Tier 1
Deep research that reads the web so you don't have to
Orbit's AI research tool runs 8–14 rounds of search and extraction across 65+ sources, merges findings into a living report draft, and produces a comprehensive deliverable in Markdown and HTML — with every source linked for verification.
AI Research Tool
Deep research on Orbit is iterative, not one-shot. The engine searches, extracts, delta-merges new findings into the report every round, and tracks coverage of your sub-questions so nothing is left unanswered. The final report ships with a sources appendix and is exportable to DOCX, XLSX, or PDF.
In plain English
Orbit's research mode is a tireless analyst: it breaks your question into parts, reads 65+ sources across several rounds, and hands you a cited report instead of a pile of links.
How it works in Orbit
Deep research on Orbit is a multi-round investigative pipeline, not a single search call. It plans, searches, extracts, merges, and verifies until your question is genuinely answered with sources you can check.
Planning the investigation
The engine breaks your question into sub-questions and decides how many rounds of searching each one needs. Planning up front is what prevents shallow answers: each sub-question gets targeted queries rather than one broad search that misses half the topic.
Searching across providers
Orbit fuses search pipelines such as Tavily and Perplexity, running up to 160 results per round in deep mode. Multiple providers reduce bias and give the extraction phase more material than any single index would.
Extract, merge, and track coverage
Each round, the engine extracts claims from fresh sources and delta-merges them into a living report draft. It also tracks which sub-questions have enough evidence, so a brief cannot quietly end with gaps.
Verifiable, exportable output
Every finding records its source, and the final deliverable carries a sources appendix with favicons and titles. You can export to Markdown, HTML, DOCX, XLSX, or PDF and hand it to a stakeholder knowing it is checkable.
Pose the brief
Describe the research question and any angle — 'pricing strategy for a B2B SaaS in Europe.'
Watch the plan form
Orbit splits the brief into sub-questions and starts searching across providers.
Follow the rounds
Findings merge into the report draft each round; coverage is tracked per sub-question.
Verify sources
Open any claim to its source from the appendix, or ask follow-ups against the same corpus.
Export the deliverable
Download as Markdown, HTML, DOCX, XLSX, or PDF with the sources appendix attached.
The challenge
Try it yourself
Run this one on a competitor first — then on Orbit. Same prompt, and keep score.
Build
Try this one elsewhere first.
Give the same hard question to a search engine and to Orbit's deep research. Compare the sources, the citations, and which answer you could actually forward to your boss.
Research
Ask a research tool to go deep on one topic.
Then ask Orbit. Compare how many rounds it runs, how many sources it actually reads — we target 65+ — and whether every claim in the final report has a link attached. Bring a bigger coffee mug.
Chat
Ask a plain chatbot the same question.
One just talks. Orbit's agent actually searches the web, runs code, reads your files, and comes back with something done. Compare how many tabs you opened and how many are still open when you're finished.
Image & video
Generate the same image, then edit it.
Ask two tools for the same hero shot, then ask both to recolor it, fill in the background, and animate it. Compare who hands you a first draft and who keeps refining in the same conversation.
150+
models routed per task
1h
cloud sandbox sessions
25
tool calls per chat turn
8–14
deep research rounds
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Supabase AI Builder
Orbit generates fullstack apps with Supabase built in: schema, row-level security, auth, storage, and edge functions provisioned automatically, with the client configured via VITE_SUPABASE_URL and VITE_SUPABASE_SCHEMA.
Prisma AI Builder
Orbit turns Prisma-style schema requests into Supabase SQL — the data layer of your app generated as idempotent migrations with RLS policies, instead of an ORM you maintain.
Postgres AI Builder
Orbit generates fullstack apps on Postgres via Supabase — the relational database your spec describes, with RLS, realtime, and the ability to run SQL directly from the panel.
MongoDB AI Builder
Orbit maps MongoDB-style document requests to Postgres — flexible JSONB columns give document-like flexibility with relational integrity and RLS on top.
Python AI Builder
Orbit executes Python directly in sandboxes for data analysis, automation, and scripts — and standardizes Python web-app requests to the React stack, with Supabase as the data layer.
Build a Dashboard with AI
Orbit generates dashboards with charts, KPI cards, filters, and data tables — wired to Supabase views or seeded sample data — in a single prompt.
Frequently asked questions
How many sources does Orbit's deep research cover?
Deep mode targets 65+ sources across 8–14 rounds, with a hard cap of 160 search results per round when using the fused Tavily + Perplexity pipeline.
Can I verify what Orbit researched?
Yes. Every finding tracks its source, the report lists sources with favicons and titles, and the final report exports a full sources appendix.
What formats can I export research in?
Markdown, HTML, DOCX, XLSX, and PDF. The export pipeline uses canonical tools and validates source files before conversion.
How is deep research different from a normal web search?
A search returns links. Deep research iterates: it searches, extracts content, merges new findings into a report draft every round, and tracks coverage of your sub-questions until nothing is left unanswered.
Can Orbit research topics I care about over time?
Yes. Research runs inside a project, so findings, sources, and report drafts persist. You can resume a brief, ask follow-ups against the same sources, and update the report as new information arrives.