Coworker AI

More AI for less spend with context-aware model routing

Productivity

SaaS

Artificial Intelligence

Visit WebsiteSee on Product HuntTwitter ⧉

I used Coworker day in a day out. Once i changed my job, I had to use Claude. And now I miss Coworker so much. The seamless connection between Google Meets -> Note takers, and Coworker was exceptional.

No other product is better integrated than Coworker out there. And the new token optimization is world class.

Congratulations! Enterprise AI cost management is going to be one of the defining problems of the next two years. Really glad a focused team is building directly for it. Best of luck with the launch.

How is this implemented technically? Do you use cheaper models for simple tasks and more expensive ones for complex tasks, so that on average you get a lower cost? Or did you just deploy something Chinese on your own server?

Smart approach to model routing — the cost problem with AI tools is real. Curious how it decides between models when the task is ambiguous? Does it let you override the routing manually?

Running AI agents across Tuple's client base, model cost was the biggest variable we couldn't predict. The instinct is always to default to the most powerful model, but 80% of tasks don't need it — and that 80% is where the bill comes from. Context-aware routing is the right architectural call. The hard part isn't the routing logic, it's getting teams to trust the cheaper model when it handles something well. People revert to expensive defaults out of habit. Design the confidence score UI carefully — that's where user trust actually lives or dies.

Context-aware routing that dispatches to the right model tier based on task complexity is a genuinely hard inference problem. We've hit this building multi-step AI pipelines where some steps need strong reasoning and others just need basic extraction. What does your routing classifier actually look at: token count, prompt structure, semantic embeddings, or something else?

Context-aware routing is a smart play. AI costs scale fast when teams use the same heavy model for everything from summarizing notes to complex reasoning. We've been building in the AI customer success for B2B SaaS space, and Coworker AI touches on something we think about a lot. How does the company context layer stay updated as org structure or products change?

Context-aware routing that downgrades requests to cheaper models based on complexity is genuinely hard to get right. The classifier has to be fast enough not to add meaningful latency. At RetainSure we've been hand-routing between models by task type and it's become its own maintenance burden. How do you handle classification confidence thresholds, and what's the fallback when confidence is low?

Context-aware routing is the right framing for AI cost. Most teams overpay because everything gets sent to a flagship model when a smaller one would do the job. How does Coworker AI decide when a task is simple enough to downroute without degrading output quality?

The 5x tokens at opus 4.7 quality thing, how do you measure that? is it benchmarked on specific task types or more of an overall feel?

Congrats on the launch @alex_calder, very timely! Upvoted :)

So is this about storing memory/context efficiently to avoid agents running same queries again and again? Or you have a mechanism to stop agents from traversing some paths because you somehow figure out that is dead end?

Congratulations. Its an amazing launch, I have been hitting rate limit with Claude at an alarming rate these days. More tokens would definitely mean more time, and I need that!

Context-aware routing is the piece most teams skip when they're trying to cut AI costs.

they either over-engineer a manual decision tree or just default to GPT-4 for everything. Curious how you handle routing decisions when a query sits ambiguously between tiers, like something that looks simple but actually requires nuanced reasoning. Also wondering what the latency overhead looks like from the routing layer itself. But anyway, I find it very interesting

congrats on launch!

Model routing is becoming such an important layer in AI products right now.

I'm Nigel, one of the team here. 👋

We're excited to bring Coworker.ai to the world. Would love to hear from anyone who's already hit their AI spend wall, curious what the breaking point looked like for you/your org. And happy to answer any questions on how the credit system or model routing works under the hood!