AI INFRASTRUCTURE / THE CONTROL PLANE
Intelligence,
in full flow.
The infrastructure between your models and the world. Route with purpose. See every decision. Keep control.
Book a technical demoChoose a model for the work, not the other way around. Keep capability, latency and cost in the same decision.
Follow a request from your application to its outcome. Trace the decisions that matter, without collecting what you don't need.
Change providers without rewriting your product. Keep policies, environments and ownership where they belong: with your team.
Explore the platformSee the decision. Follow the flow.
A request is more than an input and an output. Follow the decisions in between.
Example request: Summarize the release notes. The request passes through workspace policy and a suitable model route. The output keeps decisions, owners and next steps together, with the routing decision recorded in a trace.
YOUR MODELS.
YOUR STACK.
Bring your models. Keep your tools. One foundation for the stack you choose.
05 / BUILT FOR THE WORK
Built for what you build.
Solutions / Product teams
AI applications
Give each model-powered feature a dependable path from prompt to product, with clear controls around the work in between.
Solutions / Engineering teams
AI agents
Connect model-driven workflows to tools through explicit permissions, bounded steps, and a record operators can understand.
Solutions / Platform teams
Enterprise operations
Give platform and operations teams a consistent way to govern model access, inspect usage, and manage change across services.
Built by people. Made to move forward.
Teams building together
Countries represented
Years of collective progress
07 / FOR THE PEOPLE BUILDING IT
From idea to first request.
Keep the tools your team already understands. Start with a familiar request format, make your routing policy visible, then bring the whole lifecycle into view.
import os
from openai import OpenAI
client = OpenAI(
base_url="https://api.your-domain.com/v1",
api_key=os.environ["API_KEY"]
)
response = client.chat.completions.create(
model="balanced",
messages=[{
"role": "user",
"content": "What should we build next?"
}]
)