Skip to main content

Private Data Collaborations

Turn private enterprise systems into AI-ready RL assets.

You sit on codebases, admin tools, ERP flows, fintech systems, or operations data that could power coding and computer-use models. It just has to be usable, controlled, and defensible first.

WootzApp licenses that asset non-exclusively, engineers it into a W8-RL environment, and attaches the documentation model teams need to trust it. Your IP stays yours. You approve what leaves.

  • New revenue from dormant systems
  • Non-exclusive licensing
  • Source and IP retained
  • Documented W8-RL delivery
Become a data partnerSee qualification checklist

Why We Specialize In India

India is where large-scale services delivery, GCC infrastructure, and private enterprise software overlap. That density is why W8-RL specializes here: a forward-deployed program preserves training signal and respects the privacy posture that enterprise data owners need.

US$283B -> US$350B

India tech sector

FY25 to projected FY26 sector scale, showing why India is a strategic software market in its own right.

5.8M professionals

Engineering depth

India contributes 28% of global STEM talent and 23% of the world’s software engineering professionals.

4.3% of world services exports

Global services weight

India ranks second globally in telecommunication, computer, and information services exports.

1,800+ GCCs

Enterprise density

India’s GCC base employs 1.9 million professionals and is projected to reach US$110B by 2030.

Selected benchmarks from India's Economic Survey 2024-25 and IBEF 2025 reporting.

From Private Systems To RL Assets

One pipeline: assess, engineer, document, and re-assess.

W8-RL turns private systems into AI-ready assets for RL. The pipeline covers the full lifecycle: from initial asset assessment through signal-preserving engineering to documentation that travels with the environment.

When the system, recipient, or permitted use changes, the asset is re-assessed in place. That keeps private enterprise data practical for model training.

  1. 01

    Assess

    We scope the asset and the intended AI use.

    We work with your team to understand the codebase, workflow, sensitivity, setup path, and downstream model use case before anything is packaged.

  2. 02

    Engineer

    We preserve the signal and constrain what should not move.

    W8 sanitizes risky fields, captures approved browser evidence, builds tasks and rewards, and turns the system into a repeatable W8-RL environment.

  3. 03

    Document

    Every delivery comes with an evidence package.

    The environment ships with what was retained, what was removed, what can be replayed, how rewards are scored, and why the asset is safe to use downstream.

W8-RL Asset Pipeline

  1. 01
    SourceIdentify candidate codebases, admin workflows, ERP systems, fintech flows, or enterprise operations data that could become useful training signal.
  2. 02
    AssessMap data owners, sensitivity, access constraints, setup complexity, and the intended AI use case before committing to an asset package.
  3. 03
    EngineerSanitize what should not travel, preserve useful behavior, capture browser evidence, and build tasks, rubrics, process rewards, and outcome labels.
  4. 04
    DeliverShip a W8-RL environment with documentation attached: retained artifacts, redactions, verifier design, reward traces, and deployment assumptions.
  5. 05
    Re-assessWhen the system, buyer, or permitted use changes, update the asset package so the documentation and environment stay current.

Good Fit

What makes a private system valuable to model teams.

The best assets contain real work: messy setup, business logic, edge cases, integrations, admin flows, and outcomes that cannot be learned from public code alone.

Good fit assets

  • Private codebases, admin systems, and operational workflows with real business logic
  • ERP, fintech, card-stack, IT-services, claims, or enterprise operations flows
  • Systems whose real work is absent from public corpora

What helps sanitization

  • Clear data owners and approval paths
  • Test accounts, synthetic tenants, or controlled staging data
  • Known fields, screens, records, and artifacts that should never be exported

What we can adapt around

  • Swapped databases, secrets, and infrastructure dependencies
  • On-prem, VPC, or managed workflows with constrained data movement
  • Legacy systems that need packaging before they become trainable

Program Shape

  1. 01
    You keep ownershipThe structure is usually non-exclusive. You retain the source system, data, IP, customers, and existing commercial rights.
  2. 02
    We create the asset layerW8-RL creates the AI-ready layer: sanitized snapshots, task definitions, rubrics, reward traces, browser evidence, and environment artifacts.
  3. 03
    You approve what movesSensitive fields, source archives, customer records, and deployment assumptions are scoped before delivery. Delivery moves only the scoped artifacts.
  4. 04
    Why FDRLs matterOn-ground deployment lets us understand enough of the system to preserve training utility while keeping the commercial and privacy posture clear.

Commercial terms can start as a focused pilot or become a standing program. The constant is the same: retained IP, sanitized artifacts, defensibility documentation, and W8-RL environments that model teams can actually train on.

Details

What to send first.

Keep the first note short and send context only. Source code and sensitive records come later, once scoping starts. We use the note to decide whether to scope the asset.

What to send us first
  • What the codebase, dataset, or workflow does
  • Why it is hard, mature, or commercially realistic
  • Which screens, fields, records, or documents contain sensitive data
  • Whether test accounts, synthetic tenants, or staging data are available
  • Which artifacts can leave the enterprise boundary and which must stay inside
  • Whether the workflow comes from IT services, ERP, card stack, fintech, or enterprise operations
  • Where process failures and outcome failures can be observed in the browser
  • Any existing tests, logs, audit trails, or documentation that can support verifier design
Do you need exclusive rights?

No. Non-exclusive licensing is usually the right structure. You keep ownership and can continue to use the system, data, and IP however your business requires.

Are you reselling raw data or source code?

No. We build privacy-reviewed environments, reward structures, and training artifacts on top of private workflows.

What leaves the enterprise boundary?

Only the artifacts agreed in the asset design: reviewed snapshots, task specs, rubrics, reward traces, documentation, or packaged environments. Sensitive fields can be redacted, constrained, or kept inside a controlled deployment.

How is sensitive data protected?

FDRLs map sensitive surfaces first, define what can be captured, remove or constrain risky fields, and document the decisions attached to each environment. The goal is signal-preserving sanitization.

What kinds of companies do you work with?

We work with Indian IT services companies, financial services IT providers, card stack companies, ERP companies, and enterprise software companies, with presence in Delhi, Bengaluru, Pune, Hyderabad, and Chennai.

How do you think about pricing?

Pricing depends on asset quality, workflow complexity, setup maturity, privacy constraints, verifier design, and whether the engagement is a focused pilot or a standing program.

What makes a workflow especially useful?

Useful assets have real decisions, meaningful user outcomes, setup complexity, and enough evidence to score process and outcome separately.

Private Conversation

If your team is sitting on private systems that could power AI, start with a short note.

We begin with context: what the system does, why it is realistic, what must stay private, and who owns the decision.

Start a private conversationfounders@wootzapp.com