Coworker AI

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!

Hey Product Hunt 👋We keep hearing the same thing on repeat: enterprise AI token costs are exploding.Orgs that were spending $500K/year in December are spending $15M/year in May.And CFOs are starting to ask the same question: do we cut back AI spend, or cut heads?Coworker gives organizations a third choice: more AI, less spend.Coworker delivers the same frontier-quality chat, cowork, and code for 80% less. We do that by pairing every task with the right context and model for the job - open or closed.That means you get the same output quality as Opus 4.7, but 5x the tokens for the same spend versus Anthropic or OpenAI API rates across:Chat - grounded in your company's real context and a persistent knowledge graphBuild - docs, decks, pdfs, real-time dashboards, apps or any artifact and share across your orgCode - any arbitrary task in a virtual sandboxAgents - automate workflows end to end with long-running agents and complex triggersMeet - meeting summaries, transcripts, and follow-up actions via a meeting notetaker or ambient transcriptionEnterprise-ready - all models hosted in the US, SOC 2, pen-tested, 30+ enterprise connectorsWe're getting things started by giving everyone who signs up this week 500 credits on us. And if you sign up in the next 24h you'll get an additional 200 credits.Head over to Coworker.ai - I can't wait to see what you build.Alex