01 · EXTERNAL AGENT
ops-agent · 09:41:07 UTC
The real-timedatabase forAI agents.
Connect agents to streaming and lakehouse data with fast SQL, complex joins, and control inside your cloud.
Runs in your AWS, Azure or GCP account · SOC 2 certified
Selected customers running production workloads
eightfold.ai
Celonis
One question, answered on live data.
An external agent asks in its own words. PhoenixAI runs the SQL across streaming and lakehouse tables and returns a structured result with the data timestamp attached.
02 · PHOENIXAI · SQL
3 tables · streaming + Iceberg— ms
03 · STRUCTURED RESULT
| sku | refunds_15m | baseline | on_hand |
|---|---|---|---|
| SKU-4471 | 38 | 4.2 | 120 |
| SKU-1093 | 21 | 2.8 | 0 |
| SKU-8820 | 17 | 5.1 | 64 |
| SKU-3305 | 12 | 1.9 | 310 |
— rows · data as of 09:41:07 UTC
The agent asks in natural language. PhoenixAI receives SQL — it is a database, not a chat product.
One query joins live orders, refunds and inventory. No pre-aggregation, no separate serving layer.
Rows come back with the data timestamp, so the agent can reason about freshness before acting.
ILLUSTRATIVE DEMO · SYNTHETIC DATA · TIMINGS ARE NOT BENCHMARKS
Latency that holds under agent concurrency.
Agents fan out hundreds of queries at once. This chart plots p95 latency as concurrent queries grow — one engine, no pre-computed cubes, no separate serving layer.
PhoenixAI (illustrative)
Typical batch warehouse (reference)
Synthetic curves for layout only. Published benchmarks will state workload, hardware, and percentile.
The engine, up close.
Isolated warehouses per workload, streaming ingest, and lakehouse tables queried in place — one SQL surface for agents, applications and BI.
PRODUCT UI · PHOENIXAI ANYWHERE CONSOLE
Put a real-time database under your agents.
A 30-minute session with an engineer, or start querying live data today.