Opportunities › Deterministic LLM behavior control for regulated industries › Proposed app
Plumbline
Write model policies, verify every answer against facts, keep an audit trail
- Score
- 42
- confidence 58
- Evidence
- 9
- items on the page
- Rivals
- 3
- named in sources
- First version
- 45 d
- estimate
- Type
- Desktop app
- PWA: runs in the browser, installs on Mac and Windows
- Market since
- 2023
- Emerging market · AI estimate
ScreensConcept
Desktop app: Enterprise teams write and test policy rules, review fact graphs, and prepare audit reports; this governance and compliance work demands sustained focus and dense tables. Sample screens on a computer, in the proposed brand. Their numbers are sample data.
Today's traffic
2 need review98.2%
About this appAI proposal
Plumbline is a policy and verification console for teams putting language models into regulated work. You write rules in plain structured form, link them to a graph of approved facts, and every model answer is checked against both before it reaches a user.
It is built for AI engineers and compliance leads in finance, pharma, healthcare and media. Instead of hoping a prompt holds, each answer gets a pass, a block or a fix, with the exact rule and source fact that decided it.
The proposal has two layers. The first, a checking gateway that works with any model, is the first build. The second, activation-level steering for open-weight models you host yourself, is a later and harder step that needs testing against the claims made for it.
- Tagline
- Every answer checked against your rules before anyone reads it.
- Voice
- PreciseCalmPlain
- Why this name
- A plumb line is the simple tool that shows whether a wall is true. The product does the same for model answers: a fixed reference, checked every time.
In the first version
Members say which features they would use. It tells PWAS what people want; it changes no score.
Policy editor with test cases
Define rules such as required disclaimers or banned claims, then run them against saved example conversations before release.
No member yet
Fact graph for grounding
Load approved facts and relations from documents or tables. Answers are checked for relational claims, not just matching text.
No member yet
Check-before-send gateway
A proxy sits between your app and the model. Failing answers are blocked or rewritten before the user sees them.
No member yet
Decision trail per answer
Each verdict stores the rule, the source fact, the model version and the time, ready to export for an auditor.
No member yet
Brand identityAI proposal
Check the name against trademark registers before using it.
Palette
#1F5C5BDeep Teal · primary#F6F3ECPaper · surface#1E2A2EInk · text#D9822BAmber · accent#6F8A35Moss · data, pass state#A8402FBrick · data, block state
Typography
Aa
Plumbline
ABCDEFGHIJKLMNOPQRSTUVWXYZ 0123456789
PricingAI proposal
The material gives no competitor prices. It only says the model is likely usage-based, by tokens or calls. These tiers are a starting point to test, not a market-based figure.
Sandbox
Free
- 1 policy
- 5,000 checked answers
- Test cases
- 7-day log
Team
$900 a month
- 20 policies
- 500,000 checked answers
- Fact graph
- Review queue
- Shareable audit reports
Enterprise
Contact us
- Volume pricing
- SSO and sign-off flows
- Long log retention
- Steering adapter pilot
Would you pay Team · $900 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. Teams send read-only audit report links to compliance reviewers, risk officers and auditors. Each report shows the policy, verdicts and trail, with a small Plumbline mark.
- Do they become customers?
- Reviewers mostly see the report once. Test whether any of them, in other teams or firms, ask to run their own policies. Count reports opened per account and follow-up requests.
- First customers
- AI platform leads at mid-sized financial services firms (200 to 2,000 staff) who already run a customer-facing or analyst-facing LLM pilot and answer to compliance.
- Where to reach them
- Hacker News follow-up: Reply to the source thread and post a demo of blocking a relational hallucination with a visible rule trail.
- LinkedIn outreach: Message AI and compliance leads at fintechs and asset managers, offering a free policy test on their own sample prompts.
- Compliance meetups: Present a short talk on audit trails for model answers at regional fintech and risk events.
- First milestone
- Three paid pilots in financial services, each running a live policy through the gateway for 30 days.
Why people already pay for this
- VerifiedHacker News post received 54 points and 34 comments
- Public claimEnterprises struggle to deploy LLMs in regulated environments due to inability to guarantee compliance
- Public claimFortune 500 companies are paying for deterministic LLM control solutions today
- Public claimFinancial services and media companies need AI model reliability for compliance and accuracy
- 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 · Checking gatewayLow-latency proxy service with rule engineAnswers must be checked in the request path, which needs steady response times beyond serverless limits.
- This app · Fact graph storeGraph database or Postgres graph tablesRelational claims need links between entities, not only text search.
RisksAI inference
- TechnicalClaim extraction is itself done by a model, so the checker can miss or misread claims. The first version cannot honestly promise zero hallucinations, only measured catch rates.
- AIActivation-level steering works only on open-weight models and its reliability at production scale is unproven here. It rests on an AI inference in the evidence and must be tested.
- CommercialRegulated buyers run long security reviews. A small team may struggle to pass them without certifications.
- DataCustomers' prompts and answers may hold sensitive data. Storing logs creates privacy duties and a breach target.
- CommercialEvidence of paying customers is only a public claim from a forum post. Demand for this smaller product is unverified.
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.