Opportunities › Guardrails and safety constraints for AI agents › Proposed app
Tagline Gate
Rules about your data, enforced on every agent tool call, with no LLM judge
- Score
- 56
- confidence 49
- Evidence
- 6
- items on the page
- Rivals
- 5
- named in sources
- First version
- 60 d
- estimate
- Type
- Desktop and mobile
- PWA: on computers and phones alike
- Market since
- 2016
- Established market · AI estimate
ScreensConcept
Desktop and mobile: Teams set up data policies and audit rules at a desk, but approve blocked agent calls from their phone on the go, with real work in both places. Sample screens on a computer and a phone, in the proposed brand. Their numbers are sample data.
Live decisions
Agent: support-bot412 calls
About this appAI proposal
Tagline Gate sits inside an AI agent's loop and checks every tool call against rules written about the data, not about each use case. You label data sources and fields once (customer email, salary, internal doc). The gate then follows those labels as the agent reads, reasons and writes, and allows or blocks each action by fixed rules.
When an action is blocked, the agent gets a remedy plan: a safe alternative such as masking a field, sending a summary instead of the raw record, or asking a person to approve. The agent keeps working instead of stopping. No LLM decides whether something is safe, so a prompt injection cannot talk its way past the gate.
It is for enterprise teams who connect agents to several tools and sensitive systems and find that LLM judges leak, while hand-written IF-ELSE policies make the agent far less useful.
- Tagline
- Rules about your data. Enforced on every agent call.
- Voice
- PreciseCalmDirect
- Why this name
- A tag marks the data, a gate decides what passes. Two plain words that say what the product does.
In the first version
Members say which features they would use. It tells PWAS what people want; it changes no score.
Data-labelled policies
Write rules once per data type, such as 'payroll fields never leave the company domain', and reuse them across every agent and tool.
No member yet
Deterministic decisions
Allow, block or modify is decided by fixed rules. No model call in the decision path, so prompt injection has nothing to persuade.
No member yet
Remedy plans
A blocked call returns safe next steps to the agent: mask, summarise, narrow the query or request approval. The task can still finish.
No member yet
Label tracking across tools
Labels follow data from one tool's output into the next tool's input, so a leak through a chain of calls is caught.
No member yet
Brand identityAI proposal
Check the name against trademark registers before using it.
Palette
#14232BDeep Slate · primary#F4F1EAPaper · surface#1B1F23Ink · text#E0902BSignal Amber · accent#2F7D5BPine · data: allowed#B3422FBrick · data: blocked
Typography
Aa
Tagline Gate
PricingAI proposal
The material gives no competitor prices. These tiers are a starting point to test, priced by agents and decisions as the opportunity's monetization note suggests.
Developer
Free
- 1 agent
- 10,000 decisions a month
- Basic audit log
- Community support
Team
$299 a month
- 10 agents
- 1 million decisions a month
- Remedy plans and approval inbox
- Utility report
- Email support
Enterprise
Contact us
- Unlimited agents
- Self-hosted engine option
- SSO and audit export
- Security review support
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
- Partners and integrations. Ship open hooks for common agent frameworks and list in their plugin directories and tool catalogs. Teams find the gate where they already wire up tools and policies.
- Do they become customers?
- Developers who install the hooks can become paying Team customers when they hit decision limits. Test whether free-tier users move to paid once an agent reaches production.
- First customers
- Security and platform engineers at mid-size software or fintech firms with agents connected to CRM, email and internal documents in pilot or early production.
- Where to reach them
- Hacker News and dev forums: Publish a replayable benchmark showing leak rate and task success against LLM judges and hand-written policies.
- AI security communities: Offer free policy reviews of real agent setups and write up the findings.
- Framework plugin directories: List the hooks in agent framework catalogs so builders find them while wiring tools.
- First milestone
- Five teams run the gate on a real agent for 30 days, and two convert to paid.
Why people already pay for this
- VerifiedHacker News post received 25 points and 12 comments
- Public claimNon-deterministic guardrails (LLM-based judges and auto modes) suffer from high data leak rates on benchmarks.
- Public claimExisting deterministic guardrail solutions require extensive case-specific policies and significantly degrade agent utility.
- Public claimEnterprises building AI agents encounter the problem of agents potentially running out of control and leaking sensitive data when connected to multiple tools.
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 · Policy engineDeterministic rule evaluator with label propagationCore of the product. Must run fast and give the same answer for the same input, with no model in the path.
- This app · Agent SDKTypeScript and Python client packagesTeams add pre-call and post-call hooks to their existing agent loops.
RisksAI inference
- TechnicalTracking data labels through free-text model output is hard. Data can be rephrased or summarised, so label propagation may miss leaks or over-block.
- CommercialTeams may already use Cedar or OPA, or build in-house. Switching needs a clear, measured gain in agent utility.
- DataLabelling data sources takes effort. If setup is slow, teams will leave data unlabelled and the gate protects little.
- PlatformAgent frameworks change their hook interfaces often, so each integration needs ongoing maintenance.
- CommercialThe 90% versus 40% utility figures are a public claim from one source, not verified. Our own benchmark must confirm them.
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.