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.
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.
What you get
Model teams need realistic private systems, not toy repos. We make those systems trainable by capturing the useful behavior, sanitizing risky surfaces, and packaging replayable environments.
Our FDRLs work with your team on the ground so setup, redactions, verifier updates, and artifact approvals happen with the people who actually understand the system.
You keep the source system and commercial rights. W8-RL creates the AI-ready asset layer that can be licensed, explained, and maintained.
Delhi, Bengaluru, Pune, Hyderabad, and Chennai give us proximity to the teams that own the private enterprise systems model labs cannot get from public corpora.
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: the right forward-deployed program can preserve training signal while respecting 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.
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 can be re-assessed instead of starting over. That is what makes private enterprise data practical for model training.
Assess
We work with your team to understand the codebase, workflow, sensitivity, setup path, and downstream model use case before anything is packaged.
Engineer
W8 sanitizes risky fields, captures approved browser evidence, builds tasks and rewards, and turns the system into a repeatable W8-RL environment.
Document
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.
$ Source
Identify candidate codebases, admin workflows, ERP systems, fintech flows, or enterprise operations data that could become useful training signal.
$ Assess
Map data owners, sensitivity, access constraints, setup complexity, and the intended AI use case before committing to an asset package.
$ Engineer
Sanitize what should not travel, preserve useful behavior, capture browser evidence, and build tasks, rubrics, process rewards, and outcome labels.
$ Deliver
Ship a W8-RL environment with documentation attached: retained artifacts, redactions, verifier design, reward traces, and deployment assumptions.
$ Re-assess
When the system, buyer, or permitted use changes, update the asset package so the documentation and environment stay current.
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.
$ You keep ownership
The structure is usually non-exclusive. You retain the source system, data, IP, customers, and existing commercial rights.
$ We create the asset layer
W8-RL creates the AI-ready layer: sanitized snapshots, task definitions, rubrics, reward traces, browser evidence, and environment artifacts.
$ You approve what moves
Sensitive fields, source archives, customer records, and deployment assumptions are scoped before delivery. Nothing depends on broad extraction.
$ Why FDRLs matter
On-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.
Keep the first note short. Do not send source code or sensitive records upfront. Send enough context for us to decide whether the asset is worth scoping.
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.
No. We build privacy-reviewed environments, reward structures, and training artifacts on top of private workflows. We are not packaging up and reselling source archives or sensitive records.
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.
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, not broad extraction.
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.
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.
Useful assets have real decisions, meaningful user outcomes, setup complexity, and enough evidence to score process and outcome separately.
We can begin with context, not paperwork: what the system does, why it is realistic, what must stay private, and who owns the decision.