Opportunities › Agent-driven API integrations without pre-built tools › Proposed app
Plainspec
Let your in-app agent call customer APIs from the spec, with approvals and logs
- Score
- 30
- confidence 49
- Evidence
- 5
- items on the page
- Rivals
- 2
- named in sources
- First version
- 40 d
- estimate
- Type
- Desktop and mobile
- PWA: on computers and phones alike
- Market since
- 2024
- New market · AI estimate
ScreensConcept
Desktop and mobile: Developers configure specs and policies at a desk, but customers and support teams approve task previews and review audit logs on phones during their workday. Sample screens on a computer and a phone, in the proposed brand. Their numbers are sample data.
New request
Ready to plan- Connection
About this appAI proposal
Plainspec lets the agent in your product connect to a customer's external system without you writing a tool for each case. The customer adds an API spec and describes the task in plain words. The agent reads the spec, plans the calls and runs them.
It is built for founders and small teams shipping chat or agent products whose customers want to link systems like a CRM. Each customer has different record types, required fields and mappings. Plainspec keeps that logic in the customer's own request and connection, not in your code.
Because the agent acts on its own plan, Plainspec adds guardrails: a dry-run preview, approval for writes, per-connection limits and a log of every call. You trade some autonomy for integrations that would otherwise never get built.
- Tagline
- Describe it. The agent reads the spec and does it.
- Voice
- PlainCarefulDirect
- Why this name
- Plain words plus spec: the agent works from the API spec, and customers state what they want in plain language.
In the first version
Members say which features they would use. It tells PWAS what people want; it changes no score.
Spec-driven planning
Load an OpenAPI document. The agent reads it at runtime and plans the calls a request needs, so you write no per-endpoint tool.
No member yet
Multi-step workflows
Create one object, then dependent ones, with conditional fields and record templates handled inside a single plan.
No member yet
Dry-run preview
See every call the agent intends to make, with payloads, before anything is sent to the customer's system.
No member yet
Approval rules for writes
Require a human OK for creates, updates or deletes per connection. Reads can run freely.
No member yet
Brand identityAI proposal
Check the name against trademark registers before using it.
Palette
#0F4C4ADeep Teal · primary#F6F3ECPaper · surface#1B2422Ink · text#E8A317Signal Amber · accent#B4532AClay · warning and data
Typography
Aa
Plainspec
ABCDEFGHIJKLMNOPQRSTUVWXYZ 0123456789
Describe it. The agent reads the spec and does it.
PricingAI proposal
The material gives no competitor prices. These tiers follow the monetization idea of volume and connections and are a starting point to test.
Free
Free
- 1 connection
- 100 agent calls a month
- Dry-run preview
- 7-day logs
Team
$49 a month
- 10 connections
- 10,000 agent calls a month
- Approval rules
- 90-day audit trail
Scale
$199 a month
- Unlimited connections
- 100,000 agent calls a month
- Per-customer limits
- 1-year audit trail
- Priority 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
$190,000
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
- Visible inside customers’ products. Plainspec runs inside the builder's chat or agent product. End customers see a 'Connected with Plainspec' approval and log view when they link a system, which names the product to other teams.
- Do they become customers?
- End customers are not buyers; the builders are. Test whether builders who see it in another product ask to use it, and whether a visible badge leads to signups.
- First customers
- Seed-stage founders of customer-facing AI chat or agent products whose customers ask to connect a CRM like Salesforce, in teams of 2 to 10 engineers.
- Where to reach them
- Hacker News: Follow up on threads about agent integrations and share a demo with real approval and audit flows.
- Founder communities: Offer free setup in AI builder Slack and Discord groups for the first connected customer system.
- First milestone
- Five teams each with at least one live customer connection, and two paying for the Team tier.
Why people already pay for this
- VerifiedHacker News post received 15 points and 10 comments
- Public claimTeams building agents face a problem where users can describe what they want in natural language but translating that into hard-coded integration tools does not scale because the logic is different for every user
- Public claimFounders building customer-facing applications are actively seeking solutions to let customers create integrations with third-party systems like Salesforce without developers having to build custom tools
- Public claimThe agent-driven approach successfully handled complex, user-specific integration workflows including multi-step object creation with conditional field logic and record type templates
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 · AI model accessLLM API with tool callingThe agent must read specs and plan multi-step API calls at runtime.
- This app · Spec parsingOpenAPI parser and validatorPlans must be checked against the spec before any call is sent.
RisksAI inference
- AIThe agent may plan a wrong or harmful write, such as a duplicate or bad record, in a customer's live system. Approval rules and dry runs reduce this but add friction.
- CommercialOpen-source tools like Superglue already offer an agentic integration approach. A paid hosted product must prove its value in guardrails and audit.
- DataHolding customers' credentials and business records makes this a security target; one breach would end trust.
- TechnicalPoorly written or missing API specs, odd auth flows and rate limits will cause failures that the agent cannot always fix.
- PlatformModel providers or agent platforms could add runtime spec reasoning as a built-in feature.
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.