Animus
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Enterprise ML Data Control Piloton‑prem

Animus DataPilot

Take one real ML pipeline and make it reproducible, auditable and governed — inside your infrastructure. Timeline: 6–8 weeks.

Request pilot discussionEmailHow it works
timeline6–8 weeks

Fixed scope: one pipeline → controlled, audit-ready system.

No hosted platform. No outbound network. No external data flow.

Pilot focus

Versioned, immutable datasets
Data quality gates (PASS/FAIL)
Lineage: source → dataset → experiment
RBAC + audit trail
Core panelops sync

Control, not a demo

We deploy inside your perimeter, implement governance primitives, and deliver an assessment report so you can decide: scale, extend or stop.

scope1 pipeline · 1–2 data sources
deploymentOn‑prem / private cloud
controlsVersioning · gates · lineage
outcomeAssessment report + decision
Next actionRequest pilot discussion →

Duration

6–8 weeks

Fixed-scope pilot

Deployment

On‑prem

Private cloud / air‑gapped

Data flow

Stays inside

No external data flow

  • On‑Prem
  • Private Cloud
  • Air‑Gapped
  • No outbound
  • RBAC + Audit
  • BYOK
Why

Why enterprise ML breaks at scale

Most failures aren’t models — they’re missing control over data, lineage and audit.

Mutable datasets

Duplicated, undocumented, and constantly changing.

Non-reproducible experiments

Runs can’t be replayed with confidence.

Missing lineage

No clear path from source data to model output.

Manual audit & compliance

Risk and review rely on spreadsheets and tribal knowledge.

Knowledge silos

Critical context lives in people, not systems.

Outcomes

One real pipeline, made controllable

A fixed-scope pilot focused on governance and reproducibility — deployed inside your perimeter.

Request pilot discussion

What DataPilot solves

  • Makes a real ML workflow reproducible and audit-ready.
  • Restores traceability: data source → dataset → experiment.
  • Gives decision confidence: scale, extend or stop.

Out of scope (by design)

  • No AutoML or accuracy optimization.
  • No replacement of your ML stack.
  • No full platform rollout.
  • No hosted trial; no external data flow.

What you get after the pilot

Immutable, versioned datasets

A dataset registry you can trust.

Data quality gates

PASS/FAIL before ML usage.

End-to-end lineage

Source → dataset → experiment.

Reproducible experiments

Clear metadata and repeatable runs.

RBAC + audit trail

Who did what, when — inside your perimeter.

On‑prem deployment

Private cloud & air‑gapped environments supported.

Governance assessment report

Risks, gaps and a practical roadmap to scale safely.

Process

How the pilot works

Four steps. Strict scope. Predictable timeline.

Step 1

Scope & alignment

One pipeline, 1–2 data sources, clear success criteria and strict boundaries.

Step 2

Deploy inside the perimeter

On‑prem / private cloud, dedicated namespace, TLS, RBAC and audit logging.

Step 3

Implement controls

Dataset registry + versioning, quality validation, lineage capture, experiment tracking.

Step 4

Wrap-up & decision

Live walkthrough + assessment report. The outcome is a decision, not a demo.

Clarity

What this pilot is not

To avoid misunderstandings.

  • Not a hosted trial
  • Not a demo environment
  • Not a full platform rollout
  • Not AutoML or accuracy tuning
  • A controlled pilot focused on governance
Security

Security & data ownership

Designed for regulated and security-sensitive environments.

  • Fully on‑prem / private cloud deployment
  • Air‑gapped environments supported
  • No external data flow
  • SSO / OIDC / LDAP / Active Directory
  • Role-based access control (RBAC)
  • Full audit logging
  • BYOK (Vault / cloud KMS)

You keep full ownership

Data, keys, infrastructure and decisions stay with you.

Data
Keys
Infrastructure
Decisions
Fit

Who this is for

Enterprise ML & Data teams that need auditability and reproducibility.

  • Enterprise ML & Data teams
  • Regulated industries (finance, insurance, energy, telco, industrial)
  • Organizations concerned about audit and reproducibility
  • Teams avoiding cloud lock‑in
  • CTOs accountable for AI risk
Format

Engagement format

Fixed scope, deployed on your infrastructure.

Duration

6–8 weeks

Fixed-scope pilot

Deployment

On‑prem

Private cloud / air‑gapped

Pricing

€20k–€50k

Depends on scope

Commitment

None

Decide after the pilot

Start

Request pilot discussion

No sales pitch. No demo. Just a focused, technical conversation.

  • Bring one real ML pipeline to discuss.
  • Share 1–2 data sources and constraints.
  • We align on success criteria and strict out-of-scope boundaries.
  • If it’s a fit, we run a 6–8 week pilot inside your perimeter.

Prefer email? [email protected]