Besides everything else we’re building at Corent, we’re also working closely with traditional companies to restructure how their AI workloads run and significantly reduce their operating costs through our orchestration layer.
One key. One integration. Everything handled underneath.
Corent’s brain uses @typesafeai Jev now.
Not to generate the output, to make the decision before the output gets generated.
When a request comes into Corent, you don’t need to tell us which model to use. You send the prompt, the workload and the quality level.
Then Jev helps us decide what that request actually needs, how confident we are in that decision, and whether we should take the route or fall back to something safer.
That matters because every request has different economics.
A 2¢ image can tolerate more uncertainty.
A $2 video clip cannot.
Across 1000+ models, this changes how we route.
We can set different confidence requirements depending on the workload, the cost and the quality expected. If the confidence is too low, we stop guessing.
Routing was never really a text problem. It was a decision problem.
@CompleteSkeptic and the team did a wonderful job with JEV
Get your key at https://t.co/5LeuYAhKze
LLMs are moving too fast for developers to keep wiring them in one by one.
GPT, Claude, Gemini, Grok, DeepSeek, Kimi, Qwen, Llama and over 1000 other Open Models.
Corent gives you access through one key.
When the model landscape changes, your integration doesn’t.
Every model we serve is quality-scored and benchmarked before it enters our orchestration layer.
From there, Corent selects the best execution path based on quality, price and speed.
Same output quality, but with an average cost reduction of around 50%.
That’s what orchestration should do.
This is the problem Corent is built around.
Models change. Providers change. Compute changes. Your application shouldn’t.
Corent sits above all of it, routing each workload to the right execution path while developers and agents keep building on one stable layer.
AI is starting to look less like a model race and more like a systems problem.
Models are shipping faster, inference is getting cheaper, agents are getting more capable, and compute is becoming its own market.
The hard part now is making all of it work together.
🧵
@AnthropicAI then disclosed four incidents where Claude gained unauthorized access to real third-party systems during cyber evaluations.
At the same time, @amazon signed a Qualcomm AI-chip deal that could reach $60B.
Models, agents and compute are all accelerating at once.
It’s normal to get confused with all the new models coming out, while others are being shut down just as fast.
You don’t have to keep up with all of it anymore.
Corent stays agnostic and figures out the best available execution path for you.
Agnostic by design.
If a result falls below the quality floor, you don’t pay for it.
Corent should only bill for outputs that actually meet the execution standard.
Bad output shouldn’t become the client's problem.
The problem isn’t access to AI models.
The problem is that the model layer changes faster than developers can integrate and maintain it.
New LLMs, image models and video models keep launching, each with different APIs, SDKs, pricing and infrastructure.
Corent keeps the integration stable while everything underneath keeps changing.