Nuvepro - Work Transformation
Work Transformation

Learn to build agents, agentic workflows and software factories.

Through simulations of your enterprise’s real work.

We shape business environments around your enterprise’s systems, data and rules, so your teams can build, test and improve how the work gets done.

Discuss your work
See 53 real enterprise examples

Hands-on labs and learning customers

  • TCS
  • Infosys
  • Wipro
  • Capgemini
  • HCL
  • LTIMindtree
  • Persistent
  • Skillsoft
  • Great Learning
  • upGrad
  • PwC
  • EXL
  • Genpact
  • Zensar
  • Tech Mahindra
  • Maveric
  • Microland
  • Edureka
  • Mphasis
  • Virtusa

Connect AI to the work your enterprise needs done.

Software teams want agents that find real test failures and factories that produce checked changes. Developers want to build with models in their existing roles. Finance and sales teams want better invoice preparation and lead research. Each needs a simulation shaped around its own systems, rules and decisions.

Define how the workflow changes, what your people and agents will build and do, and how the result will be checked. See changing roles in the data →

Your team builds or configures the agreed solution. Nuvepro provides the scoped project, environment and preparation. Your enterprise reviews the evidence and decides what to accept and operate.

Hiring data describes market signals, not Nuvepro customer outcomes.

Start with your work

Start with a role or workflow you want to reimagine.

01

Reimagine a Role

Define what people will build and do differently: from finance workflows to testing agents, models and FDE delivery. Prepare for a current or new role through enterprise-specific projects.

02

Reimagine a Workflow

Bring work you want to improve, such as generating invoices or enriching leads. Define the AI solution, the people and agent responsibilities, and how to check the result.

Or explore the data first

Work transformation in practice

What work do you want to change?

Build on the work your people already do. These examples show how software delivery, testing and business teams could add AI capabilities, what they would produce and what needs checking.

Task Intelligence follows each workflow across the teams it passes through and classifies every task as automate, augment or human-only. Those are proposals. The simulation tests them before your owners decide. Explore the Task Intelligence framework →

Software engineering and QA

Build a website-testing agent

The work today
Engineers repeat browser journeys, reproduce failures and assemble test reports.
What changes
Your team builds an agent that exercises agreed journeys, runs executable checks and records failures. Engineers verify findings and decide what to fix.
Bespoke to your enterprise
Your test application, user journeys, access boundaries and release criteria.
What the team produces
A testing agent, repeatable checks and a failure report.
What needs checking
Known defects detected, reproducible failures and false positives reviewed. Test completion alone is not a pass.
What to measure after rollout
Defects that reach release and time per regression cycle, against your current release.
Read the testing-agent guide

IT services and software delivery

Build a software factory

The work today
Delivery teams repeat requirements, implementation, testing and review for each change.
What changes
Your team builds a repeatable process in which agents prepare changes, independent checks test them and engineers review the result before release.
Bespoke to your enterprise
Your client requirements, repository, dependencies, coding standards and release controls.
What the team produces
A build process, reviewable software changes, test results and a record of failures and repairs.
What needs checking
Required behavior, regression failures and dependencies checked against the built application. The release owner decides what ships.
What to measure after rollout
Changes rejected at review and lead time from requirement to release.
Read the software-factory guide

Finance operations

Generate invoices with AI

The work today
People collect order details, check amounts and prepare invoices across systems.
What changes
Your team builds or configures a workflow to prepare invoice drafts and surface missing details. People resolve exceptions and approve issue.
Bespoke to your enterprise
Your order data, billing rules, accounting interfaces and approval limits.
What the team produces
An invoice workflow, checked drafts and an exception queue.
What needs checking
Correct customer, amounts and tax treatment; missing or duplicate orders; approval before issue.
What to measure after rollout
Invoices reworked or reissued and time from order to issued invoice.
Read the invoice guide

Bring the work your team is asking about.

Prepare for Forward Deployed Engineering →

Connect stakeholder discovery to a bounded build, integration decisions and demonstration.

Build an AI software factory →

Define a repeatable software product, build stages, independent verification and release ownership.

Fine-tune a model for a specific task →

Scope training data, a baseline, held-out evaluation and deployment constraints.

Test a website for security weaknesses →

Agree an authorized target, test boundaries, validated findings and remediation checks.

Build or adapt an AI model →

Start with the task and data. Distinguish model development, adaptation and training from scratch.

Build an agentic workflow →

Connect tools, decisions and human handoffs around an agreed enterprise work product.

Enrich leads and prepare outreach →

Define sources, CRM fields, message review and the sales decisions people keep.

Bring one baseline. Your operating owner sets the target.

Illustrative examples for scoping, not customer results or a fixed catalogue.

The new workforce: people and agents

Are your AI agents ready to do the work you’re giving them?

Explore Business Environments →

Already have an agent? Test it on the work you expect it to do. We scope a bespoke business environment around your enterprise’s systems, data, rules and exceptions. Simulations exercise that work; evaluations check actions and results. Your owners review the evidence before deciding on deployment. Team preparation can be scoped alongside it.

One environment. Connected use cases.

For example, supplier onboarding and catalog selection share supplier and product records. Agree which business conditions the environment must represent and have your domain experts review them.

Within the environment

Run simulations

Exercise normal work, exceptions and human handoffs using the shared business state.

Evaluate the work

Check actions, outputs and state changes against independently reviewed criteria.

Your people

Extend existing roles with AI. Prepare for new ones.

A tester can build an agent that exercises the application they already test. A developer can add model integration or fine-tuning to their work. A delivery team can build a software factory around its client’s requirements. Start with the role and experience people already have, then build the AI capabilities their work needs.

Simulations connect those capabilities to your systems, data and review standards. Each person demonstrates the new responsibility before it is assigned. A move into a new role is a separate choice: FDE preparation adds stakeholder discovery, integration and customer delivery responsibilities.

Reimagine a Role →

Map the changed responsibilities within a role, or the path from the current role to a target role.

Forward Deployed Engineers →

Practise conversations with simulated stakeholders, define a bounded problem, build with the provided data and tools, and demonstrate the result against agreed criteria.

Hands-on labs →

Sandboxes, practice projects, skill validation assessments and readiness bundles for the capabilities your changed work requires.

Delivering through a learning platform? Review catalogue delivery, LMS and SSO launch, licensing and support.

What our customers say

Customers on hands-on practice and talent development.

Explore Project Readiness →

TCS

Any learning is incomplete if it is not amply practiced and assessed in a real-world like environment.

Janardhan Santhanam

Global Head, Talent Development at TCS

Read full testimonial
Any learning is incomplete if it is not amply practiced and assessed in a real-world like environment. At TCS, we want our employees to get hands-on experience so they can effectively use that learning to design transformational solutions for our customers' real life business problems. Hand-on labs like the ones Nuvepro helped us rapidly set up, deliver a balanced learning of theory and practice. The scalability and flexibility in setting up a production-like environment.

Genpact

Our collaboration with Nuvepro for their Playground Labs reaffirms our ongoing commitment to build talent for the future and enables them to continue creating value for our clients while building career resilience.

Sreekanth Menon

AI/ML Global Practice Lead, Genpact

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At Genpact, one of our key pillars of success has been the sustained investment in the learning and development of our employees to build critical new skills in areas such as digital, data, and analytics that are needed in a changing world. Our collaboration with Nuvepro for their Playground Labs reaffirms our ongoing commitment to build talent for the future and enables them to continue creating value for our clients while building career resilience.

Tech Mahindra

With our partnership with Nuvepro, we are looking to further strengthen the base by investing in niche skills to build a future-ready talent ecosystem.

Harshvendra Soin

Global CPO & Head of Marketing at Tech Mahindra

Read full testimonial
At Tech Mahindra, we are focused on supporting the digital transformation journey of our customers across the globe by providing relevant solutions powered by emerging technologies such as cloud. In all this, our tech talent serves as a pillar of support. With our partnership with Nuvepro, we are looking to further strengthen the base by investing in niche skills to build a future-ready talent ecosystem.

Excerpts from testimonials previously published on Nuvepro.com. Names and roles shown as published.

Find the route for your organization.

We help shape the project and preparation around your requirements.

Plan impact measurement →Understand commercial scope →
Choose the discussion that fits your work →

Preparation engagement

6–8 weeks
AI Bootcamp duration

Field Truth: Nuvepro’s field observations from meeting recordings and follow-up discussions. Where this leads: The Agentic Enterprise, the book behind the method.

Frequently Asked Questions

Start with work your enterprise wants to improve, such as invoice generation, lead enrichment or website testing. Define the changed workflow, what people and agents do, what your team builds and how to check the result. Nuvepro shapes simulations and environments around your requirements.
Yes. Bring requirements for testing agents, authorized security testing, model development, fine-tuning or FDE delivery, alongside functional work such as finance and sales. Prerequisites, tools, build scope and assessment need to be agreed for each project.
Yes. Discuss a bespoke business environment and representative cases for the work your existing agent is expected to do. Team preparation is a separate choice. Evaluation results concern the represented conditions; your enterprise decides operating acceptance.
In a team project, your people build or configure the agreed solution with Nuvepro’s scoped project, environment and preparation. For existing-agent evaluation, bring the agent and agree the environment and checks. Integration, licences, ownership rights, production operations and support are confirmed in the engagement scope.
Tool capability is one part of the question. Results also depend on the workflow, available data, decision ownership and how people use and review AI outputs. Start with the work you want to improve and the tools approved for it. Nuvepro helps define the changed work and the solution your team will build or configure, then scope the project, bespoke environment and checks around the gaps.
AI-augmented work uses AI to assist people. An AI-first approach deliberately reviews how people and agents contribute to the workflow, with clear responsibilities, evidence, decision authority and handoffs. Neither label establishes readiness or business impact; those need evidence for the work you intend to operate.
The AI Bootcamp runs for 6 to 8 weeks. Agree the workflow, roles, practice scenarios and assessment criteria first. Evidence covers the responsibilities assessed. Production rollout and ongoing support are separately scoped.
AI can change responsibilities within existing roles and create new roles. Some people may need to supervise outputs, handle exceptions or build and maintain the workflow. Start with their experience and the work they will own, then assess the preparation needed. Staffing and headcount decisions remain with your organization.
Changed work needs managers and operating owners who understand the responsibilities, handoffs and evidence behind it. Discuss manager preparation, team communication and adoption support alongside the workflow. Agree which activities Nuvepro supports and which your organization owns; the scope depends on the engagement.
Connect your partner’s workflow and delivery expertise with Nuvepro’s hands-on preparation, simulations and assessments. Agree who defines scenarios, supplies approved tools and access, reviews readiness evidence and owns implementation. Task Intelligence informs the design. Joint delivery, customer reuse rights and continuing support are agreed for the selected work.