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JAND AI

Features. Architecture. Usage.

Features & How It Works

A detailed, practical overview of JAND AI. Read examples, technical notes, and FAQs that explain how JAND delivers fast, private, and accurate AI assistance.

Core features (expanded)

Free forever

All major functionality is available at no cost. There are no feature walls that block core capabilities. Monetization is limited to lightweight ads to keep the platform sustainable while preserving user access.

Example: use the full prompt enhancer and link enrichment on the same session without a trial or subscription

Privacy-first architecture

Conversations are stored locally in browser storage by default. No user-identifying logs are retained on our servers. When external provider calls are used (for enrichment), only the minimal request data is forwarded and only with explicit user consent.

Example: enabling Links will fetch references; disabling Links keeps queries fully local.

Adaptive Chat Engine

JAND uses an LLM-first pipeline tuned for concise and context-aware replies. The pipeline adapts to conversation context to preserve intent across follow-up prompts and maintain continuity without re-sending the entire history every request.

Example: ask JAND to "summarize the following thread" and it will reuse local context to produce a compact summary.

Prompt Enhancer

Automatic prompt rewriting improves clarity, reduces ambiguity, and produces higher-quality outputs for code generation, content writing, and research tasks. Enhancements are deterministic and reversible.

Example: "fix my SQL" will be rewritten to include schema assumptions and sample input for better debugging output.

Link Enrichment

Optional link enrichment returns curated sources and citations with answers. The feature can be toggled per session. The enrichment layer prioritizes reputable domains and returns short summaries beside each link.

Example: a historical question returns 2–3 links and a one-sentence source attribution under each link.

Local history & export

Chat history is stored locally and can be exported or cleared by the user. Export formats include JSON and plain text to facilitate backups or migration to a self-hosted instance.

Accessible, fast UI

The UI is optimized for low-latency interactions and accessibility. It supports keyboard navigation, screen readers, and responsive layouts for mobile and desktop.

Developer-friendly (self-hostable)

JAND's backend is modular with a FastAPI core and pluggable providers. Developers can self-host the stack for full control over data and provider selection.

Deterministic utilities

Features such as formatting, code refactor, and templates run deterministically to ensure reproducible outputs useful for testing and CI workflows.

How JAND AI works-step-by-step

1-Client input You enter a prompt. The UI optionally runs local enhancements (spellfix, expand intent, add format instructions).
2-Local composition For many tasks the frontend composes an enhanced prompt and retains relevant context locally to reduce server payloads and speed up replies.
3-Optional provider calls If Link Enrichment or external lookups are enabled, the system queries configured providers for short snippets and URLs. These calls are limited and do not include full chat transcripts.
4-Model inference The backend orchestrates model inference using the chosen provider. Responses are post-processed for clarity, filtered for safety, and returned to the client.
5-Rendering & history The client renders the answer and stores the resulting message locally. You can export, copy, or clear this history at any time.

Technical architecture (concise)

Frontend

Vanilla JS + lightweight state. Emphasis on performance and low memory footprint. Local storage holds session history and theme preferences.

Backend

FastAPI-based microservice. Pluggable provider layer abstracts model vendors, search indexes, and link enrichment services. Authentication is optional for self-hosting scenarios.

Security & privacy summary

By default chats stay local. Minimal server interaction occurs only when using optional features that require external data, such as link lookups. JAND does not sell personal data. For full details see our Privacy & Data Policy.

Performance notes

We optimized common flows to reduce latency. Examples include caching of recent provider responses, incremental context encoding, and prompt compaction to save bandwidth and speed inference.

Examples & use cases

Developer workflows

Generate, refactor, and explain code. JAND can produce unit tests, explain errors, and translate code between languages.

Prompt: "Convert this Python function to idiomatic Rust and include unit tests." → Response: rewritten function + tests + explanation of type choices.

Writers & creators

Create outlines, rewrite for tone, produce titles, and summarize long text. Use prompt enhancement to refine style automatically.

Prompt: "Rewrite this paragraph for a social post with a humorous tone." → Response: three variations with notes on tone and hashtags.

Research & learning

Ask for step-by-step explanations, request references with Links enabled, and get compact summaries for study notes.

Prompt: "Explain gradient descent in 5 bullet points and give a short analogy." → Response: concise explanation + analogy + optional links.

FAQ

Is JAND AI really free?

Yes. Core features are free. Lightweight ads fund hosting and development. There are no hidden paywalls for essential functionality.

Where is my data stored?

By default your chat history is stored locally in your browser. You can export or clear it anytime. Self-hosting provides full control over storage location.

How do links work?

When you enable Links, the system sends a short query to configured providers. It returns curated source links and small excerpts. You can turn this off to keep all operations local.

How often is the product updated?

We push updates regularly. Check the blog for release notes and changelogs. The features page will also be revised when major capabilities land.

Roadmap & what's next

Planned items in priority order:

  • Improved offline models: smaller on-device models for ultra-low latency tasks.
  • Workspace templates: shareable templates for common workflows (code review, social posts, lesson plans).
  • Team features (opt-in): secure, encrypted sharing for small teams and classrooms.
  • Plugin marketplace: vetted community plugins for integrations.

Roadmap items may change based on user feedback and usage signals.

Contact & feedback

Report bugs, request features, or discuss partnerships via our social handle.

@tunexnow