Most AI tools search the web the same way a browser does — type something in, get a pile of links back, read through them yourself. Orbis does something different. It knows before it searches what kind of result you're looking for, routes to the right sources, and reads what it finds so you don't have to.
This isn't a general web search with a nicer interface. It's a search layer built specifically for the context a founder works in. The sources it pulls from, the way it routes queries, the way it reads live pages — all of it was assembled with a specific kind of person in mind.
The sources are specific to the startup world.
Orbis doesn't just search the open web. When you ask it to research a company or a person, it routes to indexes that are actually relevant to how founders work — community signal, funding data, professional profiles, product launch history. Not SEO-optimized listicles.
The routing is automatic. Ask Orbis to research a company and it pulls from the company index. Ask it to find a person and it goes to the professional profile index. Ask it what the community thinks of a product and it goes to HN, Reddit, and Product Hunt. You don't configure this — it reads the intent of the query and routes accordingly.
Not every question deserves the same effort.
Looking up who founded a company is different from building a competitive brief before a board meeting. Orbis adjusts how much work it does based on what you're asking. You can also tell it explicitly.
| Mode | What it does | When to use it |
|---|---|---|
|
Quick
|
Single-fact lookup. Runs one targeted query, returns immediately. Under a second. | Who is the CEO. When did they raise their Series A. What does this company do. |
|
Standard
|
Default mode. Balances speed and coverage. Works well for most research tasks without you thinking about it. | Research a competitor. Find an investor. Summarize a market. |
|
Wide
|
Multi-angle search. Breaks the question into related sub-queries, runs them in parallel. Surfaces things a single query would miss. | Comprehensive competitor research. Understanding a market from multiple angles. |
|
Full Brief
|
Full research mode. Maximum coverage, cross-source synthesis, live page reads. Writes a structured document when finished. | Investor-level market analysis. Overnight research before a key meeting. |
For most questions, Standard is the right choice and Orbis picks it automatically. Full Brief is reserved for when you actually need a brief — it takes longer and costs more compute. The model won't escalate to it without a signal that you need that level of detail.
It reads pages, not just search results.
Most AI search tools work on snippets — the short excerpt a search engine returns. Orbis goes further. When a search returns relevant URLs, it fetches and reads the actual pages — the full text, not a summary someone else wrote.
This matters because most company websites are single-page apps. A standard HTTP request to a modern SaaS pricing page returns an empty HTML shell — the content only exists after JavaScript runs in a browser. Orbis uses live-crawl infrastructure that renders the page before reading it. You get the real content.
per page
live-rendered page
content, never cached
When you're researching a competitor's pricing, checking a team page, reading a Medium post, or pulling a job listing — you get the actual content. Not a stale cached version. Not a snippet. The document Orbis writes from research cites real text from the pages it read, because it actually read them.
Finding companies you didn't know to search for.
Standard competitive research has a blind spot: you can only find competitors you already know enough about to search for. If you search "alternatives to Notion" you get a listicle. You get the obvious names. You don't find the tool that launched six months ago that's quietly taking your market.
Orbis does this differently. Give it the URL of any company — a competitor, a company you admire, a product in an adjacent space — and it finds semantically similar ones. Not companies that share keywords with the URL. Companies that are structurally similar: same kind of product, same kind of customers, same kind of trajectory.
"Find companies similar to linear.app" — Orbis takes the page, runs semantic similarity across the company index, cross-references HN and Product Hunt for community traction, and checks the YC directory for batch-mates. The result is a set of competitors you can actually act on — including ones you didn't know existed.
The combination of semantic similarity and community signal is what makes this useful. Similarity alone finds what's similar on paper. Community signal tells you which of those companies anyone actually cares about.
Built for this context, not adapted to it.
O7.5 is our model.[1] It isn't a general-purpose model with a search plugin added on — it was developed and trained by Brainsless, specifically for the context a founder works in: funding, competitive analysis, hiring, product development, go-to-market. The model understands these situations the way a domain expert does, not the way a generalist does.
In practice this means: when you ask it to research a competitor, it doesn't need to be told to look at their pricing, team, and recent news. It knows that's what you need. When you ask it to find an investor, it knows you want their portfolio, thesis, and recent activity. That behavior isn't configured — it's in the model.
"We didn't train the model to be helpful in general. We trained it to be indispensable in specific."
Brainsless Research Lab · 2026There's also a compounding effect. Because Orbis operates inside your workspace — not as a separate tool — it accumulates context. Which companies you've been tracking. Which investors you've spoken with. Which questions you keep returning to. Each search becomes slightly more useful than the last, not because the model is updating, but because the context is deepening.
Orbits bring search into the background.
Search inside Orbis is reactive — you ask, it retrieves. But Orbis also runs Orbits: persistent background automations that watch things for you. Search is one of the tools they use.
A competitive intelligence Orbit monitors a set of companies weekly. It fetches their pages, checks for new HN discussions and Product Hunt activity, and tells you what changed — a pricing update, a new job posting that signals a product direction, a Show HN that just shipped. You set it once. It runs until you stop it.
"The difference between knowing and noticing is when you find out."
Most competitive intelligence fails not because the information isn't available — it's available. It fails because nobody had time to go look. Orbits remove that bottleneck.
[1] O7.5 is our model. It is developed and trained by Brainsless — built on an open-weight foundation, with a proprietary training corpus assembled specifically for the founder context. The base architecture is not disclosed. A technical paper on methodology is planned for later in 2026.
[2] Search depth modes are selected automatically in most cases. Full Brief mode is rate-limited on base Planless plans. The model will not escalate to Full Brief without explicit signal from the query.
[3] LinkedIn profile coverage is highest in the US and EU. YC directory coverage is current as of W26. Reddit and HN indexing reflects the last 24 months of public posts.