AI INFRASTRUCTURE FOR PRODUCTION SYSTEMS
The control plane for efficient, data-driven AI.
Ada AI brings model routing, governed data, and continuous optimization into one platform—so teams can improve quality, latency, and cost without rebuilding their stack.
- One API
- Multiple model providers
- Organization controls
- OpenAI + Anthropic compatible
ONE PLATFORM · THREE COMPOUNDING ADVANTAGES
Infrastructure that makes every AI workload better.
- 01
MODEL EFFICIENCY
Make every model call earn its place.
Standardize access across providers and route workloads against the right balance of quality, latency, reliability, and cost.
- Multi-provider routing
- Budgets and failover
- Compatible APIs
- 02
DATA AS A PLATFORM
Turn AI data into shared infrastructure.
Organize model-ready files, metadata, and team context in governed workspaces built for production, evaluation, and reuse.
- Organization-scoped data
- Tags and metadata
- API-first access
- 03
AI-NATIVE IMPROVEMENT
Build a platform that learns how to run better.
Connect operational signals to controlled evaluation loops that identify better routing, data, and configuration choices over time.
- Traceable signals
- Team-defined objectives
- Human review points
BUILT FOR THE STACK YOU ALREADY HAVE
One interface across models, teams, and workloads.
Put a compatible infrastructure layer between your applications and providers. Manage access, routing, data, and operational signals without rewriting your stack.
| Model | Upstream | Priority |
|---|---|---|
| claude-sonnet | anthropic-prod | 100 |
| gpt-5 | openai-default | 90 |
| deepseek-r1 | fireworks-r1 | Fallback |
import os
from openai import OpenAI
client = OpenAI(
base_url="https://api.ada.ai/v1",
api_key=os.environ["ADA_API_KEY"],
)
client.chat.completions.create(
model="claude-sonnet-4-6",
messages=[{"role": "user", "content": "Hello"}],
)Adopt incrementally. Keep your clients, providers, and deployment choices.
THE IMPROVEMENT LOOP
Every workload becomes an opportunity to improve.
Ada AI closes the gap between operating AI and improving it—so what your organization learns compounds inside the infrastructure instead of disappearing into disconnected logs.
- 01
Run
Route production workloads through shared provider connections and explicit operating policies.
- 02
Observe
Capture tokens, latency, errors, provider behavior, and the context around each workload.
- 03
Evaluate
Compare outcomes against the quality, reliability, latency, and cost objectives your team defines.
- 04
Improve
Review better routing, data, and configuration choices, then feed approved changes back into the platform.
BUILD ON ADA AI
Build a more efficient AI stack.
Bring model access, operational data, and controlled continuous improvement into one infrastructure layer.