Multi-sensor radar fusion visualization
BUSINESS PROPOSAL · MAY 2026

DRISHTIAKSHI

An indigenous, AI-fused, cost-matched counter-UAS platform for India's $1,000 drone problem.

DETECT · IDENTIFY · TRACK · NEUTRALIZE

<3s
Detect → engage loop
200×
Cost asymmetry to close
>70%
Indigenous content target
DETECT
IDENTIFY
TRACK
NEUTRALIZE
X-BAND RADAR
PASSIVE RF
EO / IR
ACOUSTIC FUSION
EDGE AUTONOMY
QUANTUM-ADVERSARIAL AI
AKASHTEER INTEGRATION
INDIGENOUS
DETECT
IDENTIFY
TRACK
NEUTRALIZE
X-BAND RADAR
PASSIVE RF
EO / IR
ACOUSTIC FUSION
EDGE AUTONOMY
QUANTUM-ADVERSARIAL AI
AKASHTEER INTEGRATION
INDIGENOUS
/ 01 · PRODUCTS

Three products. One sovereign AI stack.

ParaMedha applies the same quantum-aware AI core across defence, capital markets and enterprise automation — each productised, each generating revenue.

/ 02 · DRISHTI-AKSHI · EXECUTIVE SUMMARY

An Indian answer to the $1,000 drone problem.

THE OPPORTUNITY · Operation Sindoor (May 2025) exposed a 200× cost asymmetry — a $1,000 drone defeating $200,000-class interceptors. The IAF SWAC RFI explicitly demands a launch-and-forget answer. India has roughly 24 months to deploy it.

THE SOLUTION · DRISHTI-AKSHI fuses four sensing modalities through an indigenous transformer-based AI pipeline running on edge compute, with a six-tier effector ladder that selects the cheapest sufficient response.

THE ASK · ₹3–4 Cr non-dilutive grant for Phase 1. Full programme ₹210–335 Cr through induction. 10-year revenue potential >₹2,500 Cr including export.

/ 02 · FIVE PILLARS

Self-reliant C-UAS. Built to win the economics.

01

Multi-sensor fusion

Radar, RF, EO/IR and acoustic fused at feature and decision level. No single blind spot.

02

Edge-autonomous AI

Each node operates locally when comms degrade. Mesh-cooperative when uplinks are healthy.

03

Cost-matched kill chain

Six-tier effector ladder. Median engagement ₹500 to ₹2 lakh — not ₹4.2 Cr.

04

Friend / Foe / Neutral

DigitalSky + RF fingerprint + behavioural model. Friendly UAVs stay alive.

05

Indian-context AI

Models trained on subcontinental birds, DGCA platforms, local terrain. The data is the moat.

IDEX · ADITI · TDF · DRDO · BEL · IIT MADRAS CAI
Thermal EO/IR drone tracking with HUD telemetry overlay
/ 03 · SENSOR FUSION

Four modalities.
One decision in under 3 seconds.

Sensor diversity beats sensor sophistication. Every node fuses X-band radar, passive RF, EO/IR and acoustic at feature and decision level — eliminating the single-sensor blind spots adversaries exploit.

X-BAND RADAR
Doppler · 360°
PASSIVE RF / SDR
70 MHz – 6 GHz
EO / IR
Day · thermal · zoom
ACOUSTIC ARRAY
8-mic beamform
EDGE COMPUTE · Jetson AGX Orin 64GB · 275 TOPS · Hailo-15H · TensorRT INT8 · <300W
Quantum lattice visualization
/ 04 · QUANTUM-ADVERSARIAL AI

Robust AI by design,
not by retrofit.

Game-theoretical defences today. Quantum-enhanced equilibria for the Phase 5+ adversary. Aligned with India's National Quantum Mission (₹6,003 Cr, 2023–2031).

Adversarial oracle

Worst-case attack simulation against every sensor pipeline — co-evolved with the defender model in a min-max loop.

Hybrid quantum solver

QAOA / VQE on NISQ devices to compute Nash-equilibrium defender policies as quantum hardware matures.

Certified robustness

≥70% AI capability under 100 W/m² broadband jamming with provable ε-adversarial-radius bounds.

Quantum neural network of entangled qubits
QPU · IBM_HERON_R2156 QUBITS · T₂ 280µsCIRCUIT DEPTH · 42
/ FOUR-LAYER STACK

From PGD-hardened CNNs to post-quantum mesh comms.

Layer 01Classical adversarial training

PGD, AutoAttack and FGSM augmentation across radar, RF and EO/IR feature spaces. Hardens models before any quantum layer engages.

Layer 02Game-theoretic equilibrium

Stackelberg defender–attacker formulation solved with mirror-descent. Yields min-max policies that survive the worst rational adversary.

Layer 03Quantum optimisation (QAOA)

NISQ-era QAOA / VQE on 20–127 qubit backends to approximate Nash equilibria in defender policy space. Quantum hardware abstracted behind a classical fallback.

Layer 04Quantum-secure comms

Post-quantum KEM (ML-KEM / Kyber) on mesh uplinks. Aligned with National Quantum Mission Thrust Area on quantum communication.

≥70%
AI capability under 100 W/m² jam
≤1e-4
Adversarial fooling rate (ε=8/255)
156
Qubit backends targeted (IBM Heron)
ML-KEM
Post-quantum mesh KEM (FIPS 203)
FOUNDATION · PhD UTS Sydney · 3 Springer monographs · 10 peer-reviewed papers · Indian patent (KAN-based residual learning, filed 2025)
/ 05 · THREAT COVERAGE

Six classes. One strategic threat.

Built around the worst case — silent, GNSS-denied drones with no RF signature.
Class I threat — Nano / micro quads
01Class I
Nano / micro quads
Reconnaissance, FPV
Class II threat — Tactical UAVs
02Class II
Tactical UAVs
Loitering munitions
Class III threat — Fixed-wing
03Class III
Fixed-wing
Long-range strike
Swarms threat — Decoy + strike waves
04Swarms
Decoy + strike waves
Saturation attacks
GNSS-denied threat — VIO / SLAM nav
05GNSS-denied
VIO / SLAM nav
Jam-resistant
Silent / EO threat — Frequency-hopping
06Silent / EO
Frequency-hopping
No RF signature
Hostile drone swarm at dusk with surveillance HUD
◉ HOSTILE · 14 TRACKS · BRG 042°
SWARM SIZE
8–32
UNIT COST
~$1K
DEFENDER COST
$200K+

The economic gap is the threat. A 16-drone saturation wave costs an adversary ~$16,000 — and bankrupts any defender forced to answer it with legacy SAMs. DRISHTI-AKSHI closes the ratio with a cost-matched effector ladder.

/ KILL CHAIN · < 3 SECONDS

Detect to engage in six fused steps.

T+0.0s
Radar return
X-band Doppler clusters tagged as candidate track
T+0.4s
RF correlate
SDR fingerprint matched against DJI / Autel / military library
T+1.1s
EO/IR lock
Thermal tracker confirms airframe class and heading
T+1.8s
Classify
Friend / foe / neutral decided by behavioural transformer
T+2.4s
Effector select
Cheapest sufficient response chosen from six-tier ladder
T+2.9s
Engage
Launch-and-forget · operator-on-the-loop · audit logged
EFFECTOR LADDER · CHEAPEST SUFFICIENT
T1Soft kill — RF spoof₹500
T2GNSS denial₹2,000
T3Directed RF burst₹15,000
T4Net interceptor UAV₹40,000
T5Kinetic micro-missile₹1.2 L
T6Swarm-on-swarm₹2 L
/ 06 · ARCHITECTURE

Sensors → Fusion → AI → Effector mesh.

Click any stage to inspect its sub-systems and latency budget.
/ DATA FLOW · LEFT TO RIGHT
STAGE 01 · SENSORS

Heterogeneous sensors fielded at every node. Time-synchronised via PTP, geo-referenced via INS.

X-band radar
Passive SDR (70 MHz–6 GHz)
EO / IR gimbal
Acoustic 8-mic array
ADS-B / DigitalSky
END-TO-END LATENCY
S
.4s
F
.7s
AI
1.1s
E
.7s
TOTAL · DETECT → ENGAGE< 2.9s
DEPLOYMENT TOPOLOGY
  • Node-level autonomy when uplink degrades
  • Mesh-cooperative fusion when uplinks healthy
  • Akashteer / IACCS C2 northbound integration
  • Air-gapped install option for classified sites
/ 07 · LIVE OPS DASHBOARD

Operator view, live.

Simulated feed of fused tracks, threat scoring and the immutable audit log. Production view streams over signed mTLS.
LIVE OPS · NODE-04 · SECTOR 042°
TICK 0000
TRACKS
6
FOE
3
ENGAGING
0
UPLINK
OK
IDCLASSTYPEBRGRNG (m)SPDTHREATEFFECTORSTATUS
TRK-1000DJI Mavic 3FOE012°180018
88
T1 · RF spoofCLASSIFY
TRK-1001Autel Evo IIFOE047°222024
88
T2 · GNSS denyCLASSIFY
TRK-1002Custom FPVFOE088°264030
88
T3 · RF burstCLASSIFY
TRK-1003Heron-MK2FRIEND134°306036
15
NEUTRAL
TRK-1004Bird flockNEUTRAL176°348042
15
TRACK
TRK-1005Unknown wingUNKNOWN210°390048
55
TRACK
AUDIT LOG · IMMUTABLESHA-256 · CHAINED
T+00:00 · NODE-04 · uplink OK · sweep 360°
T+00:02 · TRK-1023 · radar+RF correlate · class candidate
T+00:05 · TRK-1041 · classifier → FOE · score 0.94
T+00:06 · TRK-1041 · effector T3 armed · operator hold
SIGNED · ML-DSA● STREAMING
/ P-02 · QUANTEDGE

Quantum-aware algorithmic trading.

QuantEdge is a multi-asset execution and signal platform for prop desks, HNI portfolios and quant funds. The same adversarial-AI core that hardens DRISHTI-AKSHI runs the alpha models — trained against synthetic market regimes, stress-tested with QAOA portfolio search.

  • NSE / BSE / MCX co-located, global FIX gateways
  • Transformer alpha · regime classifier · risk parity overlay
  • SEBI-aligned immutable audit · pre-trade compliance
  • Strategy IDE · Python / Rust · walk-forward backtest
P99 ORDER ACK
780µs
LIVE STRATEGIES
42
AVG SHARPE (WF)
2.41
MAX DRAWDOWN
−6.8%
ASSET CLASSES
Eq · F&O · FX · Crypto
DEPLOYMENT
Co-lo · VPC · On-prem
SIGNAL STACK
Ingest
Feature
Alpha
Risk
Sizing
Route
Execute
Audit
/ P-03 · AUTOFLOW

Web app QA, Jira to Playwright.

AutoFlow is an end-to-end web application test automation suite. A Jira ticket flows through a Navya-Nyāya inference engine, becomes Gherkin, compiles into a Cucumber BDD framework, and executes via a Playwright wrapper — with physics-informed ML for flake prediction and quantum-enabled deep learning for self-healing locators and visual diff.

Navya-Nyāya reasoner

Pakṣa · Sādhya · Hetu · Dṛṣṭānta · Nigamana mapped to acceptance criteria — auditable, citation-grounded test inference.

Gherkin → Cucumber

Given/When/Then auto-generated, step definitions emitted into a typed Cucumber-JS BDD project.

Playwright wrapper

Page-object factory, parallel shards, trace + video + HAR, cross-browser Chromium · WebKit · Firefox.

PIML + Quantum DL

Physics-informed models predict flake & runtime; QDL (variational hybrid nets) self-heals locators and ranks visual diffs.

NAVYA-NYĀYA
5-step inference
PIML FLAKE MODEL
AUC 0.94
QDL LOCATOR NET
8-qubit VQC
SELF-HEAL RATE
92.7%
/ END-TO-END PIPELINE
01
JIRA INTAKE
Webhook on story · ACs · attachments · linked epics
jira-sdk
02
NYĀYA REASON
Pakṣa→Hetu→Dṛṣṭānta inference over ACs · risk-weighted scenarios
navya-nyaya
03
TESTCASE GEN
Positive · negative · boundary · a11y · perf cases
llm + piml
04
GHERKIN EMIT
Feature · Scenario · Given/When/Then · Examples tables
gherkin
05
BDD COMPILE
Cucumber-JS step defs + tagged hooks + fixtures
cucumber
06
PLAYWRIGHT RUN
Sharded across browsers · QDL self-heal on locator miss
pw-wrapper
07
REPORT + LOOP
HTML/Allure · trace · flake score · Jira comment + status
report
/ AUTO-EMITTED · checkout.feature
Feature: Checkout — PROJ-1428
  @smoke @piml-stable
  Scenario: User completes payment with saved card
    Given a logged-in user with a saved Visa card
    When they add "SKU-991" to cart
    And proceed to checkout
    Then the order is confirmed within 2s
    And a receipt is emailed
/ 08 · COMPETITIVE POSITION

Built where the others cannot ship.

Sovereign IP, sub-continental data, cost-matched effectors. Indian C2 native — not bolted on.
SYSTEMSOVEREIGNTYSENSOR FABRICAI COREEFFECTOR LADDERARCHITECTUREINDIA INTEGRATIONCOST / ENGAGEMENT
DRISHTI-AKSHI
ParaMedha · India
◆ INDIGENOUS
IndigenousRadar + RF + EO/IR + AcousticQuantum-adversarial6-tier (₹500–₹2L)Edge + MeshAkashteer / DigitalSky₹500 – ₹2L
Anduril Lattice
Anduril · USA
ITAR-restrictedRadar + EO/IRClassical MLKinetic-heavyMeshLimited$50K – $500K
DroneShield DroneGun
DroneShield · AUS
ImportableRF-onlyRules-basedSoft-kill onlySingle-nodeNone$25K – $80K
Smart Shooter SMASH
Smart Shooter · ISR
ImportableEO-only (rifle sight)Vision MLKinetic (rifle)Single-shooterNone$10K / round
IAI Drone Guard
IAI · ISR
ImportableRadar + EO/IRClassical MLJam + kineticMesh (proprietary)Limited$1M+ system
◆ Indicative comparison from public material. Detailed benchmark report available under NDA.
/ 09 · TECHNICAL DATASHEETS

Subsystem specs. Issued on identification.

⚠ CONTROLLED DISTRIBUTION
Identify before download

Datasheets are issued to verified defence, government and integrator contacts only. Submissions are logged and reviewed by the ParaMedha programme office.

/ 10 · ROADMAP

Concept to induction in 30 months.

Phase 1
Concept validation
iDEX ADITI · TRL 3→4
₹3–4 Cr
Phase 2
Prototype + trials
Single-node field trial
₹15–25 Cr
Phase 3
User evaluation
Multi-node mesh, IAF SWAC
₹40–60 Cr
Phase 4
Limited series
25–50 inducted units
₹150–250 Cr
Phase 5
Series + export
SP/RP route, GCC + ASEAN
Scale
Isometric engineering blueprint of the interceptor node
/ DELIVERABLES BY PHASE

Every milestone is TRL-anchored and audited.

P112 moWorking sensor-fusion prototype · adversarial dataset v1 · TRL 4 report
P218 moSingle-node field trial at user range · TRL 6 · iDEX milestone clearance
P324 mo3-node mesh · IAF SWAC user evaluation · TRL 7 · MoD AoN preparation
P430 mo25–50 unit limited series production · BEL co-manufacturing line · TRL 8
P536+ moSeries production · SP/RP route · GCC + ASEAN export campaign
FUNDING STACK · iDEX ADITI · TDF · DRDO TBI · MoD AoN · BEL co-mfg · SP/RP induction
₹210–335 Cr
Cumulative programme outlay
~85%
Non-dilutive (grants + contracts)
>₹2,500 Cr
10-year revenue potential
/ 11 · LEADERSHIP

Indigenous IP. Indian consortium.

ParaMedha leads as prime integrator and AI/fusion IP owner. Consortium partners include BEL, SAMEER, Tonbo Imaging, IIT Madras CAI, Bharat Dynamics and DRDO labs.

AC
Aneesh Sreevallabh Chivukula
Founder & Chief Scientist

PhD, UTS Sydney · Assistant Professor, BITS Pilani. Three Springer monographs, ten peer-reviewed papers on adversarial machine learning.

/ CONFIDENTIAL · FOR EVALUATION PURPOSES ONLY

Detect. Identify.
Track. Neutralize.

Indigenous. Aatmanirbhar Bharat. Built for the next Operation Sindoor.