ELEVATE NXT 2026 — Round 3
Infrastructure Governance
& Audit Readiness Engine
for Power Distribution Utilities
A read-only governance overlay with explainable AI — enabling continuous risk visibility and audit-ready compliance evidence for India's DISCOMs.
Deeptech
Public Infrastructure Governance
Power Distribution
TRL 4 → 6
Karnataka-First
SkyEdgeAI · Bengaluru, Karnataka · Founded 2024 · Grant Ask: Rs. 1 Crore
The Team
Built by People Who Have Done This Before
Domain depth, AI systems expertise, and hands-on power sector experience — in one team.
Afzal Jan
Co-Founder & CTO
AI/ML Systems · Governance Architecture
Architect of multi-domain AI platforms for regulated cyber-physical infrastructure. 12+ years designing explainable AI systems for utilities, grid, and edge environments.
Nida Sahar Rafee
Co-Founder & COO
Cloud Infrastructure · Edge Networks
Led IT infrastructure at scale for telco and edge networks. High-availability systems design for large-scale distributed infrastructure deployments.
Devi Prasad Vuriti
Power Domain Lead
Power Generation · Transmission · DISCOM Operations
20+ years across generation, transmission, energy auditing, WHR and solar integration. Predictive reliability and governance dashboards for industrial leadership.
Kameswara Rao Tangudu
Integration & Solutioning Lead
OT/IT Convergence · API Governance · Utilities
Enterprise integration across mobility, utilities and safety domains. Middleware platforms and interoperability frameworks connecting OT/IT systems across regulated infrastructure environments.
Track Record
The founding team brings direct hands-on experience from power sector AI deployments in prior roles — including shadow-mode pilots covering outage management and compliance traceability at large-scale transmission and generation infrastructure. This operational experience is the architectural foundation of the SkyEdgeAI platform. Both founders are committed full-time.
Company
SkyEdgeAI — Purpose-Built for Infrastructure Governance
Founded 2024 · Bengaluru, Karnataka
Origin
SkyEdgeAI was purpose-built to solve a gap we observed repeatedly: infrastructure operators in regulated sectors have world-class operational systems but no equivalent governance intelligence layer.
Operational systems run the grid. Nothing was synthesising governance evidence from it.
We built the first read-only governance overlay purpose-designed for audit scrutiny — not operational control.
Operational systems run the grid. Nothing was synthesising governance evidence from it.
We built the first read-only governance overlay purpose-designed for audit scrutiny — not operational control.
This Grant
Validate TRL 4 → 6 for the DISCOM governance platform — demonstrating shadow-mode audit readiness in a Karnataka DISCOM environment, with STPI Bengaluru as institutional partner.
What We Do
- Build governance intelligence with explainable AI for critical infrastructure operators
- Generate explainable, audit-ready compliance evidence automatically — continuously, not episodically
- Operate exclusively in shadow mode — advisory outputs only, never replacing human accountability
Who We Serve
- Primary: State power distribution utilities (DISCOMs / ESCOMs) across India
- Audit & Compliance teams who need continuous governance visibility
- Adjacent: Water utilities, gas distribution, transmission companies — same governance gap
The Problem
A Governance Gap That Regulators Are Already Documenting
DISCOMs generate vast operational data — but governance of that data remains reactive, fragmented, and audit-vulnerable.
The Core Gap
- DISCOMs operate complex networks under increasing regulatory and audit scrutiny
- SCADA, OMS, MDMS, and ERP systems generate enormous operational data — but governance remains reactive and post-facto
- No system continuously links operational events to regulatory obligations in real time
Consequences
- Late risk identification — systemic issues surface weeks after occurrence
- Manual compliance evidence — fragmented, inconsistent, gap-dependent
- Audit preparation takes 3–6 months per compliance cycle
- Regulatory penalty exposure — Rs. 1–10 Cr per DISCOM per SERC cycle
Why Karnataka, Why Now
- KERC is among India's most active regulatory commissions — BESCOM's AT&C losses remain above 11% against a national best-practice benchmark of under 8% (KERC Tariff Order FY24)
- CAG audits increasingly demand digital audit trails — manual reconstruction is no longer sufficient
- Data volume from SCADA, OMS, MDMS, AMR systems has outpaced manual governance capacity
- Karnataka's RAPDRP-established IT infrastructure is the most DISCOM-digitised in India — the integration foundation already exists
Primary Impact
Audit & Compliance teams — who currently spend months preparing evidence that a continuous AI system can generate automatically.
Secondary: Oversight, planning, and regulatory affairs functions.
Secondary: Oversight, planning, and regulatory affairs functions.
Market Timing
2026: A Tipping Point for DISCOM Governance
Four forces are converging — and they are converging fastest in Karnataka.
01
Regulatory Pressure Intensifying
KERC and CERC compliance timelines are tightening. CAG audit standards are rising. AT&C loss reduction targets create operational urgency — and regulators are demanding evidence of continuous monitoring, not annual snapshots.
02
Data Volume Has Outpaced Manual Governance
SCADA, OMS, MDMS, and AMR systems are generating unprecedented operational data volumes. Human review cycles cannot keep pace. Governance must become automated to remain meaningful.
03
Manual Processes Are Visibly Failing
Audit preparation takes months. Risk identification lags by weeks. Compliance evidence is fragmented and inconsistent across cycles. This is not a technology gap — it is a governance architecture gap.
04
Karnataka Is the Right Starting Point
Karnataka has India's most digitised DISCOM infrastructure — RAPDRP-established across 64 towns. BESCOM serves 30 million+ consumers. KERC is the most active state regulator. The foundation for AI governance exists here first.
The window for AI-enabled governance is open now. Karnataka is the right place to validate it.
Competitive Landscape
No One Else Does What We Do
Every existing solution either runs the grid, controls it, or audits it manually — once a year. SkyEdgeAI is the only AI-native, read-only governance overlay that continuously synthesises explainable audit evidence.
| Solution | Read-Only? | AI-Native? | Explainable AI? | Audit Evidence? | Domain-Specific? |
|---|---|---|---|---|---|
| SkyEdgeAI | ✓ | ✓ | ✓ | ✓ | ✓ |
| SAP / Oracle ERP | ✗ | ✗ | ✗ | ✗ | ~ Generic |
| GE / Siemens SCADA | ✗ | ~ | ✗ | ✗ | ~ Control only |
| Big 4 Audit Firms | ✓ | ✗ | ~ | ~ | ~ Point-in-time |
| Generic GRC Tools | ~ | ✗ | ~ | ~ | ✗ |
| TCS / Infosys SI | ✗ | ~ | ✗ | ~ | ✗ |
What "Explainable AI ✓" Means in Practice
For every anomaly detected, SkyEdgeAI generates a plain-language compliance narrative identifying the originating data source, the detection rationale, and the specific KERC/CERC obligation implicated. An auditor reads a document — not a model score requiring interpretation.
Gap Analysis
Why Existing Approaches Cannot Close the Gap
The problem is not lack of tools — it is that every available tool was designed for a different job.
Operational Systems
Designed to Run the Grid
SCADA, OMS, MDMS, and ERP systems are optimised for operational performance — not governance evidence synthesis. They generate the data but have no mechanism to correlate it into a compliance record.
Manual Audits
Episodic and Post-Facto
Annual or periodic audits are structured snapshots of past performance. They cannot detect risk as it emerges. By the time an audit captures a compliance gap, the regulatory exposure has already accumulated.
Consultant Reviews
Point-in-Time, Not Continuous
Third-party reviews provide valuable but narrow windows. They do not produce the continuous, time-stamped evidence trail that CAG and KERC are increasingly requiring from state utilities.
The Result
No continuous, explainable linkage from operational events to compliance obligations — at any point in time.
Governance visibility does not scale with system complexity — and Karnataka DISCOMs are becoming more complex, not less.
The Solution
A Read-Only Governance Overlay — Not a Grid Control System
SkyEdgeAI sits above existing operational systems, reading their data without touching their operations, and synthesising continuous governance evidence.
What It Delivers
- Early risk visibility across outages, losses, and operational deviations — before they become regulatory findings
- Compliance traceability linking every operational event to the specific KERC/CERC obligation it implicates
- Verifiable, audit-ready evidence — structured artifacts that satisfy CAG and SERC evidentiary standards
Users
Primary: Audit & Compliance teams — whose evidence burden is directly reduced
Secondary: Oversight, planning, and regulatory affairs functions
Secondary: Oversight, planning, and regulatory affairs functions
Design Guarantees
🔒
No Operational Authority
The system advises. It never commands. No output touches grid control.
👁️
Read-Only Integration
Connects to SCADA, OMS, MDMS, ERP via read-only interfaces. No write access. Ever.
🧑⚖️
Human Accountability Retained
Every governance event is attributable to a human decision-maker. AI advises; humans decide.
🏛️
No System Replacement
Works across existing SCADA, OMS, MDMS, ERP — no migration, no disruption.
Technical Architecture
Read-Only by Design — How the System Works
Data Flow
SCADA
Event logs
Event logs
OMS
Outages
Outages
MDMS
Losses
Losses
ERP
Billing
Billing
↓
Read-Only Ingestion Layer
Log forwarders / read-only API connectors via DMZ · No agent on OT network · All reads logged
Log forwarders / read-only API connectors via DMZ · No agent on OT network · All reads logged
↓
Proprietary AI Engine
Anomaly detection & pattern correlation · Domain-specific · Patent-protected · No open-source components
Anomaly detection & pattern correlation · Domain-specific · Patent-protected · No open-source components
↓
GuardianLedger™
Immutable evidence store
Immutable evidence store
Compliance Index
KERC/CERC mapped
KERC/CERC mapped
Integration Scope
- SCADA: Hansen, Oracle, GE, or vendor-neutral event log adapters
- OMS: Standard outage event APIs
- MDMS: AMR/AMI meter data feeds
- ERP: Oracle, SAP, or custom billing adapters
OT Network Safety
Access uses network-isolated log forwarders or scheduled batch exports from read-only API endpoints through a DMZ connection. No agent is installed on operational technology infrastructure. Every data read is timestamped and logged with user and scope.
Deployment
On-premise at the DISCOM's site. No cloud dependency. All data stays within the DISCOM's own infrastructure boundary.
System Logic
Four Steps. Nothing Written Back. No Command Goes Out.
The entire value chain from raw operational data to audit-ready evidence — in read-only mode.
01
Read-Only Ingestion
DISCOM data — historical datasets and mirrored real-time extracts from SCADA, OMS, MDMS, and ERP — is ingested through isolated, fully logged read-only connectors. No write access. No operational dependency.
02
Proprietary AI Risk Detection
The domain-specific AI engine detects anomalies and correlates patterns across data sources. Purpose-built for power distribution — trained on sector-specific data, patent-protected, no open-source components.
03
Compliance & Control Mapping
Detected events are mapped to specific KERC/CERC regulatory obligations in real time. The compliance traceability index is updated continuously — not assembled retrospectively before an audit.
04
Time-Stamped, Verifiable Audit Evidence
GuardianLedger™ generates tamper-evident, human-readable compliance narratives — each anomaly linked to its source, rationale, and KERC/CERC obligation. An auditor receives a structured document, not a model output.
Deeptech & IP
Proprietary Technology Built for Audit Scrutiny
The output is a compliance document. The engineering underneath it sits at the intersection of five technology domains — none of which solves this problem individually.
OT/IT
Integration
Integration
Domain-Specific
AI/ML
AI/ML
Cryptographic
Evidence
Evidence
Regulatory
Knowledge
Knowledge
Explainability
Engineering
Engineering
GuardianLedger™
Immutability Mechanism — Cryptographic Evidence Domain
Implemented via a cryptographically hashed append-only event store — each governance event is assigned a SHA-256 hash chained to the prior entry. Tampering is cryptographically detectable. Performant on-premise within DISCOM compute constraints — no blockchain overhead required.
Five Properties
- Event immutability — once recorded, governance events cannot be altered; any tampering is detectable
- Cross-system correlation — unifies events from SCADA, OMS, and MDMS into a single auditable timeline
- Regulatory obligation mapping — each event tagged to specific KERC/CERC requirements; new orders ingested on issuance
- Explainable evidence output — plain-language compliance narratives; auditors read documents, not model scores
- Advisory-only architecture — every entry attributable to a human decision-maker
Proprietary AI Engine
Fully Proprietary — No Open-Source Components
- Purpose-built: Trained exclusively on power distribution operational data — SCADA event logs, OMS outage records, MDMS consumption patterns, KERC/CERC regulatory filings. Not adapted from a general-purpose model.
- Reduced hallucination and bias: Trained on power sector data with defined regulatory boundaries — does not inherit the biases or hallucination tendencies of open-source foundation models
- Structural explainability: Every anomaly output includes source, detection rationale, and applicable KERC/CERC obligation — not a probability score
IP — Three Layers
- Filed patent applications — covering GuardianLedger™ architecture and proprietary anomaly detection methodology; SkyEdgeAI retains full commercial licensing rights
- Proprietary training corpus — power distribution operational data from prior engagements; cannot be replicated without equivalent domain access
- Regulatory knowledge graph — KERC/CERC obligation taxonomy built from primary regulatory documents; encodes years of tariff orders and compliance rulings
Technology Readiness
TRL 4 → TRL 6: A Validated Core to a Demonstrated Environment
The grant does not fund a concept — it validates a working system in a real DISCOM environment. TRL 4 to 6 sits squarely within the ELEVATE NXT programme scope of TRL 3–8.
1
Basic
2
Concept
3
Proof of Concept
4
Current — Core Validated
5
Relevant Env.
6
Target — Demonstrated
7+
Scale
TRL 4 — Where We Are Now
Core components validated in controlled environments. Evidenced by the founding team's prior hands-on experience deploying shadow-mode AI pilots covering outage management and compliance traceability at large-scale transmission and generation infrastructure — the same architectural patterns and integration mechanisms underpin the SkyEdgeAI platform.
TRL 6 — Grant Target
Platform demonstrated in a relevant Karnataka DISCOM environment through a structured shadow-mode pilot — producing a validated KPI dossier, compliance traceability evidence, and stakeholder sign-off from the DISCOM Audit & Compliance team.
During the Grant
Analytics hardening for DISCOM data variability
Expanded compliance traceability framework (KERC/CERC obligations)
Stakeholder validation with DISCOM Audit & Compliance teams
Pilot Design
Shadow-Mode Validation — Zero Risk to Operations
One Karnataka DISCOM division. Read-only. Advisory. The DISCOM takes on no operational or data risk.
Pilot Scope
- Site: One Karnataka ESCOM division (primary pilot site)
- Mode: Read-only, advisory — no operational integration
- Data: Historical datasets + mirrored real-time extracts
- Duration: 6 months
Pilot Activities
- Read-only data ingestion from SCADA, OMS, MDMS, ERP via isolated connectors
- Risk anomaly detection using proprietary AI engine
- Compliance mapping against KERC/CERC regulatory obligation taxonomy
- GuardianLedger™ evidence generation and audit artifact production
- Structured validation sessions with DISCOM Audit & Compliance teams
Explicit Exclusions
- No grid control or operational commands
- No billing or consumer operations
- No procurement implications
- No data leaves the DISCOM's infrastructure boundary
DISCOM Engagement Pathway
Securing a Karnataka ESCOM as pilot site is Milestone 1 of the programme — a known, managed dependency with a clear mechanism, not an assumption.
- STPI Bengaluru provides the institutional channel to reach ESCOM MD level — bypassing standard vendor queues; engagement begins on grant day one
- Data access agreement sits below the KTPP tender threshold — no competitive tender required; approval rests with the ESCOM MD with involvement from IT and Audit heads
- The read-only, on-premise, grant-funded design means the ESCOM takes on near-zero legal, operational, and financial risk — the lowest-friction entry point possible
- Realistic commitment: 90–120 days to signed data access agreement, accounting for government legal review and committee scheduling
Data Ownership
The DISCOM retains full ownership and control of all data at all times. Access is read-only and fully logged. Every data read is timestamped and attributable. Even if SkyEdgeAI were acquired, the DISCOM has no obligation and retains all data.
Measurable Outcomes
KPIs Measured Against a Documented Pre-Pilot Baseline
Baseline documentation is the first activity — we establish current-state metrics before any SkyEdgeAI outputs are active.
| KPI | Current Baseline (Est.) | Target Post-Pilot |
|---|---|---|
| Risk detection coverage | Reactive / post-event only | >80% of systemic risks identified proactively |
| Time to risk identification | 2–4 weeks (manual review cycle) | <4 hours (automated detection) |
| Compliance traceability index | Fragmented, manual, <20% automated | 80–90% automated linkage to KERC/CERC |
| Audit preparation effort | 3–6 months per compliance cycle | <4 weeks (40–60% reduction) |
| Evidence completeness score | Inconsistent; gap-dependent | 95%+ from Day 1 of pilot |
Quality KPIs
False Positive Rate
Tracked as an explicit KPI throughout the pilot. Target: <15% of flagged events. Advisory-only architecture means false positives generate no operational impact — they are measured and reduced iteratively.
Stakeholder Acceptance
Structured feedback survey with DISCOM Audit & Compliance teams at pilot midpoint and close. Acceptance score is a formal KPI — not a qualitative observation.
Go-To-Market
Three Parallel Tracks. One Reference. National Moat.
Government DISCOM procurement is 24–30 months from first engagement to signed licence. We have designed the company around that cycle — not against it.
Track A · Pilot
Grant Period — Months 1–12
Below KTPP threshold. No tender required.
- M1–3: STPI Bengaluru institutional engagement → BESCOM MD
- M3–4: Data access agreement signed (90–120 days — government legal review + committee scheduling)
- M4–10: 6-month shadow-mode pilot, one BESCOM division
- M10–12: KPI dossier, stakeholder validation, security audit certificate
Output
Validated platform + KPI evidence + Audit & Compliance team endorsement + security clearance
Track B · Commercial
Months 12–30 · KTPP/GeM Process
- M12–18: BESCOM Board memo — MD + IT + Audit heads. Board of Directors approval (3–6 months at a financially-stressed ESCOM)
- GeM: SkyEdgeAI is a registered GeM vendor — custom bid via GeM eliminates full KTPP open tender, compressing procurement by 4–6 months
- M18–26: GeM custom bid published, technical evaluation (pilot dossier = sole validated evidence), L1 selection
- M26–30: Contract signed, performance security, first Rs. 50L–1 Cr annual licence payment
Why SkyEdgeAI Wins the GeM Bid
No other vendor will have run a shadow-mode proof-of-concept at BESCOM. The tender specification is informed by our pilot learnings — first-mover advantage translates directly into specification ownership.
Track C · Policy
Months 6–36 · Runs in Parallel
- M6–12: Energy Department informal briefings via STPI — begins during pilot, not after
- M12–18: RDSS AI/ML use case registration (REC/PFC portal — active call) — converts licence from ESCOM opex to central grant-eligible spend
- M18–36: Karnataka Energy Department G.O. issued — recommends all five ESCOMs evaluate validated platform
- M24–36: KERC accepts GuardianLedger™ artifacts at BESCOM annual performance review — establishes regulatory evidence standard
The Moat This Creates
Each subsequent ESCOM inherits BESCOM's credibility — no repeat pilot required. KERC acceptance + G.O. + RDSS registration means Year 3–6 cycles are 50–60% shorter than Year 1–2.
Honest Revenue Trajectory
First licence: Month 26–30. Karnataka ARR at Year 4: Rs. 1–2 Cr. 5–8 DISCOMs, Rs. 3.5–8 Cr ARR at Year 5–6. Patient capital — defensible moat.
Why the Slow Cycle Is the Moat
Any competitor entering post-BESCOM faces the same 24-month cycle — without the pilot evidence, KERC acceptance, G.O., or RDSS registration. First-mover in government time = 3–4 years of defensible exclusivity.
Market Opportunity
A Rs. 500–1,000 Crore Governance Tooling Opportunity in India
Sized on per-DISCOM governance licence pricing — not total IT spend.
Market Sizing
| Segment | Basis | Size |
|---|---|---|
| TAM | ~100 DISCOMs × Rs. 50L–1Cr governance licence/yr | Rs. 500–1,000 Cr |
| SAM | ~50 digitally mature DISCOMs with SCADA/OMS/MDMS stack and active regulatory scrutiny | Rs. 250–500 Cr |
| SOM Year 2.5–4 |
5 Karnataka ESCOMs × Rs. 50L–1Cr/yr (GeM procurement → G.O. → sequential adoption) | Rs. 2.5–5 Cr |
Market sizing anchored to governance tooling licence pricing — not total IT spend. Governance is a dedicated, growing budget line as regulatory pressure intensifies.
Adjacent Expansion
Beyond DISCOMs: water utilities, gas distribution networks, and transmission companies (e.g., KPTCL) share the same governance gap profile. DISCOM validation creates a replicable template across regulated infrastructure.
Karnataka Tailwind
- Karnataka has India's most digitised DISCOM infrastructure — RAPDRP across 64 towns; the integration foundation exists here first
- BESCOM alone serves 30 million+ consumers — governance improvement here has statewide multiplier effects
- Karnataka's clean energy push (14.6 GW installed renewable capacity) adds grid governance complexity and urgency
- KERC is among India's most active state regulatory commissions — highest scrutiny, highest value from continuous monitoring
Why Governance Tooling Is Growing
- KERC/CERC compliance timelines tightening; AT&C targets create urgency for continuous monitoring
- CAG audits increasingly demand digital audit trails — manual reconstruction no longer sufficient
- India's 24×7 Power for All commitments and renewable integration adding grid complexity governance cannot track manually
Socio-Economic Impact
Governance Failures Cost Karnataka More Than You Think
Current Cost of Poor DISCOM Governance
| Problem | Data Point | Source |
|---|---|---|
| AT&C losses (Karnataka) | BESCOM ~11.5%; state avg higher vs. best practice <8% | KERC Tariff Order FY24; PFC Annual Report 2022-23 |
| AT&C financial impact | Each 1% loss = ~Rs. 200–400 Cr/yr for a major DISCOM | UDAY scheme data; PFC state utility reports |
| Manual audit prep cost | Rs. 5–15 Cr/yr per DISCOM in person-hours | PFC Performance Report; operator estimates |
| Time to identify risk | 2–4 weeks (reactive manual review) | DISCOM audit cycles; operational baseline |
| Regulatory penalty exposure | Rs. 1–10 Cr/DISCOM/SERC cycle | KERC/CERC public penalty orders |
How SkyEdgeAI Reduces This Cost
- Audit preparation: 40–60% effort reduction via automated evidence generation
- Time to risk identification: 2–4 weeks → <4 hours (proprietary anomaly detection)
- Compliance traceability: 80–90% automated linkage to KERC/CERC obligations
- Evidence completeness: 95%+ from Day 1 (GuardianLedger™ continuous capture)
- Consumer grievance patterns: Early warning reduces escalation — anomaly detection flags deteriorating service metrics
Employment & Skill Creation
- Technical AI/ML/data science roles created within Karnataka
- Skill uplift for DISCOM Audit & Compliance teams through structured technology adoption
- Replicable governance framework creates a knowledge economy reference for infrastructure governance nationally
Budget & Grant Utilisation
Rs. 1 Crore — Every Rupee Scoped to TRL Validation
No spend on sales, marketing, production deployment, or live operations. One DISCOM division. Fully costed.
Line-Item Budget
| # | Line Item | Amount |
|---|---|---|
| 1 | AI model training & DISCOM data variability hardening | Rs. 22L |
| 2 | Compliance traceability framework development | Rs. 18L |
| 3 | Shadow-mode pilot operations (1 DISCOM division, 6 months) | Rs. 25L |
| 4 | Security audit, pen testing & governance review | Rs. 12L |
| 5 | KPI validation dossier & grant reporting | Rs. 10L |
| 6 | Team & project management (grant period) | Rs. 8L |
| 7 | Contingency (10%) | Rs. 5L |
| Total | Rs. 1 Cr | |
Tranche-Linked Milestones
Tranche 1
Rs. 25L
Grant acceptance + team onboarding + Karnataka ESCOM data access agreement signed (90–120 days via STPI facilitation — below KTPP tender threshold, MD-level approval)
Tranche 2
Rs. 30L
AI model hardening complete + GuardianLedger™ v1 deployed in shadow mode at pilot site
Tranche 3
Rs. 25L
Compliance framework validated with DISCOM Audit team + KPI baseline documented
Tranche 4
Rs. 20L
Shadow-mode pilot complete + KPI validation dossier submitted + stakeholder sign-off received
Karnataka Relevance
Karnataka as the Right First Environment for This Technology
Not just the most convenient — the most consequential. What works here sets the national standard.
Why Karnataka Specifically
- India's most digitised DISCOM infrastructure — RAPDRP across 64 towns; the integration foundation for AI governance exists here first
- KERC is India's most active state regulatory commission — governance tooling is most meaningful where scrutiny is highest
- STPI Bengaluru provides institutional credibility and formal ESCOM engagement pathway
- ELEVATE NXT ecosystem provides post-grant pathways for reference customer development and G.O. adoption
What This Grant Delivers for Karnataka
- Addresses increasing audit and governance pressure in Karnataka ESCOMs with a validated technology response
- Establishes a Karnataka-first, India-first reference for AI-enabled infrastructure governance
- Creates reusable governance frameworks and KPI benchmarks that become public infrastructure for the sector
- Positions Karnataka as the governance technology leader in India's power distribution sector
Non-Binding Safeguards
- No procurement implication from this grant — future adoption subject to standard G.O. processes
- No vendor lock-in — DISCOM retains all data and all rights
- Even a non-adoption outcome delivers validated workflows and benchmarks that benefit Karnataka's regulatory ecosystem
BESCOM Scale
30M+
Consumers served by BESCOM alone
14.6GW
Karnataka installed renewable capacity
64
Towns with RAPDRP-established IT infrastructure
The Ask
A Safe, Transparent Way for Karnataka to Validate What Comes Next
Scope Lock
- Read-only, advisory system
- No operational authority
- No system replacement
Risk Posture
- No operational risk — shadow mode only
- No liability transfer — DISCOM retains full data control
- Financial risk limited to milestone-linked tranches
Failure Is Acceptable
- Even if some KPIs fall short, the pilot delivers validated governance workflows, auditable artifacts, KPI benchmarks, and documented learnings
- Both success and partial success have value for Karnataka's regulatory infrastructure
Rs. 1 Crore
Grant to validate TRL 4 → 6 for India's first read-only governance overlay for DISCOM audit readiness.
A safe, transparent way for Karnataka to validate next-generation infrastructure governance — before future policy decisions are made.
A safe, transparent way for Karnataka to validate next-generation infrastructure governance — before future policy decisions are made.
Post-Grant Trajectory
First BESCOM licence signed at Month 26–30 via GeM procurement. Karnataka G.O. and RDSS AI/ML use case registration follow during Years 2–3, creating the policy infrastructure for sequential ESCOM adoption. 5–8 DISCOMs, Rs. 3.5–8 Cr ARR by Year 5–6 — patient capital, defensible moat. The KPI validation dossier anchors SkyEdgeAI's seed/pre-Series A raise targeting energy governance and infrastructure AI investors.