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SCIKIQ · Account Brief

GMR Group — account brief & discovery

The working notes behind the pitch: where they are on the maturity curve, who's in the buying group, the questions to ask, and how we're positioned against the alternatives.

Internal · for the account team
The thesis

Why GMR Group, why now

Account thesis

GMR Group is India’s leading private airport and infrastructure operator, with strategic priorities around digital transformation, operational excellence, and risk management across its airports, energy, and transportation verticals. With a multi-billion dollar asset base and operations spanning India and Southeast Asia, GMR is investing heavily in AI, IoT, and advanced analytics to drive efficiency, safety, and customer experience—evidenced by recent digital twin and AI-powered initiatives at its flagship airports. However, data remains fragmented across business units, legacy systems, and partners, impeding the group’s ambition to deliver real-time, enterprise-wide intelligence and rapid monetization of data assets. SCIKIQ is uniquely positioned to unify GMR’s siloed data, contextualize it for business and operational leaders, and accelerate the shift from reactive reporting to predictive, AI-driven decisioning—directly supporting GMR’s vision of sustainable, technology-led infrastructure leadership.

Why SCIKIQ for GMR Group — the proof that lands
  • 85% faster data integration—critical for GMR’s multi-entity, multi-system landscape across airports, energy, and transport.
  • 5x faster time-to-market for new data products—enabling rapid rollout of digital services and operational dashboards for airport, energy, and logistics units.
  • 90% lower IT integration cost—directly supporting GMR’s focus on cost-efficient digital transformation and operational excellence.
  • 95% fewer compliance violations—vital for a regulated, multi-jurisdictional operator handling sensitive passenger, cargo, and energy data.
Maturity

GMR is progressing from siloed reporting towards an integrated, AI-powered operations and business intelligence platform, but remains largely at the 'Enterprise 360' stage.

From silos and dashboards to autonomous execution. Our read of GMR Group's current stage is highlighted.

Stage 1

Reporting & Silos

Fragmented data sources, manual reporting, limited cross-LOB visibility.

  • Business units maintain separate dashboards and data marts.
  • Delayed incident/root-cause analysis across airport, energy, and transport operations.
  • Manual compliance and risk reporting.
Likely today
Stage 2

Enterprise 360

Unified data ingestion and basic cross-entity analytics; early moves to centralized BI and digital twins.

  • Central data lake/warehouse projects underway.
  • Pilots of digital twin and AI analytics at key airports (e.g., Delhi, Hyderabad).
  • Initial data governance and metadata management initiatives.
Stage 3

Reasoning: Graph + Copilot

Contextualized, relationship-aware data via knowledge graphs; semantic/LLM copilots for business and ops users.

  • Interest in graph-based analytics for asset, passenger, and incident data.
  • Early-stage conversational AI pilots for ops and customer service.
  • Desire for root-cause analysis and predictive insights, not just reporting.
Stage 4

Autonomous: Agents

AI agents autonomously detect, explain, and resolve incidents—closing the loop from insight to action.

  • Automated incident response in airport/energy ops.
  • Closed-loop optimization of passenger flow, asset maintenance, and vendor SLAs.
  • AI-driven, real-time business activation (e.g., dynamic pricing, resource allocation).
Stakeholder map

Who's in the room — and the line that lands

The buying group for an enterprise-AI platform, with each persona's concern and the message that resonates.

CIO / CDOeconomic buyer
Mrinal Mayank
Cares about: Unified, governed data platform to enable digital transformation, reduce integration cost, and accelerate analytics delivery.
“SCIKIQ delivers an AI-first, no-code data fabric that unifies your siloed data, slashes integration costs, and accelerates time-to-value for every business and ops leader.”
Head of AI & Digitalchampion
Cares about: Rapid deployment of AI/ML pilots, digital twins, and analytics at scale without dependency on IT bottlenecks.
“SCIKIQ’s Data Product Factory and GenAI Studio let you activate AI-ready data products and copilots in weeks, not quarters.”
COO / Head of Operations (Airports/Energy)user
Cares about: Real-time visibility into asset health, incident root-cause, and operational KPIs across airports and plants.
“Get a unified, context-rich control tower—see what’s happening, why, and let AI recommend or trigger fixes.”
Chief Risk & Compliance Officerblocker
Cares about: Data lineage, regulatory compliance, and minimizing operational risk across regulated assets.
“SCIKIQ’s end-to-end lineage, access control, and compliance monitoring ensure you meet all regulatory and audit requirements.”
CFOeconomic buyer
Cares about: Cost control, ROI on digital investments, and monetization of data assets.
“SCIKIQ delivers 60% lower TCO and unlocks new revenue streams from data products—fast payback, measurable impact.”
Business Unit Heads (Airports, Energy, Transport)user/champion
Cares about: Faster, more actionable insights to drive business performance and customer experience.
“SCIKIQ contextualizes your business data so you can act faster, serve better, and outpace competitors.”
CISO / Head of IT Securityblocker
Cares about: Data security, access governance, and risk of breaches across critical infrastructure.
“SCIKIQ’s granular access controls and security model keep sensitive operational and passenger data protected.”
Discovery

Questions to ask in the meeting

Data & context

  • Where are your most critical data silos across airports, energy, and transport?
  • How do you currently contextualize asset, incident, and customer data for decision-making?
  • What are the biggest gaps in real-time, cross-entity analytics today?

AI & digital initiatives

  • Which digital twin, AI, or automation pilots have shown the most promise?
  • What are the blockers to scaling AI/ML from pilots to enterprise-wide adoption?
  • How do you envision leveraging GenAI or copilots for ops and business users?

Operational pain & opportunity

  • Can you share a recent incident where siloed data delayed response or root-cause analysis?
  • Where do you see the most value in linking asset, vendor, and customer data?
  • How do you currently track and optimize vendor SLAs and asset uptime?

Governance & compliance

  • What are your top data governance and compliance challenges across jurisdictions?
  • How do you ensure lineage and auditability for regulatory and risk reporting?
  • Where do you see the biggest compliance risks as you scale digital operations?

Monetization & value

  • What new data-driven products or services are you exploring for airports or energy customers?
  • How do you measure ROI on data and analytics investments today?
  • Where could faster data integration or productization unlock new revenue streams?
Competitive landscape

GMR faces a crowded landscape of data platform and AI vendors, but SCIKIQ’s contextual, AI-first approach is uniquely fit for infrastructure operators.

GMR will benchmark SCIKIQ against global platforms like Palantir Foundry and Databricks, cloud-native fabrics (Microsoft Fabric), and point BI/graph tools, as well as the option to build in-house. SCIKIQ’s edge is its no-code, AI-ready data product factory, rapid integration, and deep contextualization for complex, asset-heavy, regulated environments.

Palantir Foundry
Strong in data integration and operational analytics for infrastructure, but costly, code-heavy, and less flexible for rapid data productization.
SCIKIQ edge: SCIKIQ is no-code, faster to deploy, and natively supports GenAI copilots and agent automation.
Databricks
Leading data lakehouse for analytics and ML; requires significant engineering and customization for contextual, business-ready data products.
SCIKIQ edge: SCIKIQ provides out-of-the-box data productization, graph modeling, and business activation without heavy engineering.
Microsoft Fabric
Cloud-native data fabric with strong integration to Azure ecosystem; best for Microsoft-centric, less complex environments.
SCIKIQ edge: SCIKIQ is cloud-agnostic, supports hybrid/multi-cloud, and is tailored for regulated, asset-intensive industries.
Build-it-yourself (internal IT/consulting)
Custom fit, but slow, expensive, and high risk of failure—especially for cross-LOB, AI-driven use cases.
SCIKIQ edge: SCIKIQ delivers proven, referenceable outcomes in <6 months with 60% lower TCO and 5x faster time-to-market.
Niche graph/semantic vendors
Strong graph/semantic tech, but lack end-to-end ingestion, governance, and AI productization at enterprise scale.
SCIKIQ edge: SCIKIQ unifies ingestion, graph, governance, GenAI, and agent automation in a single, business-ready platform.
POC requirements

How we'd prove it — the ScikIQ POC, layer by layer

Download checklist (Excel)

A POC proves ScikIQ's feasibility against GMR Group's data needs — installed, configured and tested inside your environment to validate a set of business, functional, technical and operational goals. Every POC covers three things: technical & functional validation, deployment sizing, and ROI.

Problem statement & financial driver — revenue or cost; regulatory or discretionary spend.
Key success criteria (KPIs) and decision criteria — technical, economic and benchmarking.
Risks — organizational/political, technical, commercial — and the named economic buyer.
01

Enterprise 360

ScikIQ Data Integration · Connect

Connect GMR Group's structured & unstructured sources and build the unified Business 360 with no-code pipelines — cutting data-to-action from months to days.

Validate in POC
Scope inputs needed
Success criteria
Applicable SKUs
SCIDI001 · Document (Mongo DB)SCIDI002 · Real-time / StreamingSCIDI003 · BatchSCIDI004 · SAPSCIDI005 · Log-based CDCSCIDI006 · API
ScikIQ POC Guide — Data Integration POC
02

Knowledge Graph

ScikIQ Data Governance · Knowledge Graph & Lineage

Model GMR Group's entities and relationships into a living knowledge graph with end-to-end lineage, cataloguing and quality — so AI can traverse cause → effect.

Validate in POC
Scope inputs needed
Success criteria
Applicable SKUs
SCIDGI001 · Data CatalogSCIDG002 · Metadata DiscoverySCIDGI003 · Asset Approval & Search (Elasticsearch)SCIDGI004 · Knowledge Graphs (Neo4j) & Data LineageSCIDGI005 · Data Quality & Data Observatory
ScikIQ POC Guide — Data Governance POC
03

AI Copilot

ScikIQ GenAI Studio · Talk to your data

Ground a conversational copilot on GMR Group's knowledge graph + semantic layer — plain-language operational, commercial and risk queries with explainable, auditable answers.

Validate in POC
Scope inputs needed
Success criteria
Applicable SKUs
SCIAI001 · GenAI Studio — Conversational CopilotSCIAI002 · Semantic Search (structured + unstructured)SCIAI003 · Grounding & Explainability (graph-RAG)SCIAI004 · Guardrails & Governance for GenAI
Authored to the POC Guide structure (step not in the source doc)
04

Agent Factory

ScikIQ Agent Factory · No-code autonomous agents

Build no-code agents that act on GMR Group's live context — detect, reason and close the loop with a real transaction in the source system, under human-in-the-loop guardrails.

Validate in POC
Scope inputs needed
Success criteria
Applicable SKUs
SCIAG001 · No-code Agent BuilderSCIAG002 · Triggers & OrchestrationSCIAG003 · Closed-loop Connectors (IT/OT write-back)SCIAG004 · Agent Governance, Approvals & Audit
Authored to the POC Guide structure (step not in the source doc)
POC readiness checklist
Kick-off
Data readiness
IT readiness
Testing readiness
Battle card

Objection handling — across all four layers

Field-ready objection handling for GMR Group, layer by layer — grounded in the SCIKIQ Battle Cards. For each: the objection you'll hear, the response that wins it, the proof, and who you're really competing with.

Buyer: C-suite (CIO, CTO, CFO) and leaders in data, compliance and innovation.
01

Enterprise 360

Data Hub & Lakehouse · Innovation at speed
“We're happy with our current data stack and tools.”
We complement and enhance what you have — no rip-and-replace. One no-code platform unifies all data across cloud/hybrid and adds AutoML & GenAI value your current stack can't reach.
“We already have a data lake / warehouse.”
Separate lakes and warehouses raise cost and slow real-time analytics. SCIKIQ unifies them and builds the Business 360 on top — no data movement.
“Our SI / in-house team can build it.”
That's years of pipelines and heavy services spend. SCIKIQ delivers strategy-to-execution on one platform — up to 80% cost savings, <6 months to value, 200+ no-code connectors.
“Another integration project that stalls in IT.”
No-code pipelines move integration to the business team; data-to-action drops from months to days — proven on your data in the POC.
200+ connectors · no data movementUp to 80% cost savingsForrester Top-34 augmented-BINo-code · <6 months to value
Real competition: Big-4 & boutique data firms (strong on strategy, light on execution), global / local SIs (vendor-tied, generalized, services-heavy), plus Informatica/Fivetran & build-it-yourself. Wedge: one no-code platform, strategy-to-execution — a Business 360, not just pipes.
02

Knowledge Graph

Data Governance · Governance on autopilot
“We already have a data-governance solution.”
We enhance rather than replace — a no-code, metadata-first, GenAI-integrated layer that boosts your governance and builds the knowledge graph + lineage on top.
“A BI dashboard already shows what's happening.”
Dashboards answer what; only a graph answers why. Typed relationships + column-level lineage let AI traverse cause → effect across silos.
“Can it scale to our complex cloud / hybrid data?”
A modular, flexible architecture adapts to growing volumes and new sources across complex cloud/hybrid stacks, continuously updated with the latest tech.
“How do we trust the relationships?”
Every edge is lineage-traced and governed; GenAI authors the rules (manual rule creation is ~70% slower) — fewer errors, lower cost to maintain.
Graph + lineage pre-built (Neo4j)Metadata-first · GenAI rule authoringForrester DG challenger~70% faster rule creation
Real competition: Big-4 & boutique data firms (strong on strategy, light on execution), global / local SIs (vendor-tied, generalized, services-heavy), plus Palantir Foundry & niche graph vendors. Wedge: governed, metadata-first graph + lineage — no-code and faster to value.
03

AI Copilot

Gen AI · Talk to your data
“Do we really need a GenAI platform? We're good today.”
Chat-based access puts data in everyone's hands and lifts data literacy org-wide. Grounded on your graph, answers are explainable — not generic chatbot guesses.
“We'll just use ChatGPT / a generic copilot.”
Ungrounded models hallucinate on enterprise data. Ours is grounded on your graph + semantic layer with citations and lineage; RBAC honoured in every answer.
“GenAI is still maturing — invest now?”
Every tech matures; our engineers keep the platform current so it never goes stale. Start with one department, prove ROI, then roll out.
“LLMs can't be trusted with our numbers / security.”
Every figure cites its source and path; quality & freshness gate what it answers, and row-level security is honoured inside every answer.
Graph-grounded (no hallucination)Explainable & lineage-tracedChat access · data literacyRBAC enforced
Real competition: raw LLMs/chatbots, BI NLQ, and Big-4 & boutique data firms (strong on strategy, light on execution)' GenAI services. Wedge: graph-grounded, governed, auditable — and democratised access.
04

Agent Factory

Machine Learning & Auto ML · Automate data processes
“We already have AutoML / automation.”
Replace point automation with a holistic no-code platform — more capabilities and value, and agents that close the loop, not just score models.
“Autonomous agents are too risky in production.”
Human-in-the-loop approvals, full audit and safe-stop are built in; agents run in a sandbox first and you own the approval matrix.
“RPA already automates our workflows.”
RPA scripts brittle UI steps; agents reason on live graph context and close the loop via APIs — incident response, compliance, optimization.
“Why now / no special skills on the team?”
Begin today — automation cuts this year's spend itself: no code, no special skills, immediate results. ROI aligns future budgets.
No-code agent builderClosed-loop write-back to IT/OTApprovals · audit · safe-stopNo special skills needed
Real competition: RPA (UiPath), AutoML point tools & bespoke scripts, plus global / local SIs (vendor-tied, generalized, services-heavy). Wedge: context-aware, governed, closed-loop on one platform.
Objections you'll hear at every layer
“No budget / we don't need it right now.”
Begin with a phased pilot on one domain — ROI shows in days and aligns next year's budget. The best firms modernise every year; the competition won't wait.
“Long-term support & reliability?”
Although the platform is no-code, a dedicated support team is always available, with long-standing customer references.