
From the team
Blog
What we ship, how it performs,
and practical ways to put rtrvr to work.


Research
How prefix caching cut our browser-agent token bill by 90%
The cache boundary, measurements, and implementation choices that made long browser runs materially cheaper.
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Product
The self-improving action layer we are building
A concrete roadmap for turning successful browser work into reusable actions without losing review and control.
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Proof
From 500 event attendees to 10 prospects
How a browser-based enrichment workflow researched an attendee list, kept its sources, and prepared the best matches for review.
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Research
Websites need headless agents, not chatbots
Browser agents are a compatibility layer. The durable interface is a site-side agent that exposes intent handoff, knowledge, auth, tools, payments, and policy.
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Research
A cheaper browser agent: let DeepSeek write the plan as code
What changed when we replaced repeated tool selection with a compact program executed against a text-first browser state.
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Research
Navigation Was Never the Moat: Why Agents Make the Sell Side Bigger, Not Smaller
Models will soon navigate any website perfectly. That was never the moat. The agent that converts is the one the seller owns — its accounts, its payments, its data — and the world's biggest retailer just proved it inside ChatGPT.
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Research
Replacing browser-agent tool loops with eval() in a Chrome sandbox
A for-loop should not cost tokens. We let the model write browser workflow code once, then execute it locally through a sandboxed `rtrvr.*` DSL layer.
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Research
The Four Architectures for Website AI Agents
RAG bots can only talk. API-tool agents ship a second maintenance org. Code sandboxes burn per-user server cost. Only DOM-native execution inherits the web's own truth model — live HTML, user session, existing IAM. A structural comparison.
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Product
AI Subroutines: automation scripts that run inside your tab
Turn a successful browser action into a deterministic routine that can run again without another planning loop.
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