
- Score
- 42
- confidence 54
- Evidence
- 6
- items on the page
- Rivals
- 1
- named in sources
- First version
- 35 d
- estimate
- Type
- Desktop app
- PWA: runs in the browser, installs on Mac and Windows
- Market since
- 2025
- New market · AI estimate
ScreensConcept
Desktop app: Turning vague requirements into formal specs involves pasting tickets, reading gaps, answering clarifying questions, and reviewing diffs—focused writing and review work. Sample screens on a computer, in the proposed brand. Their numbers are sample data.
Requirement scan
Draft: not ready7 gaps
About this appAI proposal
Plumbline turns a rough requirement into a precise specification before your coding agent starts. You paste a ticket, a prompt or a feature brief. It checks the text for gaps, conflicts and undefined terms, then asks you short questions to settle each one.
When the questions are answered, it writes a structured spec with explicit rules, edge cases and acceptance checks. You hand it to the coding agent as a file or through an API. It is meant for engineers and small teams who use coding agents and are tired of re-prompting.
Unlike a prompt editor, it keeps a list of open and resolved gaps and does not call a spec complete while any gap remains open. Whether this reduces iterations is something to test with real teams.
- Tagline
- Close the gaps in your requirements before the agent writes a line.
- Voice
- PreciseCalmDirect
- Why this name
- A plumb line checks that a wall is true before you build on it. Plumbline checks that a requirement is true before an agent builds on it.
In the first version
Members say which features they would use. It tells PWAS what people want; it changes no score.
Gap and ambiguity scan
Finds missing cases, conflicting rules and vague terms in a requirement, and marks each one with where it appears in the text.
No member yet
Clarifying questions
Turns each gap into a short question with suggested answers, so you settle it in seconds instead of rewriting a prompt.
No member yet
Formal spec output
Produces a structured spec with rules, edge cases and acceptance checks in Markdown and JSON that agents can read.
No member yet
Completeness gate
A spec stays marked draft while any gap is open. You can see at a glance whether it is safe to hand to an agent.
No member yet
Brand identityAI proposal
Check the name against trademark registers before using it.
Palette
#12372AInk Green · primary#F7F4ECPaper · surface#1E2421Charcoal · text#E3A008Signal Amber · accent#B5412FGap Red · data: open gaps#8DB08FSage · data: resolved
Typography
Aa
Plumbline
PricingAI proposal
The material gives no competitor prices. These tiers follow its suggested models (projects, specs per month, API calls) and are a starting point to test.
Free
Free
- 1 project
- 5 specs per month
- Markdown export
Team
$29 a month
- 10 projects
- 100 specs per month
- Shared review links
- Spec history and diff
Agent API
$79 a month
- Unlimited projects
- 1,000 specs per month
- Agent handoff API
- Priority checks
Would you pay Team · $29 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
$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. Specs are shared as links or files with teammates, reviewers and agents. Each shared spec page shows a 'Checked with Plumbline' footer and lets the reader see the resolved gaps.
- Do they become customers?
- Readers are often engineers and leads who could run their own checks, so some may become paying users. Test whether people who open a shared spec go on to create a project of their own.
- First customers
- Small software teams of 2 to 10 engineers who already run coding agents on real projects, starting with startups and agencies where one lead writes the tickets.
- Where to reach them
- Hacker News and dev forums: Post a worked example showing one vague ticket turned into a spec, and invite readers to try their own requirement.
- Agent user communities: Offer free spec checks in Discord and forum groups for coding agent users, and ask for feedback on results.
- Direct outreach to agencies: Message agency leads who publish about using agents, and offer a free pilot on one live project.
- First milestone
- Ten teams run three or more specs each, and five of them pay for a plan within eight weeks.
Why people already pay for this
- VerifiedHacker News post received 27 points and 5 comments
- Public claimAI coding agents produce working code in fewer iterations when given precise formal specifications instead of natural language prompts
- Public claimRequirements gaps and ambiguities are a problem that prevents coding agents from working correctly
- Public claimTeams are already using coding agents in their projects and workflows
- Public claim
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 · Language model accessHosted LLM API for gap finding and questionsNeeded to read free-form requirements and write clarifying questions and spec text.
- This app · Constraint checkingSMT solver library run server-sideChecks the structured rules for conflicts and missing cases, which gives the formal part beyond model judgment.
RisksAI inference
- AIGap detection may miss real ambiguities or raise false ones. Users could trust a 'complete' spec that is not, so the checks must be tested against real tickets.
- TechnicalFull formal verification is hard to do on free-form text. The first version may rely on structured rules and model checks rather than proofs, and must not overstate this.
- CommercialThe evidence is a single post with 27 points. Teams may find re-prompting cheap enough that they won't pay for a separate step.
- PlatformCoding agent vendors may add their own planning and clarifying steps, which would cover much of what this does.
- DataRequirements often contain private product details. Teams will want clear rules on storage and on which models see their text.
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.