Launching today
Coworker AI
More AI for less spend with context-aware model routing
253 followers
More AI for less spend with context-aware model routing
253 followers
Same AI. 5x the tokens. Coworker provides deep company context and automatically routes to the right model for every task. More chat, cowork and code with the same spend.
Interactive
Free Options
Launch Team / Built With
Coworker AI
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 graph
Build - docs, decks, pdfs, real-time dashboards, apps or any artifact and share across your org
Code - any arbitrary task in a virtual sandbox
Agents - automate workflows end to end with long-running agents and complex triggers
Meet - meeting summaries, transcripts, and follow-up actions via a meeting notetaker or ambient transcription
Enterprise-ready - all models hosted in the US, SOC 2, pen-tested, 30+ enterprise connectors
We'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
PicWish
@alex_calder is the OM2 knowledge graph built on a specific graph database or vector embeddings entirely custom under the hood?
Coworker AI
@mohsinproduct OM2 is built on a hybrid architecture: a specific graph database for entity and relationship structure, plus custom vector embeddings for semantic search and similarity. It is not off-the-shelf vector-only; the graph layer is what lets us traverse organizational relationships and maintain consistency across updates.
@alex_calder Hey Alex, congrats on the launch. The "$500K/year in December, $15M in May" framing is the realest line here, that curve is exactly why routing stopped being optional. Reading the thread, everyone's circling how to make the routing decision (confidence bands, classifier signals), but Dhruv named the harder one and moved past it: knowing after the fact whether you under-routed. That's the part I'd dig into. A wrong downgrade often isn't visibly wrong, the cheap model returns a plausible answer that's just quietly worse, and in production there's no ground-truth label to catch it. So how does Coworker close that loop? Is there a signal that flags "this got routed cheap and the output degraded," or does it rely on the user noticing and hitting rerun? Because routing quality you can't measure post-hoc slowly drifts, and the savings number stays great right up until trust erodes. That measurement loop seems like the real moat, harder to build than the classifier itself. Following along.
Coworker AI
@artem_fedorovich Our 'cheap' primary agent is still a frontier open model. This means that the baseline quality and judgment is still incredibly high and any cheaper subagents are handled by this advisor.
Cursor
congrats nigel and team!
Coworker AI
@benln thank you for your support!
Coworker AI
@benln thanks for the support sir!
@benln Thank you Ben!!
Coworker AI
@benln Thank you!
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?
Coworker AI
@anand_thakkar1 yeah, hand-routing gets brutal as the taxonomy drifts. We use confidence bands instead of one cutoff, anything ambiguous defaults up. A wrong downgrade is way more visible than a wrong upgrade so we'd rather burn a bit of cost than ship a bad answer. How big is your task list now?
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?
Coworker AI
@retain_dev can't share the internals, but token count alone is a weak signal; complexity doesn't correlate with length. The harder problem is the feedback loop: knowing when you under-routed vs just burned cost you didn't need to. What does your pipeline look like?
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.
Coworker AI
@thekrew absolutely. We have a feature that lets you rerun your query with a different model so you can compare outputs. Helps users build trust in the cheaper models when they see them hold up.
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!
Coworker AI
@fberrez1 our 'cheap' primary agent is still a frontier open model. This means that the baseline quality and judgment is still incredibly high and any cheaper subagents are handled by this advisor. And very low latency added. Try it out and let me know thoughts!
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?
Coworker AI
@irina_sumtsova The "5x cheaper" is a cost-per-task number, not a benchmark claim. On SWE-Bench and Terminal-Bench Kimi is basically on par with Opus, and honestly in most real-world use it's pretty close too. Simple tasks the 5x holds (~$0.39 vs $3.59), and even on complex work the gap isn't as dramatic as you'd expect. And since Coworker knows your context, you can decide if and when you actually need Opus over Kimi.