We don't compete with your AI. We give it a job.
There's no shortage of brilliant models, search bars, and copilots. What's missing is the thing that knows your company well enough to actually do the work. We know, because we tried to build the easy version first.
We tried to build it the easy way. It broke three times.
Every shortcut to an AI that does real work hits the same three walls. We hit each one, hard. Here's what we learned, and what we built instead.
First, the knowledge was scattered.
We wired a sharp model straight to our tools and it still knew nothing. The context it needed lived in twenty apps and seven people's heads: mail, docs, Slack, the CRM, the deck nobody could find. No single tool saw the whole company, so nothing built on one tool could act like it worked there.
One brain, built from all of it, with a source on every fact.
Then the runtime kept going off the rails.
Getting a model to answer is easy. Getting an agent to do multi-step work reliably, with memory, tools, retries, and guardrails, without quietly breaking, is the part nobody had solved. Everything off the shelf was a prompt and a prayer.
Platos, our own open agent runtime, built for durable, inspectable, approval-gated work.
Then it remembered everything and knew nothing.
A brain that recalls everything is as useless as one that recalls nothing. We learned the hard way that the job was never memory, it was relevance: knowing what matters for this decision, right now, and surfacing exactly that while ignoring the noise.
A brain that ranks for relevance and confidence, not raw recall, and always shows its work.
No runtime did the job, so we built one. It's called Platos.
Open source, because the engine your employees think on shouldn't be a black box you take on faith. Two ideas make it different from a pile of prompts.
Metabolic cost
Every thought and action carries a cost, and Platos budgets it the way an organism budgets energy. Employees spend compute where it earns its keep and go quiet when there's nothing worth doing, instead of burning tokens to look busy. Autonomy you can actually afford to leave running.
Self-assembling nets
Real work isn't a fixed pipeline. Platos lets employees assemble themselves into the right network for the task in front of them, then take it apart when the work is done. The structure follows the problem, not a diagram someone drew a year ago.
Grounded in research. Tuned to your pace.
None of this is guesswork. The brain, the runtime, and the relevance model are built on published research and our own evals, not on vibes and a good demo. Then we ship at the speed real work moves, because the answer that arrives next quarter is the wrong answer.
“Does” isn't a slide. It's a queue.
Cross those three walls and this is what you get: real work, staged and waiting on your yes. Straight from the product.
“Hi Priya, since our April call we've added 3 new logos and pushed ARR to $1.4M. Check size confirmed at $250K floor.”
So, why not just use the others?
We'll tell you where each one is genuinely good. Then where we're different. Tap in for the full scorecard.
Claude does the thinking. Winsen gives it your company, your tools, and an approval boundary. Not a competitor. A workplace.
See the scorecardvs GleanGlean is great search over your knowledge. Winsen turns that knowledge into work that actually gets done.
See the scorecardvs CopilotCopilot lives in one vendor's suite. Winsen works across every tool you use, and the brain belongs to you.
See the scorecardvs Build-your-ownAgent frameworks hand you a box of parts. Winsen hands you employees who already know the job.
See the scorecardEveryone else sells you a tool. We sell you the work, done.
The questions you're actually asking.
No dodging, no contact-sales-to-find-out.
