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How it Works
We built a system of intelligent agents that optimize how your data warehouse scales, schedules, and runs queries.
Each agent targets a specific layer: scaling and sizing, workload placement, and query structure. Together, they help your warehouse - and your data engineering team - do more with less.
Autoscaling Agent
Smarter multicluster scaling that lowers cost without compromising performance
Smarter scaling
Snowflake's multicluster scaling uses the same algorithm for every workload, leaving a ton of credits on the table.
Our agents analyze and scale each workload independently, leading to utilization gains of 25%+.
Predictive warehouse management
Our models are trained on your metadata logs, which means our agents understand your
workload's unique spikes and fluctuations, enabling optimal, real-time scaling decisions.
Lower cost, no performance impact
We model agentic warehouse management as a constrained optimization problem: minimizing costs with no increase to latency.
Our agents make thousands of decisions each day to keep your warehouse running smoothly at minimal cost.
Scheduling Agent
Run your data warehouse like Google runs its datacenters.
Stop paying for idle warehouses
The average Snowflake user's warehouse utilization is between 40% and 60%. That means just about half your compute bill is paying for idle machines.
This happens because each workload is statically assigned to one warehouse - no matter what machines you're already paying for when that workload comes in.
Realtime Scheduling
Instead of routing each query to a fixed warehouse, our agent analyzes your running workloads to understand where to send query to maximize utilization without impacting performance.
It's like Kubernetes for Snowflake, and it drops idle time below 10%.
LLM-Powered Routing
Companies like Google, Amazon, and Microsoft all run their datacenters the same way: a central system uses resources requirements to optimally route workloads in real-time.
Our agent does the same, powered by LLMs that understand your workloads' resource needs and scaling characteristics.
Query Agent
Our query agent optimizes your SQL on the wire - it's like having an expert data engineer look at every piece of SQL before it hits your data warehouse.
Neural Optimization
We use LLMs for optimization, automating query improvements that previously required human experts.
Formal Verification
Our agents don't hallucinate. Every optimization is cross-checked using formal verification, ensuring that the optimized query is mathematically equivalent to the original.
Optimize Everything
Your checked-in SQL is probably good - but what about ORM-generated queries coming from tools, or one-off analyst queries typed straight into Snowflake's UI?
Our agent works with every query, no matter where it comes from, leading to improved performance and reduced costs across the board.
select NATION.N_NAME as N_NAME, COUNT(1) as "cnt:C_NAME:ok"
from SNOWFLAKE_SAMPLE_DATA.TPCH_SF1000.CUSTOMER CUSTOMER
inner join SNOWFLAKE_SAMPLE_DATA.TPCH_SF1000.NATION NATION
on (CUSTOMER.C_NATIONKEY = NATION.N_NATIONKEY)
where CUSTOMER.C_MKTSEGMENT not
in ('AUTOMOBILE') and CUSTOMER.C_MKTSEGMENT is not null
group by NATION.N_NAME; select t0.N_NAME as N_NAME, SUM(1) as "cnt:C_NAME:ok"
from SNOWFLAKE_SAMPLE_DATA.TPCH_SF1000.CUSTOMER CUSTOMER
inner join (
select CUSTOMER.C_NATIONKEY as C_NATIONKEY, NATION.N_NAME as N_NAME
from SNOWFLAKE_SAMPLE_DATA.TPCH_SF1000.CUSTOMER CUSTOMER
left join SNOWFLAKE_SAMPLE_DATA.TPCH_SF1000.NATION NATION
on (CUSTOMER.C_NATIONKEY = NATION.N_NATIONKEY)
group by 1,2) t0
on (CUSTOMER.C_NATIONKEY = t0.C_NATIONKEY)
where (case when ((CUSTOMER.C_MKTSEGMENT in ('AUTOMOBILE'))
or (CUSTOMER.C_MKTSEGMENT IS NULL)) then FALSE else TRUE end)
group by 1;