AI assistants and search decision
Lindy
A single developer can build a narrow assistant (email drafting + basic scheduling + meeting notes) using existing LLM APIs and integrators in a few months, but replicating Lindy’s enterprise compliance, broad integrations, iMessage/SMS polish, and production reliability is expensive—so keep paying for the full product unless you only need a constrained workflow.
Visit website↗Open-source builds that already do this
Every project below is open source and already does this job today. Fork one, self-host it, or take the parts you need — the build prompt further down assumes an empty file, and this is the shortcut past that. Licences differ; check the one on each card before you ship.
What a replacement has to do
- Connect user's email and calendar, read incoming messages, draft reply suggestions, schedule/reschedule meetings, and produce meeting summaries.
What it still won’t have
- Enterprise compliance and signed BAAs out of the box
- Built-in SOC2/HIPAA attestations and audit logs
- Large catalog of plug-and-play integrations and maintained connectors
- Polished iMessage/SMS routing and high-availability infra
- Ongoing model fine-tuning and production monitoring
What remains hard
- Compliance and regulation
SOC 2 Type II compliant
- Compliance and regulation
HIPAA Compliant and signed BAA
- Compliance and regulation
Lindy is built privacy-first. That means you control your data and encryption comes standard. Approvals are built in, and your data is never sold or used to train models.
First-year cost
No published price
Lindy does not publish a price we could read, so there is nothing to compare against. What building costs is below.
Money you would actually spend
Time you would spend
—
What you would spend
What we assumed
The verdict above measures whether you could build it. This one is only about money.
Runnable build prompt
Build a minimal AI executive assistant web service in Node.js (Express) + Postgres + React that connects to Gmail and Google Calendar via OAuth, polls new messages and calendar events, classifies/prioritizes inbound email, generates reply drafts using an LLM API (e.g., OpenAI), creates calendar events for scheduled meetings, and produces meeting summaries from uploaded transcripts. In scope: OAuth flows, secure token storage (encrypted), email parsing and labeling, draft generation API endpoint, scheduling flow (find free slots + create event), a small React UI to view drafts and approve/send, basic SMS/iMessage webhook simulator. Out of scope: native iMessage integration, HIPAA certification, multi-tenant enterprise features, PBX/phone call recording. Include error handling, retries for transient API errors, logging, automated tests for core endpoints, and Dockerfiles for local deployment.
How we checked
How the score was reached
- Partly verdict base52
- An open-source build was found+5
- 5 cited sources+3
- 3/3 assessment runs agreed+4
- Hard moats found in the evidence-3
- Evidence score61
The base comes from the verdict. Everything under it is a check that either happened or did not, and each one is a fact frozen in this record rather than a judgement made at render time — so the same evidence always produces the same number.
How scoring works →Cited sources · 5
Every page the run actually retrieved.
- official productLindy – The Ultimate AI Executive Assistant
- official pricingPricing - Lindy
- official docsLindy Documentation
- open sourceleon-ai/leon
- open sourceqwersyk/Newelle
Integrity checks
What held up, and what did not.
