Opportunities › Memory and search for AI agents over datasets › Proposed app
Lantern Index
Turn JSONL, CSV and SQLite files into ranked search your agents can query
- Score
- 52
- confidence 42
- Evidence
- 2
- items on the page
- Rivals
- 0
- named in sources
- First version
- 35 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: Developers upload files, configure searchable fields, test queries in a playground, and review ranked results at a workstation during setup and maintenance. Sample screens on a computer, in the proposed brand. Their numbers are sample data.
New dataset
Ready to index48,210
About this appAI proposal
Lantern Index turns raw data files into a ranked full-text index that AI agents can query. Add JSONL, JSON, CSV or a SQLite database, pick the fields that matter, and get a hosted search endpoint plus a tool definition your agent can call.
It is for developers and small teams building agents and LLM apps over large datasets who do not want to run a search cluster or write a new indexing layer for every file format.
Unlike a general-purpose search engine, results come back shaped for agents: the matching record, its source and a short snippet, so context stays small and traceable. Setup takes minutes, not a project.
- Tagline
- Give your agents a memory they can search.
- Voice
- PlainPreciseCalm
- Why this name
- A lantern lights up one spot in a dark room, as an index lights the right record in a large dataset. Two plain words, easy to say.
In the first version
Members say which features they would use. It tells PWAS what people want; it changes no score.
Many formats, one index
Import JSONL, JSON, CSV and SQLite tables. Fields are detected and mapped for you; adjust which ones are searchable.
No member yet
Ranked full-text search
Results are ordered by relevance, with field weights you set per dataset, so the best record comes first.
No member yet
Agent-ready tool endpoint
Each dataset gets a search endpoint and a ready tool definition to paste into your agent framework.
No member yet
Compact, cited results
Each hit returns record ID, source file, matched snippet and score, so agents use less context and can cite.
No member yet
Brand identityAI proposal
Check the name against trademark registers before using it.
Palette
#14213DDeep Ink · primary#FBF7EFPaper · surface#1F2430Charcoal · text#F2A33ALamp Amber · accent#1B7F79Teal Signal · data
Typography
Aa
Lantern Index
ABCDEFGHIJKLMNOPQRSTUVWXYZ 0123456789
Give your agents a memory they can search.
PricingAI proposal
The material gives no competitor prices. These tiers are a starting point to test, based on per-dataset and per-query pricing suggested in the opportunity.
Free
Free
- 1 dataset
- Up to 50,000 records
- 1,000 searches a month
Builder
$29 a month
- 10 datasets
- Up to 2 million records
- 100,000 searches a month
- Scoped API keys
Team
$99 a month
- Unlimited datasets
- Up to 20 million records
- 1 million searches a month
- Usage logs per agent
- Team members
Would you pay Builder · $29 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
- Partners and integrations. Ship ready-made tool definitions and example projects for popular agent frameworks, so developers find Lantern Index inside the tools they already use.
- Do they become customers?
- Developers who try a tool in their framework can sign up and pay themselves. Test whether free-tier users convert once a dataset or query limit is reached.
- First customers
- Solo developers and teams of 2 to 10 building LLM agents over their own JSONL, CSV or SQLite data, such as support logs or research notes.
- Where to reach them
- GitHub and open-source communities: Post worked examples and answer questions in threads about agent memory and dataset search.
- Developer forums: Share a short tutorial: from a CSV file to a searchable agent tool in 10 minutes.
- First milestone
- Ten paying Builder customers who each keep an agent querying a dataset for 30 days.
Why people already pay for this
- VerifiedGitHub repository gained 744 stars in 7 days
- Public claimThe tool is designed to provide deep memory for agents over large datasets by creating ranked full-text indexes from multiple data formats.
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 · Search engineManaged full-text search with ranking (BM25-style)Ranked search over millions of records needs a purpose-built index, beyond what Postgres alone handles well.
- This app · File storageS3-compatible object storageUploaded datasets and index snapshots can be large and need cheap storage.
RisksAI inference
- CommercialDevelopers can run open-source search locally for free. Demand shown so far is GitHub interest only; willingness to pay for a hosted version is untested.
- TechnicalIndex size, memory and cost can grow fast with large datasets, which could squeeze margins on low-priced tiers.
- DataCustomers may upload sensitive records. Hosting them requires clear isolation, deletion and security practices.
- PlatformAgent frameworks and big vendors may add built-in retrieval, reducing the need for a separate service.
- TechnicalKeyword ranking alone may miss meaning-based matches that some agents need, so users may ask for vector search.
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.