The Intelligence OS for
Modern Enterprises
Building Operating Intelligence for Enterprises
Impactree bridges physical data and financial intelligence to power Enterprise Operational Intelligence.
0-25%
Cost/Unit Reduction
0-50%
Downtime Reduction
0-10x
Faster Risk Reduction
0+
Enterprise Relationships
The more data enterprises collect,
the worse their decisions get.
Enterprises are spending $300B on AI. No system delivers decisions.
CEOs
Cannot link operations to financial outcomes — growth bottlenecks, costs, and risks remain invisible until they hit the P&L
Investors / Lenders
Cannot price non-financial, physical and transitional risk into portfolios — exposure stays hidden until it turns into losses
Board of Directors
Cannot connect performance to strategy — fragmented systems leave decision-making reactive and blind
Lost annually due to poor data and fragmented systems
of Enterprise AI Initiatives fail — root cause: fragmented, siloed, legacy data
of enterprise data is ever used for actual decisions
Enterprises are spending $300B on AI. No system delivers decisions.
Three reasons generic AI cannot solve this
The enterprise intelligence gap isn’t a technology problem. It’s a context problem.
The Data Failure
AI fails because enterprise data is fragmented
ERP, IoT, SCADA, and financial systems were built to store transactions — not to share intelligence. AI doesn’t fix broken data. It amplifies it.
“Only 26% of enterprise data is used for decisions.”
\u2014 IBM, 2025
The Generic AI Failure
Generic LLM wrappers don’t understand industry context
A generic model cannot reason about the relationship between a refinery’s asset degradation, its energy costs, and its credit exposure.
“Generic AI was trained on the internet — not on your plant floor or risk ledger.”
\u2014 Rubicr Research
The Context Gap
Industry intelligence must be built — not scraped
The data relationships inside a chemical company are structurally different from a bank or auto manufacturer. This context must be encoded from operational ground truth.
“Context is the moat. And context takes years to build.”
\u2014 Rubicr
The Current Enterprise Approach
Enterprises must access a universe of managed services to build custom solutions
Custom Services
Infosys, Accenture, Capgemini
Custom services deliver tailored solutions, but remain project-based and lack standardization and scalability
Fragmented Tools
Databricks, LatentView, Atlan
Fragmented tools solve specific problems, but operate in silos and fail to deliver unified, end-to-end intelligence
Just as ERP replaced manual processes with standardised systems, Enterprise intelligence must now evolve from services to products.
Introducing Rubicr
Three stages. One continuous intelligence loop. Built on 7 years of proprietary domain reasoning.
Every data source your enterprise already has
- PDF, Excel, Web Scraping
- System Entry, Database
- Voice, Vision, Video
- GeoSpatial, IoT, SCADA
- ERP, CRM, Financial Systems
Domain AI — Not a generic wrapper
- Data completion & enrichment agents
- Autonomous anomaly & validation
- Predictive models (risk, performance)
- Industry-specific reasoning
- Decision-support / Scenario simulation
Actions inside your systems — not reports
- Decision interpretation
- ERP / CRM integration
- Module-wise risk alerts
- Workflow optimization
- Automated triggers & actions
Data Network Effects
The more enterprises use Rubicr, the sharper the industry intelligence becomes — data network effects compound with every deployment.
The BLM Core — Business Language Model
Rubicr Intelligence Engines are not LLM wrappers. They are built to improve over LLMs in Reasoning and Governance.
Industry Context Modeller
Sector-specific structures — assets, supply chains, capital flow dependencies
Constraint Engine
Applies financial, regulatory, and operational constraints to ensure decision validity
Intangible Variable Maps
Non-linear drivers like risk exposure, environmental factors, and operational constraints
Cross System Orchestration
Coordinates intelligence across ERP, IoT, supply chain, and external data ecosystems
Traction that speaks for itself
7 years of proprietary models, data, and enterprise deployments. Now in market as Rubicr.
Enterprise Relationships
Across 7 industries
Industries Served
Chemicals, Energy, BFSI, Auto
Operational Data Access
No new entrant can replicate
EBITDA Margin
Consistent profitability
Industry Use Cases
Sustainability reporting and decarbonization
Companies use climate forecasts to anticipate supply disruptions and stabilise procurement costs.
Non-Financial portfolio risk mapping
Banks integrate geographic risk data to identify vulnerable assets and price climate intelligence into lending.
Safety & Productivity Signals
Manufacturers analyze environmental factors to identify unsafe workspaces and reduce production downtime.
Performance & Emissions Modeling
Operators combine telemetry and emissions models to reduce energy costs and align with sustainability constraints.
Case Study · Industrial Enterprise Client
The Challenge
Higher Opex than Benchmarks
Legacy scheduling based on 30-year-old data drove excessive energy and repair spend
Hour Work Weeks
Made it difficult to attract and retain local talent
Fragmented Data Silos
Manual reporting and reactive maintenance led to unnecessary downtime
The Impact
Annual Savings — Direct reduction in power costs and maintenance expenses
Reduction in O&M Time — 152,971 annual man-hours saved through optimized workflows
Why this cannot be replicated
Platform licenses compound, services accelerate. Margin improves as platform mix increases.
Industry context takes years to encode
Rubicr's reasoning engine encodes the financial, operational, and regulatory logic specific to each industry. This is not learned from public data — it is built from operational ground truth, one sector at a time.
Data network effects compound with scale
Every enterprise deployment adds benchmarks, anomaly patterns, and performance norms to the platform. The more industries Rubicr serves, the more precisely it reasons for each one.
Workflow integration creates switching costs
Rubicr triggers actions inside ERP, CRM, and operational systems — not reports on a screen. Once enterprise workflows depend on Rubicr decisions, the platform becomes infrastructure.
Annual Recurring License (ARR)
Annual SaaS subscription. Scales with modules added.
Domain Intelligence Modules
Add-on modules for risk, supply chain, financial analytics. Each multiplies contract value 2–3×.
Setup + Initial License
One-time platform deployment, module customisation and onboarding.
Enterprise Advisory
Project-based deployment services that accelerate platform adoption.
Board & Sustainability Advisory
GreenArc advisory for boards on sustainability strategy and ESG disclosure.
Three-Layer Expansion Model
Each layer 3–5× the contract value of the last
Sustainability + Risk Intelligence
Forces data unification across all enterprise systems. Builds the intelligence foundation.
Manufacturing · Chemicals · Energy
Ops + Safety + Supply Chain Intelligence
Same data foundation, broader reasoning. No new sales cycle — expands within existing accounts.
Predictive maintenance · H&S · Supply risk
Financial Intelligence — BFSI & Insurers
Credit risk, portfolio monitoring, asset-backed lending decisions. Serves banks and insurers on top of the industrial data layer.
BFSI · Insurers · Infrastructure funds
From $0.2M today to $5.5M ARR by FY29
Platform-led growth compounding through land-and-expand · 70% YoY CAGR
Competitive Differentiation
Built by operators who earned the data the hard way
7 years of proprietary models, data, and enterprise deployments. Now in market as Rubicr.
Rajashri Sai
Founder & CEO
“13 years building data platforms across GE, HSBC, UNICEF & the Gates Foundation.”
IIM Kozhikode · Lawyer & Company Secretary · Independent Director, Autoline Industries (NSE listed) · Co-Founder, Zuppa Drones (Series B) · Top 50 Women Innovators
rajashri@impactree.aiVivek Shankaranarayanan
Co-Founder & CPO
“15 years as Head of Site Technology at Reliance Industries — industrial context powering Rubicr’s domain reasoning engine.”
London Business School MBA · PG Diploma in AI/ML · ASQ Six Sigma Black Belt · 2 WIPO patents · R&D · Operations risk
vivek@impactree.aiRamki Ramakrishnan
Co-Founder & CRO
“24 years as C-level P&L leader at Temenos APAC — built enterprise deals with leading banks across Asia.”
London Business School MBA · Based in Singapore · Temenos MD, Asia Pacific (24 years) · Led SaaS GTM across APAC banking sector
ramki@impactree.aiTeam Members
Cross-functional expertise
WIPO Patents
Proprietary domain reasoning
Global Offices
Chennai & Singapore
Industries
Deep domain expertise
Company Journey
Foundation
Quantitative Impact Models
Built proprietary measurement frameworks. First data models — origin of the intelligence stack.
Partners: UNICEF, Gates Foundation, GE, HSBC
Productisation
Automating Impact Tracking
First enterprise SaaS products launched. Automated impact data collection and reporting. Product-market fit proved without institutional capital.
Expanded to 7 industries
Platform
Sustainability Intelligence Stack
Built Conflux, Nexus, Eden and Axiom — full AI stack. Expanded into enterprise sustainability consulting.
200+ enterprise relationships · 2 WIPO patents
Market
Rubicr Enterprise Launch
Rubicr launched as commercial enterprise product. $0.2M ARR · $1.5M FY27 pipeline identified.
3 flagship enterprise deployments in progress
Let’s Build the Future of Enterprise Together
The seed round does not fund an experiment — it scales a platform that already works.
Round Structure (SAFE Discount)
Expected Series-A Valuation
$23M – $30M
Projected FY27–28 · Benchmarked at $2–2.5M ARR
Fund Utilization
Round Milestones
Launch Caetis
AI Based Enterprise Financial Intelligence
100+ Enterprise Clients
Across identified sectors
$1MM Annual Revenue
With multi-year enterprise contracts
Industrial + BFSI Intelligence
The Palantir · Snowflake · SAP Analytics category
India · MENA · SE Asia
Manufacturing, Energy, Chemicals, Oil & Gas, BFSI
5–7 Year Realistic Capture
190,000 target enterprises × $350K ACV
The Long-Term Vision
Industry OS
Centralising intelligence for Manufacturing, Energy, Chemicals, and Automotive sectors.
Financial OS
Extending data into credit risk, portfolio monitoring, and for banks and insurers for their manufacturing assets.
Real-Economy OS
Connecting supply chains, insurers, and regulators into a single, cohesive intelligence network.