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SCIKIQ · Account Brief

Motilal Oswal Asset Management Company Limited — 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 Motilal Oswal Asset Management Company Limited, why now

Account thesis

Motilal Oswal Asset Management Company (MOAMC) is at a strategic inflection point, seeking to deepen mutual fund penetration, scale AUM, and expand into new asset classes like pensions, all while maintaining industry-leading compliance and investor trust. With rapid AUM growth (₹1.3 lakh crore+), new regulatory approvals (PFRDA pension fund sponsor), and competitive churn (key leader exits, new ventures), MOAMC's leadership is under pressure to unify fragmented data across mutual funds, PMS, and new pension lines to drive differentiated investment insights, operational efficiency, and regulatory agility. SCIKIQ can help MOAMC unlock enterprise-wide intelligence, accelerate new product launches, and automate compliance by contextualizing siloed data into AI-ready, actionable products—directly supporting their growth, profitability, and risk management priorities.

Why SCIKIQ for Motilal Oswal Asset Management Company Limited — the proof that lands
  • 85% faster data integration—critical for onboarding new fund types and regulatory reporting across MF, PMS, and NPS lines.
  • 90% lower IT integration cost—enabling rapid launch of new investment products and digital investor experiences.
  • 5x faster time-to-market for data products—supporting differentiated research-led offerings and investor analytics.
  • 95% fewer compliance violations—vital for SEBI/PFRDA regulatory scrutiny and investor trust.
Maturity

MOAMC is at Stage 2 (Enterprise 360), with strong reporting but limited cross-LOB context and automation.

From silos and dashboards to autonomous execution. Our read of Motilal Oswal Asset Management Company Limited's current stage is highlighted.

Stage 1

Reporting & Silos

Fragmented reporting across mutual fund, PMS, and new pension business lines; manual data pulls and static dashboards.

  • LOB-specific MIS, Excel-based reconciliations
  • Manual regulatory reporting cycles
  • Limited investor 360 visibility
Likely today
Stage 2

Enterprise 360

Unified data views across funds, portfolios, clients, and compliance; foundation for business 360 and regulatory agility.

  • Centralized data lake/warehouse projects
  • Cross-fund AUM and investor analytics
  • Improved but still reactive compliance monitoring
Stage 3

Reasoning: Graph + Copilot

Relationship-aware knowledge graphs, semantic search, and AI copilots for root-cause, investor, and risk insights.

  • Pilot LLM/AI tools for research or investor queries
  • Some automation in compliance and risk review
  • Early-stage graph modeling of portfolios
Stage 4

Autonomous: Agents

AI agents proactively surface risks, automate compliance actions, and drive investor engagement or operational fixes.

  • Closed-loop compliance workflows
  • Agent-driven investor communications
  • Automated rebalancing or risk mitigation
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
Cares about: Unified, governed data to enable analytics, regulatory reporting, and new product launches.
“SCIKIQ accelerates data unification and AI-readiness across all LOBs, reducing IT and compliance burden.”
MD & CEOchampion
Prateek Agrawal
Cares about: Growth, investor trust, and speed to market for new investment solutions.
“SCIKIQ enables faster, insight-led product launches and strengthens MOAMC's competitive edge.”
Head of Compliance & Riskuser/blocker
Cares about: SEBI/PFRDA compliance, real-time monitoring, and auditability.
“SCIKIQ provides explainable, automated compliance controls with full lineage and traceability.”
Head of Product / Mutual Fundsuser
Cares about: Rapid product innovation, investor analytics, and differentiated offerings.
“SCIKIQ's data products and GenAI studio unlock new investor insights and research capabilities.”
CFOeconomic buyer
Cares about: Cost-to-income, operational efficiency, and risk mitigation.
“SCIKIQ reduces integration and compliance costs while improving cash conversion and control.”
CISOblocker
Cares about: Data security, access controls, and regulatory data privacy.
“SCIKIQ delivers enterprise-grade security, access management, and audit trails.”
Discovery

Questions to ask in the meeting

Data & context

  • How are data silos impacting investor 360, risk, and compliance today?
  • What are the biggest pain points in integrating new fund types or regulatory data?
  • Which LOBs have the most fragmented data and manual processes?

Growth & product innovation

  • How quickly can MOAMC launch new funds or portfolio strategies today?
  • Where are delays or data bottlenecks slowing new product go-to-market?
  • What investor analytics or insights are most in demand from the business?

Compliance & risk

  • What are the most frequent compliance or audit issues flagged by SEBI/PFRDA?
  • How is regulatory reporting managed across mutual funds, PMS, and pension lines?
  • Where could automation reduce manual compliance effort or risk?

Operational efficiency & cost

  • What is the current cost-to-income ratio for key lines of business?
  • Where are manual data-prep or reconciliation costs highest?
  • How are IT integration costs trending as new products are added?

Competitive edge & AI

  • How is MOAMC leveraging AI/ML today for research, investor engagement, or compliance?
  • What are competitors doing with data/AI that MOAMC wants to emulate or surpass?
  • Where could autonomous agents or copilots drive the most value?
Competitive landscape

MOAMC faces a crowded field of data/AI platforms—SCIKIQ's edge is contextualization, speed, and compliance.

MOAMC will evaluate proven data platforms and AI tools used by leading asset managers—ranging from hyperscaler data fabrics and graph/semantic specialists to homegrown solutions. SCIKIQ's differentiator is rapid, no-code unification of asset management data into AI-ready, explainable products with built-in compliance, outpacing both point tools and generic fabrics.

Palantir Foundry
Enterprise data integration and analytics, strong in modeling and compliance.
SCIKIQ edge: SCIKIQ is faster to deploy, no-code, and purpose-built for asset management 360 and agentic AI.
Databricks
Lakehouse platform for big data and ML, popular for custom analytics.
SCIKIQ edge: SCIKIQ offers business-contextualized data products and governance out-of-the-box, not just raw infrastructure.
Microsoft Fabric
Integrated analytics and BI suite, strong in reporting and Microsoft ecosystem.
SCIKIQ edge: SCIKIQ delivers graph-based reasoning, GenAI copilots, and agent automation tailored to asset management.
Build-it-yourself (internal IT)
Custom data lakes/warehouses and point integrations.
SCIKIQ edge: SCIKIQ reduces integration time/cost by 85–90%, with proven compliance and explainability.
Niche graph/semantic vendors
Point solutions for knowledge graphs or semantic search.
SCIKIQ edge: SCIKIQ unifies ingestion, graph, GenAI, and agents in a single platform—no integration gaps.
POC requirements

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

Download checklist (Excel)

A POC proves ScikIQ's feasibility against Motilal Oswal Asset Management Company Limited'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 Motilal Oswal Asset Management Company Limited'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 Motilal Oswal Asset Management Company Limited'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 Motilal Oswal Asset Management Company Limited'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 Motilal Oswal Asset Management Company Limited'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 Motilal Oswal Asset Management Company Limited, 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.