For Top Indian Engineering & AI Institutes
Build the system that proves whether AI actually works in the real world.
A 10-week applied AI research sprint where selected students and faculty work with Agentic Universe on one of the hardest unsolved problems in enterprise AI:
How do we know whether an AI agent actually caused a business outcome — not just completed a conversation?
Selective institute collaboration · 4–6 students per cohort · 1 faculty mentor · 10 weeks
Built with enterprise reality
Agentic Universe works with enterprises across BFSI, insurance, real estate, consumer operations, and customer engagement.
Real workflows. Real customers. Real AI deployment constraints.
Most AI programs teach students how to build agents. This program teaches them how to prove whether agents actually work.
AI is moving from demos to deployment.
Enterprises are already using AI agents for renewals, claims, lead qualification, support, collections, onboarding, and customer engagement.
But the most important question remains unanswered:
Did the AI actually produce the desired business outcome?
- A conversation can sound fluent and still fail the business.
- A call can be completed and still contain a hallucination.
- A customer can be engaged and still not convert.
- A workflow can appear automated and still require human recovery later.
This program is designed to build the missing layer of enterprise AI:
Outcome Intelligence.
A system that measures whether AI agents are factually correct, workflow-compliant, safe to scale, and actually moving customers toward real enterprise outcomes.
Enterprise AI is still measured by activity. Enterprises need it measured by outcomes.
Today, most AI systems are evaluated using surface-level metrics:
Activity Metrics
How long did the conversation last?
Did the AI finish the call or chat?
Did the customer sound positive or negative?
Did the agent broadly follow the script?
But enterprises don't buy AI for activity.
Outcome Metrics
They buy AI for outcomes.
The research question is simple — and hard:
Can we build a system that proves whether an AI agent actually contributed to the business outcome it was deployed to create?
Why this is not solved today
This is not a chatbot problem. It is a systems, evaluation, and causal-intelligence problem.
Outcomes are delayed
A call may happen today, but the renewal, conversion, or claim outcome may appear days or weeks later.
Outcomes are noisy
A customer may convert because of the AI, a human follow-up, pricing, prior intent, or timing. The system must separate signal from noise.
Voice is messy
Real enterprise voice AI includes accents, interruptions, silence, background noise, code-switching, and STT errors.
Truth is domain-specific
The AI must be checked against product rules, policy terms, scripts, compliance constraints, and workflow logic.
Existing evaluation is shallow
Most benchmarks evaluate responses. Enterprise AI needs evaluation of workflows, consequences, and business impact.
Causality is missing
Current systems can say what happened after a call. They cannot reliably say what happened because of the AI.
The Research Frontier
Outcome-GroundedAgentic AI.
The next generation of enterprise AI will not be defined by agents that sound human.
It will be defined by agents that can:
- complete workflows
- avoid factual errors
- follow business rules
- improve enterprise outcomes
- explain what worked
- learn from what failed
This program asks students and faculty to help build that new layer.
The winning AI systems will not just answer.
They will produce outcomes — and prove them.
The Agentic Universe Research Sprint
A 10-week applied AI collaboration for selected institutes.
A focused 2.5-month program where 4–6 students and one faculty mentor work directly with the Agentic Universe team on a frontier enterprise AI problem.
- hallucination
- factual correctness
- workflow adherence
- compliance risk
- customer progress
- outcome-readiness
What the cohort will actually build
Participants work on a real applied AI problem with production context, not a toy use case.
Transcript Intelligence
Analyze real AI–customer conversations and convert them into structured signals.
Hallucination Detection
Detect when an AI agent makes unsupported, incorrect, or risky claims.
Workflow Adherence
Evaluate whether the AI followed the intended enterprise process.
Outcome-Readiness Scoring
Estimate whether a conversation moved the customer toward the intended business outcome.
QA Intelligence Dashboard
Turn evaluation into a product experience.
Improvement Recommendations
Identify what should change in the AI system.
How the 10 weeks work
Problem Framing & Dataset Familiarization
Students understand the workflow, business context, AI system, evaluation problem, and available data.
Outputs: project charter, workflow map, dataset schema, success definition
Annotation & Error Taxonomy
Students define what good and bad AI behavior looks like across real interactions.
Outputs: annotation rubric, sample labels, hallucination taxonomy, workflow failure taxonomy
Evaluator Prototype
Students build the first version of the automated evaluation system.
Outputs: hallucination detector, workflow scorer, call-level quality score
Outcome & Insight Layer
Students connect conversation behavior to enterprise outcome-readiness.
Outputs: outcome-readiness score, behavioral insights, improvement recommendations
Demo, Report & Showcase
Students present the final prototype, findings, and next-step roadmap.
Outputs: prototype demo, technical report, institute showcase, future research roadmap
What the institute gets out of this
A high-signal industry-academia collaboration: real frontier AI problem, production context, learning outcomes, and institutional visibility.
A flagship applied AI collaboration.
The institute participates in building one of India's earliest outcome-grounded enterprise AI systems.
- Differentiated AI industry partnership
- Institutional visibility in applied AI
- Real enterprise problem exposure
- Potential joint case study
- Demo day / showcase opportunity
- Pathway to long-term research collaboration
Built from the field, not from slides.
Grounded in live enterprise environments where AI systems must perform under real business constraints.
Generali CentralWorking with enterprise teams on AI-led workflow transformation across insurance and customer operations.
MSIGExploring high-trust AI systems for regulated, customer-facing insurance workflows.
BrigadeApplying AI to real-world customer engagement, lead qualification, and conversion workflows.
CasagrandUnderstanding multilingual, regional, and operational realities of enterprise AI deployment.
Why Agentic Universe is the right partner
Agentic Universe is building AI agents for enterprise workflows where accuracy, reliability, context, and outcomes matter.
We work across customer-facing and operational use cases including:
- insurance renewals
- claims servicing
- welcome calls
- sales conversion
- agent reactivation
- customer support
- lead qualification
- workflow automation
We bring institutes what most academic projects cannot:
- Real enterprise context
- Live deployment learnings
- Anonymized workflow examples
- Customer problem statements
- AI/product mentorship
- A pathway from research to real-world implementation
This is not a simulated AI problem. This is frontier AI meeting enterprise reality.
Learn from builders who are all in.
Anirudh Arun
Founder & CEO, Agentic Universe
Dropped out at 19 from MIT Manipal. Raised VC funding at 19. Now building with one conviction: India can create a $100 billion outcome-driven AI company.
Part founder, part field operator, part category evangelist — obsessed with making AI actually work in the real world.
LinkedIn
Deepak Jaitly
Global GTM & Strategy
Left UKG at 49 to build again. Helped scale Agentic Universe to over $1M ARR and is now leading the global GTM vision.
Operator at heart. Institution-builder by choice. Focused on turning frontier AI into enterprise outcomes at global scale.
LinkedIn
Aryamann
Product, Systems & Engineering
Rejected a JP Morgan offer in his second year. Built his own startup. Won hackathons. Breaks things, builds faster, and thinks like a systems hacker.
A crazy builder in the best possible way — allergic to slow execution and obsessed with shipping.
LinkedInWho should partner
For Institutes
For institutes that want to be known for serious applied AI work — not generic academic exercises.
- Strong CS / AI / DS departments
- Motivated faculty
- High-agency students
- Interest in industry collaboration
- Willingness to work on real-world ambiguity
For Faculty
For faculty who want to expose students to real AI systems and research with practical consequences.
- NLP
- Machine learning
- AI evaluation
- Speech systems
- Causal inference
- Human-AI interaction
- Trustworthy AI
For Students
For students who want to build, not just submit.
- Technically sharp
- Fast learners
- Curious about AI systems
- Comfortable with ambiguity
- Interested in product and research
- Hungry to work on real problems
Small cohort. High signal. Serious work.
This is not a mass certification program. Not a weekend hackathon. Not a generic industry project.
- 4–6 selected students
- 1 faculty mentor
- Weekly working sessions
- A defined project track
- Final demo and research report
We are looking for students and institutes that want to work on difficult, meaningful, real-world AI problems.
What gets built by the end
The goal is not a slide deck. The goal is a working foundation for outcome-grounded enterprise AI.
- 01 Outcome taxonomy for selected enterprise workflows
- 02 Annotated sample dataset
- 03 Hallucination and workflow-deviation detection prototype
- 04 Outcome-readiness scoring layer
- 05 Call-level QA intelligence framework
- 06 Demo dashboard / reporting interface
- 07 Final technical report
- 08 Improvement recommendations for AI agents
- 09 Roadmap for future research and production development
What it feels like to be part of the sprint
You will work like a small AI research and product team.
- 01 Start with an ambiguous, high-value problem.
- 02 Break it down.
- 03 Analyze real interactions.
- 04 Define what success means.
- 05 Build an evaluator.
- 06 Test it.
- 07 Present what you learned.
Along the way, you will see how enterprise AI is actually built — with constraints, messy data, edge cases, customer pressure, compliance concerns, and the need to create measurable value.
This is where AI leaves the lab and enters the field.
Not a typical college AI project
Frequently asked questions
Partner With Us
Help build India's first generation of outcome-grounded enterprise AI systems.
If your institute wants to work on a frontier AI problem with real enterprise context, we'd love to explore a collaboration.
The ask is simple: nominate interest, and we'll schedule a short discussion to evaluate fit.
- · Selected institutes only
- · 10-week sprint
- · Applied AI research collaboration