Enterprise · 2-week POC
Bring your baseline. We beat it.
Most enterprise ML conversations get stuck in the same place: you already have a model in
production, nobody wants to bet on a tool that can't prove it beats what you have on
your data against your definition of good. OctOpus starts from your
baseline — current metric, business decision, success threshold — and runs a live research
loop that either beats it measurably or tells you exactly why it can't. You get the delta
report either way.
Why enterprise pilots fail — and what we do instead
Every head of data science we talk to points at the same three failure modes. OctOpus is built to kill all three before the first experiment runs.
Anchored to your baseline
You tell us your current metric ("we ship on 0.78 AUC today"). Every experiment is logged as Δ vs baseline. If we don't beat it, we say so — on the same data, the same split, your definition of good.
Starts from the business problem
Discovery captures what decision this model actually supports — churn triage, demand forecast for inventory, fraud review queue — before a column gets picked. Research runs are framed around the decision, not the schema.
Knows what "good" looks like
You set the threshold that would make this worth deploying ("MAPE under 5%"). The agent stops the moment it hits that on held-out data — no grind, no vanity metrics, no wasted compute.
Procurement, security, private deploy
Security questionnaires, data-handling review, private / hybrid deployment, annual commits, and a named engineer owning your rollout. The platform layer lives here so your DS team can focus on the baseline-delta, not the SOW.
Plugs directly into your data stack
No CSV exports. No data team ticket. No "send us a sample." OctOpus queries your production tables directly — with the same security model your warehouse already enforces.
Snowflake
Warehouse · service account or OAuth
BigQuery
Warehouse · service account JSON
Databricks
Lakehouse · SQL Warehouse + Unity Catalog
Redshift
Warehouse · IAM or password auth
PostgreSQL
Database · direct or SSH tunnel
MySQL / MariaDB
Database · direct or SSH tunnel
SQL Server
Database · AD or SQL auth
MongoDB
Database · connection URI
AWS S3
Storage · Parquet, CSV, JSON, Excel
REST API
Any JSON endpoint with bearer / API key
Salesforce
Custom · REST / Bulk API — request
SAP / ERP
Custom · S/4HANA — request
Azure Blob
Storage · private container
Google Cloud Storage
Storage · GCS bucket
Kafka / Kinesis
Streaming · near-realtime — request
Your internal API
Custom · we build it for you
Runs inside your VPC on private deployments. Credentials are encrypted at rest and rotated on your schedule.
Custom connectors for SAP, Salesforce, Kafka, and internal APIs are built by our team as part of the rollout —
request one.
How a 2-week POC runs
One intake form. One research run against your data. One delta report. No slideware, no mystery SOW.
Week 0 · POC brief
Baseline + business question
You send us one dataset, the target column, your current model's metric, and the business decision this supports. We reply within a business day with a POC scope — what we'll try, what we won't, what "win" means.
Week 1 · Research run
OctOpus beats — or explains
The agent writes a research plan, runs experiments live against your data, and either beats your baseline on held-out folds or tells you precisely where it plateaus and why. Every experiment is a commit; nothing is hidden.
Week 2 · Delta report
Signed-off go / no-go
You get a one-page report: baseline, best OctOpus run, delta, confidence intervals, feature-importance diff, deploy cost estimate. Your DS team signs it off. If the delta isn't worth it we walk away.