Opportunities › Analytics for AI Agent products › Proposed app
Tidewatch
See where your AI agent fails users, with counts you can reproduce
- Score
- 48
- confidence 50
- Evidence
- 8
- items on the page
- Rivals
- 4
- named in sources
- First version
- 40 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: Engineering teams analyze agent failures through dashboards, trend views, and drill-down reports—dense table work requiring sustained focus. Sample screens on a computer, in the proposed brand. Their numbers are sample data.
Overview
Last 7 days71%
About this appAI proposal
Tidewatch reads your agent's production conversations and sorts them into what users wanted, where they had to correct the agent, and whether the request was resolved. You see failures as trends before customers write in.
Counts come from fixed, rule-based processing, not from asking a language model to do the maths. Run it twice on the same data and you get the same numbers. Engineers can open any single conversation; product people read the trends without touching traces.
Built for small teams shipping conversational agents who today spot-check logs, paste them into a chat assistant, or push prompt changes and hope. Tidewatch shows whether a change helped or hurt.
- Tagline
- Know when your agent fails before your users tell you.
- Voice
- PlainPreciseCalm
- Why this name
- A tide watch tracks a steady pattern over time. Tidewatch watches the pattern of your agent's conversations and flags the change early.
In the first version
Members say which features they would use. It tells PWAS what people want; it changes no score.
Intent grouping
Conversations are grouped by what the user was trying to do, so you see which requests are growing and which are failing.
No member yet
Correction detection
Flags moments where users rephrase, repeat or correct the agent, a failure signal that needs no complaint.
No member yet
Resolution tracking
Marks each conversation as resolved, abandoned or handed off, with the rate per intent.
No member yet
Reproducible statistics
Deterministic processing gives identical numbers on every run, so reports can be trusted and compared.
No member yet
Brand identityAI proposal
Check the name against trademark registers before using it.
Palette
#0F3B46Deep Tide · primary#F2F7F5Sea Foam · surface#12262BInk · text#E8742ABuoy Orange · accent#3E9B7AKelp · data positive
Typography
Aa
Tidewatch
ABCDEFGHIJKLMNOPQRSTUVWXYZ 0123456789
Know when your agent fails before your users tell you.
PricingAI proposal
The material states a free tier of 2,000 events a month and paid plans from $80 a month with a 30-day trial (a public claim, not verified). Higher tiers are a starting point to test.
Free
Free
- 2,000 events a month
- Email signup
- Intent and outcome views
Team
$80 a month
- Higher event volume
- 30-day free trial
- Release comparison
- Trace drill-down
Scale
Contact us
- Custom event volume
- Priority support
Would you pay Team · $80 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. Product managers share trend pages and conversation reports by link with teammates and investors who don't use the tool, each carrying a small Tidewatch mark.
- Do they become customers?
- Viewers are often teammates or founders who could become account holders, but a one-time viewer may not. Test how many shared-link viewers sign up for the free tier.
- First customers
- Seed-stage startups of 3 to 15 people whose main product is a customer-facing conversational agent, bought by the founder or lead engineer.
- Where to reach them
- Hacker News and Show HN: Post a write-up on why counts from language models are unreliable, with a free-tier link.
- Founder communities: Offer hands-on onboarding in startup founder groups and agent-building forums.
- First milestone
- Ten teams send live agent events, and five convert to a paid plan after the trial.
Why people already pay for this
- VerifiedHacker News post received 59 points and 22 comments
- AI inferenceAgent teams lack production visibility and discover failures only through customer complaints
- Public claim90% of YC founders surveyed said they only know agents are failing by hearing customer complaints
- AI inferenceTeams use trial-and-error prompt changes without visibility into impact
- AI inference
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 · Event ingestionHTTP ingest endpoint with a queueAgents send conversation events at volume; they must be accepted fast and processed in the background.
- This app · Analytics storeColumnar analytics databaseFast counts by intent and outcome across millions of events, beyond what plain Postgres handles comfortably.
RisksAI inference
- TechnicalRule-based detection of intent and corrections may be less accurate on varied domains and languages than users expect, and needs tuning per customer.
- CommercialObservability and eval vendors could add conversation analytics, and teams may keep using chat assistants for ad hoc analysis.
- DataConversations contain personal data; customers will need redaction, retention controls and clear data handling before sending logs.
- CommercialEarly-stage teams have small budgets; the $80 price point and conversion from the free tier are unproven.
- PlatformAgent frameworks change often; keeping ingestion compatible with several log formats adds ongoing work.
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.