Opportunities › AI agents with built-in company context › Proposed app
Larder
An AI teammate that remembers your company and finishes work that takes days
- Score
- 54
- confidence 59
- Evidence
- 14
- items on the page
- Rivals
- 3
- named in sources
- First version
- 45 d
- estimate
- Type
- Desktop and mobile
- PWA: on computers and phones alike
- Market since
- 2024
- New market · AI estimate
ScreensConcept
Desktop and mobile: Teams set up integrations and manage multi-day tasks from desktop, while also checking status and interacting with agents on mobile throughout the day. Sample screens on a computer and a phone, in the proposed brand. Their numbers are sample data.
Sources
No setup needed4 connected
About this appAI proposal
Larder is an AI agent workspace that already knows your company. Connect Slack, docs, email and calendar with one-click sign-in, and it builds a structured wiki for each person and for the company: decisions, preferences, product facts, and how you write.
The agent keeps its memory between sessions. Give it a task that takes several days, such as a launch script or a sales follow-up run, and it carries on where it stopped, asks you at set handoff points, and does not need the background pasted in again.
It is for founders, managers and individual contributors who want agent help without building OAuth apps or feeding context by hand. Every wiki page shows its source and can be edited, so you can see why the agent said what it said.
- Tagline
- The agent that already knows your company.
- Voice
- CalmPlainReliable
- Why this name
- A larder is where a household keeps what it needs stored and ready. Larder keeps company knowledge ready for the agent. Short, plain, easy to say.
In the first version
Members say which features they would use. It tells PWAS what people want; it changes no score.
One-click connections
Link Slack, Google Workspace, Notion and similar tools with a single sign-in. No OAuth apps to build or maintain.
No member yet
Personal and company wikis
Larder sorts what it reads into structured pages: decisions, product facts, customers, voice. One wiki per person, one for the company.
No member yet
Memory that persists
The agent remembers across sessions and days. Tasks resume with their full background instead of starting from zero.
No member yet
Long tasks with handoffs
Set checkpoints where the agent pauses for a person to approve, correct or add missing context, then continues.
No member yet
Brand identityAI proposal
Check the name against trademark registers before using it.
Palette
#2F5D50Pantry Green · primary#FAF6EEFlour · surface#1F2623Charcoal · text#E8975AApricot · accent#A9C3B2Sage · data
Typography
Aa
Larder
ABCDEFGHIJKLMNOPQRSTUVWXYZ 0123456789
The agent that already knows your company.
PricingAI proposal
The material gives no competitor prices. It only says a similar product has a freemium model with a 3-day trial and paid tiers. These prices are a starting point to test.
Trial
Free
- 3-day free trial
- Two connected sources
- Personal wiki
Solo
$20 a month
- Unlimited personal wiki
- Persistent memory
- Multi-day tasks
- Five connected sources
Team
$15 a month
- Price per user
- Company wiki
- Shared handoffs
- Suggested automations
- All connectors
Would you pay Team · $15 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
- Users invite others. A founder or manager sets up the company wiki and invites teammates so each gets a personal wiki and shared tasks. Handoffs assign work to colleagues, who join to take them.
- Do they become customers?
- Invited teammates use the product and can become paying seats. Test whether a company wiki with several users converts from the trial to team plans, and how many invites are accepted.
- First customers
- Startup founders and small teams of 3 to 20 people already trying AI agents who found setup and memory painful.
- Where to reach them
- Hacker News and founder forums: Share a hands-on write-up of the setup pain and show a before and after demo with a free trial link.
- Founder communities: Offer setup help to founders in startup Slack and Discord groups in exchange for feedback.
- Direct outreach: Message founders who post about agent setup problems and offer a guided trial.
- First milestone
- Ten startup teams connect two sources, and five pay for a plan after the 3-day trial.
Why people already pay for this
- VerifiedHacker News post received 59 points and 48 comments
- Public claimExisting AI agent setup requires substantial manual work including OAuth configuration and context feeding
- Public claimThe problem of difficult AI agent setup is widespread among startups
- Public claimMost existing AI agents lack persistent memory and treat it as an afterthought
- 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 · AI modelsHosted large language model APIRuns the agent, wiki writing and long tasks without hosting models.
- This app · ConnectorsManaged OAuth and integration serviceGives one-click sign-in to many tools without building each OAuth app.
RisksAI inference
- TechnicalBuilding accurate wikis from scattered chats and docs is hard. Wrong or stale facts could mislead the agent, so source links and review must work well.
- DataReading email and chat gives access to sensitive company data. Buyers will ask about permissions, retention and who can see which wiki pages.
- CommercialTeams may stay with the AI tools they already use or with MCP setups. Larder must show clearly better continuity to justify switching.
- AILong tasks can drift or take wrong actions without oversight. Handoff checkpoints and approval before any outward action are needed, and add friction.
- PlatformDepends on third-party model providers and tool APIs whose terms, limits and prices can change and affect margins.
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.