SHURU.
A Submission for Higher Education Leadership

Reimagining the
academic experience
with AI in service of learning.

Capabilities, frameworks and references mapped against the five capability pillars defined by your institution.
Prepared for review by the institution
and shared via CSphere Consultants & AyeMinds Projects
June 2026
shurutech.com
01 / Why this submission
Context
An honest opening

We have shaped this deck
around your five pillars, not around our brochure.

Your brief described an institution that wants depth of domain thinking, not just engineering. The next twelve slides are organised in the exact order of your capability framework, with the evidence behind each claim.

A note on framing We are an AI-native product engineering firm with deep adjacent learning experience. Where our footprint is direct, we will show it. Where it is adjacent or absent, we will say so plainly. Both matter.
Shuru · For institution review
Confidential
02 / Pillar mapping at a glance
Snapshot
Five capability pillars · one map

Each pillar, the evidence we bring to it.

01
Case-Based Curriculum & Impact Learning
Shuru App built for Reliance, Tata, Aditya Birla & Teleperformance. Scenario-based learning tied to employment outcomes.
02
Faculty AI Copilot & Pedagogy
Active engagements with SoLeLands (Indonesia) and Innovating Teachers Academy (Switzerland). Adaptive AI faculty in production.
03
Student Learning Intelligence
Per-learner content graph, recommendation engines and adaptive AI. Validated with adult learners and now with children.
04
Research & Intelligent Knowledge Systems
Governed AI over institutional plus individual knowledge, citation-grounded. HaloDoc & a multimodal coaching app (Australia).
05
Academic Analytics & Continuous Improvement
Data-Aware, not data-driven. Provenance review, counter-metric audit and human-in-loop decision rights.
Direct work
Adjacent reference
Pillars 1, 2 and 5 are anchored in direct Shuru work. Pillars 3 and 4 lean on architecturally identical adjacent engagements.
Pillar mapping
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03 / Pillar 01
Case-Based Curriculum
Pillar 01

Curriculum that learns from real outcomes, not classroom proxies.

Between 2021 and 2023, Shuru built and operated its own learning product. Scenario-based, case-driven micro-learning, integrated into an employer interview pipeline. The impact loop was unambiguous: did the learner get hired.

Adult learner pedagogy
Industry-integrated
Outcome-based
Reliance Retail
Tata Group
Aditya Birla
Teleperformance

Frameworks we bring

  • Scenario-First Curriculum Design. Start with the workplace artefact, reverse-engineer the curriculum from it.
  • Closed-Loop Learning Effectiveness. Every unit carries an external validation signal, not a quiz score.
  • Practice-to-Performance ratio. A pedagogical KPI that engineering institutions tend to invert. We help correct it.
Why this matters

For an engineering institution preparing graduates for industry, the same chassis carries case-based pedagogy plus an industry signal. We have already built it once.

Pillar 01 · Case-Based Curriculum
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04 / Pillar 02
Faculty AI Copilot & Pedagogy
Pillar 02

An AI faculty that respects pedagogy, not just productivity.

Two engagements that, taken together, give us the clearest evidence in this pillar. One in production for children, one for the global teacher community.

SoLeLands · Indonesia · 2026

An adaptive AI faculty inside an immersive role-playing learning experience. Responses align with the child's interest, age and mental maturity, without drifting from the curriculum designer's intent.

Innovating Teachers Academy · Switzerland

We are the technology partner behind a multilingual platform for educator professional development. Self-paced courses, masterclasses, in-person training, and personalised coaching.

InTA's downstream client list

When the institution evaluates depth of pedagogical thinking, this is the room our partner is invited into.

UNESCO IBE
European Centre for Modern Languages
Government of Flanders
Federation Wallonie-Bruxelles
National Pedagogical Institute, Czech Republic
HEP Lausanne
HEP Fribourg
International School of Geneva
DIP Geneva
Recognised frameworks
CLIL Pluriliteracies Assessment for Learning Concept-Based Learning Culture of Thinking Co-teaching
Pillar 02 · Faculty AI Copilot
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05 / Pillar 03
Student Learning Intelligence
Pillar 03

Every student carries a different learning trajectory. The system should know it.

We hold a per-learner content graph from Shuru App and an adaptive AI pattern from SoLeLands. Together they form the spine of a student learning intelligence framework.

An honest gap, surfaced We have not yet implemented a full SIS-grade student success early-warning system. We are confident we can build one, and can quote a comparable architecture from Pickup Coffee real-time alerting on request.
Adaptive pathways with explicit branch points, observable from the faculty copilot.
Pillar 03 · Student Learning Intelligence
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06 / Pillar 04
Research & Knowledge Systems
Pillar 04

A research copilot must cite, not just generate.

Two engagements show how we approach governed AI over heterogeneous, sensitive knowledge sources, with citations and methodology fidelity built in.

HaloDoc · Indonesia · Regulated industry

Two-layer governed AI over institutional policy plus individual user policy data. Source-cited answers. Hallucination scoring. Human escalation for high-risk queries.

Leadership Coaching Firm · Australia

Multimodal AI app that delivers personalised coaching grounded in the firm's own methodology. Knowledge graph over the case library. Memory across sessions.

Pillar 04 · Research & Knowledge Systems
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07 / Pillar 05
Academic Analytics
Pillar 05

Data informs.
Humans, with context, decide.

Academic analytics can get weaponised against students, faculty and departments. Our framing keeps the human accountable for any consequential call, and forces the data to declare its limits up front.

Our position
Data-Aware, not data-driven.
Every metric carries its provenance, its bias risk, and its decision-rights tag, before it is allowed onto a dashboard.

Three frameworks we apply

  • Provenance review. Before any analytic surface ships, we map the upstream collection process and document what is missing or biased.
  • Counter-metric audit. For every metric (completion rate), we surface its counter-metric (drop-out reason mix). No dashboard tells a one-sided story.
  • Human-in-loop decision rights. Each high-stakes metric carries a tag: who can act on it, who must be informed, who must not.
On NAAC, NBA & OBE We have not directly implemented NAAC accreditation analytics. We will partner with an accreditation subject matter expert for rubric design, and own the build-out.
Pillar 05 · Academic Analytics
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08 / Reference architecture
Technology
Built for cost, control, and DPDP compliance

A layered AI architecture, sized for your institution.

Foundation model
Open-source LLMs (Llama 3, Mistral, Qwen) self-hosted on AWS Bedrock. Avoids the multi-million-dollar custom-train cost, sidesteps the narrowness of SLM-only paths.
Knowledge layer (RAG)
Retrieval-Augmented Generation over your catalogues, syllabi, faculty resources, electives database. Source-cited responses, fully auditable.
Domain SLMs (selective)
Small fine-tuned models for narrow high-frequency tasks: rubric grading, plagiarism patterns, learning-style classification. Cost-efficient at scale.
Orchestration & guardrails
Prompt templates, response validators, refusal logic and hallucination scoring. Same guardrail pattern we shipped at Realfast.
Integration spine
APIs to your existing LMS (Moodle, Canvas, custom), Student Information System, HR systems and authentication (SSO, SAML, OAuth).
Governance & compliance
Token-level audit logs, factuality & bias evaluations, continuous red-team prompts, role-based access. DPDP Act, UGC data norms, child-safety policies for student-facing surfaces.
Adoption & behaviour design
This is where most higher-ed AI rollouts die. Faculty co-shaping cohorts from week 1, L&D collateral built with AyeMinds. Adoption is engineered, not assumed.
Recommendation
Do not train a custom LLM. Do not adopt a pure SLM-only path. The hybrid above is the sweet spot for cost, control and quality at your institution's scale.
AI Architecture
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09 / Engagement
How we partner
Three engagement models · stage-aware

We adapt our shape to where the partnership is, not the other way around.

MODEL 01
End-to-end product engineering ownership

A complete, self-sufficient squad — leadership, product management, engineers, QA and data — operates as your engineering arm. Shuru owns delivery. You own domain direction and vision.

Reference: Pickup Coffee, 20+ person team, full engineering operations owned by Shuru.
MODEL 02
Embedded team extension

Shuru engineers embed full-time into your team. They follow your processes, your sprint cadence, your leadership. Built for scale, when you already have engineering leadership in place.

Reference: Equiti, 25+ engineers across React, Angular, Flutter, .NET, SAP and platform.
MODEL 03
Keystone project delivery

Fixed-scope build against a locked specification. Shuru owns execution against the agreed milestones and timeline. Pricing tied to deliverables, not time spent.

Payment: 30% at kickoff · 30% at design close · 40% at UAT delivery.
Suggested path for the institution: a 2-3 week paid Discovery first, then choose a model.
This is the lowest-risk way to evaluate fit while making real progress on the solution canvas.
Engagement Models
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10 / How we work
Operating model
Project Reboot · since early 2024

An AI-native engineering practice, two years ahead of the curve.

Squads

Dedicated, not fragmented

One person, one project. No morning-something-else. Each squad is configured to the problem, not force-fitted from a template. Daily live standups, weekly IPM, bi-weekly retros, weekly demos.

1.5 – 4x

AI-native productivity

Every engineer has been building with Claude Code, Cursor and AI coding tools as a daily workflow since 2024. PRD generation, code scaffolding, 80%+ test coverage baseline, doc generation, review assistance.

Remote, present

A permanent virtual office

All engineers work from India, anchored in a Gather.town always-on workspace. Walk up to a virtual desk, talk in real time. The presence of co-location without the cost of it.

Data confidentiality
NDA-bound at every engagement. IP and data secure.
Compliance alignment
Engineers operate under your security framework, not ours.
Access governance
Role-based access. Revoked immediately on changes.
Frameworks
SOC 2, ISO 27001 Certified.
How We Work
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11 / By the numbers
Scale
Founded 2021 · ex-Gojek leadership

Where we stand today.

175+
AI-native engineers, all building with AI tools daily since early 2024.
25+
Active projects across US, SEA, UAE, UK, Europe and Australia.
100+
Products built across fintech, healthtech, e-commerce, climate tech and more.
3×
Year-on-year growth since inception, driven by client retention and referrals.
Four offices
Dubai · Singapore · Canada · India
Founded by ex-Gojek leaders who built and scaled payments, food delivery and consumer platforms to hundreds of millions of transactions across Southeast Asia. The same urgency, quality and accountability applied to your project.
By the Numbers
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12 / Honest frame
Said openly
Trust before transaction

What we have, and what we do not - said openly.

What we have
  • Practical frameworks battle-tested across 50+ products.
  • Shuru-built AI pedagogy in production for SoLeLands.
  • Working architecture for governed, citation-grounded AI (HaloDoc).
  • Active partnership with Innovating Teachers Academy and its UNESCO, ECML and Ministries of Education client base.
  • A Data-Aware Decision framework with provenance and bias checks built in.
What we do not have
  • Peer-reviewed academic publications on pedagogy. We are engineers, not learning scientists.
  • A multi-year university platform delivered end-to-end. We have done every component, in adjacent settings.
  • Direct NAAC, NBA or UGC accreditation analytics history. We will partner with a domain expert and own the build.
  • Existing relationships with Indian universities. We are choosing this engagement as our first deep university partnership.
  • A productised analytics platform with NAAC modules out of the box. We build to your specifics rather than ship a generic SaaS.
Honest Frame
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13 / Next steps
Proposed path
A short, low-commitment path forward

Three steps. No surprises.

Step 01 · This week
Joint working session
A 30-minute call between the institution sponsor, Priti (AyeMinds), Manish (CSphere) and Shuru's founding team. We walk through this deck, surface concerns, agree the shape of the next conversation.
Step 02 · Before client call
Tailored walkthroughs
We record and share two short walkthroughs tailored to your context: SoLeLands adaptive AI faculty, and HaloDoc governed knowledge architecture. Each ~10 minutes.
Step 03 · Discovery sprint
2 to 3 week paid Discovery
A focused Solution Shaping engagement that ends in a one-page solution canvas, prioritised use-case map, AI architecture blueprint and a target-state cost envelope. Lowest-risk way to evaluate fit.
For the institution call, a founder and a senior leader will be on the line.
Discovery: 2-3 weeks
Pilot MVP: 8-12 weeks
Full Build: 6-9 months
Next Steps
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Thank you for the consideration

The right partner isn't the one with the
longest list of universities.
It is the one your institution will trust in the room.

We will earn that trust on the call.

Kshitij Bhardwaj
COO · Engagement Anchor
Prabhakar Kaushik
SVP · Growth & Marketing
Dubai · Singapore · Canada · India
Confidential · For institutional review · June 2026
SHURU.