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, then choose the deployment mode that fits: local desktop execution for no-upload workflows, or private/VPC rollout for a shared internal platform.

Start a 2-week POC See the live workspace → How it works Try the web app
Try the live workspace
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.
Local desktop workflow, built into Enterprise
Same OctOpus research loop, but running beside your files for teams that cannot upload sensitive data into shared SaaS.
Local folder · zero upload

Run OctOpus next to the source data.

Point the desktop app at a folder, mounted drive, or managed workstation and OctOpus profiles the data, runs experiments, writes artifacts, and stores models right there. You keep the same product workflow as the web app without moving regulated data out of your boundary.

  • CSV, Excel, and Parquet files stay where your team already works
  • Artifacts, logs, and trained models remain in the local workspace you control
  • Ideal for finance, healthcare, public sector, and high-trust internal analytics teams
Talk about desktop rollout See the enterprise rollout flow
Why enterprise teams choose the desktop mode
Use the desktop deployment when policy says the work must stay local, then expand into private cloud when you need broader team rollout.

Local-first execution

Datasets, artifacts, and logs stay inside an environment your security team already trusts for sensitive work.

Same research workflow

The discovery, planning, experiment, artifact, and reporting flow stays consistent across web, desktop, and private deployment.

Fast approval path

Teams can start with a workstation or on-prem box while procurement and broader cloud review continue in parallel.

Clear path to private rollout

Once the baseline delta is proven locally, the same enterprise motion can extend into a shared VPC deployment and governed team access.

Security, privacy, and sandboxing
OctOpus is designed for teams that need strong data-handling controls, not just model accuracy. The operating assumption is that LLM-authored training code should be treated as hostile by default.

Data privacy and residency

Choose the deployment boundary that fits your policy: local desktop execution for no-upload workflows, or private / VPC deployment inside your AWS, GCP, or Azure environment.

Secrets stay protected

User-provided API keys and connector credentials are encrypted at rest with Fernet using a per-install random secret, then decrypted only inside the server process.

Sandboxing model

Every research-run subprocess launches with a scrubbed environment, so LLM-authored train.py code cannot read provider secrets from os.environ. Holdout data is stored outside the agent workspace and log streams are redacted before they reach disk or the UI.

Audit and access control

Enterprise includes SSO / SCIM, role-based access control with workspace isolation, and an exportable audit trail covering the research plan, every experiment, revisions, holdout metrics, and deployed artifact hash.

Built for regulated review cycles
For SOC 2-, GDPR-, HIPAA-, PCI-, and model-risk-sensitive workflows, Desktop and VPC deployments keep regulated data on your perimeter while preserving an inspectable audit trail of the full research loop.
Request compliance docs Read full security page
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.
Send us your POC brief
Email sales@octoopus.dev with: (1) the business question, (2) one dataset + target column, (3) your current model's metric on held-out data, (4) the threshold that would make you deploy. We reply within one business day with a scoped 2-week POC.
Start a POC