NUJEXA — AI RESEARCH & ENGINEERING LAB
We build intelligence.
Intelligent systems — models, agents, robots, private infrastructure.
SOFTWARE INTELLIGENCE
·
PHYSICAL INTELLIGENCE
·
INFRASTRUCTURE YOU CONTROL
Enter the Lab  ↓
INSIDE THE CORE
Intelligence is a system.
01PerceptionENTER →
02MemoryENTER →
03ReasoningENTER →
04ActionENTER →
Signals enter the system from outside — raw, unstructured, continuous.
Explore Intelligence  →
INDEX
Explore Intelligence
I · SOFTWARE INTELLIGENCE
AgentsPlan, act, recover
Multi-AgentOrchestrated teams
MemoryExperience becomes structure
Coding AgentsSoftware that extends itself
Deep SearchReasoning retrieval
MultimodalText, vision, sound
II · PHYSICAL INTELLIGENCE
Physical AIPerceive, reason, act
RoboticsRobot learning
Autonomous DronesAerial intelligence
Computer VisionMachine perception
DetectionEvents, not frames
Edge AIIntelligence on the device
III · FOUNDATION & TRUST
ModelsMachinery of reasoning
Model TrainingBase → specialist
Private AIYour own environment
SecurityUnderstands, not records
SimulationLearning at scale
ResearchUnderstanding first
DEPLOYMENTS BY INDUSTRY  →
MAP OF INTELLIGENCE  →
CONTACT  →
WORLD 01
Autonomous Agents
A single agent is a system of orbiting capabilities. Enter Memory to go deeper.
MEMORY
TOOLS
ENVIRONMENT
PLANNING
CONTEXT
SKILLS
WORLD 02
Memory changes intelligence.
Every interaction becomes structure. Structure becomes knowledge. Knowledge becomes behaviour.
SHORT-TERM CONTEXT
↓
EPISODES
↓
KNOWLEDGE
↓
PATTERNS
↓
LONG-TERM MEMORY
WORLD 04
Intelligence becomes more powerful when it collaborates.
RUN A REQUEST
WORLD 03
Simulation
A live population learns inside a synthetic world. Agents sense, forage, die and reproduce — genes for speed and perception mutate every generation, and mean fitness climbs.
TERRAIN — evolution in a synthetic world: agents forage, reproduce and mutate. Fitness climbs generation by generation with no human labels at all.
ENVIRONMENTTERRAIN
AGENTS24
SPEED×2
OBJECTIVEFORAGE
WORLD 05
What happens inside a model?
Hover the stages to pull the system apart, layer by layer.
A model is a stack of stages. Data flows top to bottom — each stage transforms it into something the next one can use.
SIGNAL → TOKENS → VECTORS → ATTENTION → PREDICTION
INPUTRAW SIGNAL
TOKENIZATIONCUT INTO PIECES
REPRESENTATIONMEANING AS GEOMETRY
REASONINGATTENTION LAYERS
OUTPUTNEXT-TOKEN PREDICTION
WORLD 06
Research
Understanding intelligence before deploying it.
R-01Long-Term Memory
R-02Multi-Agent Coordination
R-03Agent Evaluation
R-04Synthetic Environments
WORLD 07
What we build and train.
D-01Fintech models
D-02Health application
D-03Data analysis
D-04Deep search
D-05End-to-end automation
D-06Automation discovery
Models trained on market microstructure, risk and transaction behaviour — pricing, fraud, credit and portfolio reasoning, evaluated against live market noise rather than static benchmarks.
FINE-TUNED · RISK · REAL-TIME
BEYOND TEXT  →
WORLD 08
Intelligence is not only text.
Language is the interface, not the limit. Text models read documents, code and intent — and hand structure to everything else in the stack.
TEXT
VISION
AUDIO
TOKENS · 128K CONTEXT
STRUCTURED OUTPUT
TOOL CALLING
WORLD 09 · INTELLIGENCE ENTERS THE PHYSICAL WORLD
AI that can perceive, reason and act.
We build systems where intelligence is no longer confined to a screen. Cameras become perception. Sensors become context. Models become decisions. Robots and autonomous machines turn those decisions into physical action.
SENSORS
↓
PERCEPTION
↓
WORLD MODEL
↓
REASONING → PLANNING → CONTROL
↓
ACTION
RoboticsRobot perception, manipulation, navigation and autonomous task execution.
Autonomous dronesVision-guided flight, inspection, mapping, detection and autonomous missions.
Computer visionPerception systems for detection, tracking, segmentation and spatial understanding.
Edge intelligenceAI inference directly on robots, cameras, drones and embedded hardware.
Digital twinsVirtual replicas of environments used for testing, simulation and training.
WORLD 10 · ROBOT LEARNING
We train machines to understand the world.
Modern robots are not programmed one movement at a time. They learn perception, manipulation, navigation and behaviour from demonstrations, simulation, synthetic environments and real-world experience.
PERCEPTIONObjects, humans, environments
MANIPULATIONGrasp, move, sort, interact
NAVIGATIONMapping, obstacles, routes
IMITATION LEARNINGLearn from human demonstration
REINFORCEMENTMillions of simulated attempts
VISION-LANGUAGE-ACTIONInstruction becomes movement
SIM-TO-REALValidate virtually, deploy physically
SIMULATED TRAINING TELEMETRY
ENVIRONMENTS 2048
POLICY ITERATION 18,421
SUCCESS RATE 41%
AERIAL INTELLIGENCE  →
WORLD 11 · AERIAL INTELLIGENCE
Machines that see from above.
We develop AI systems for autonomous drones that inspect, map, detect, monitor and navigate without requiring a human to interpret every frame.
INFRASTRUCTURE INSPECTIONPower lines, buildings, roofs, towers, solar and industrial assets.
AUTONOMOUS MAPPINGVisual, thermal, LiDAR and spatial data into structured maps.
OBJECT & ANOMALY DETECTIONThermal patterns, damage, missing components, environmental change.
AUTONOMY & FLEET OPERATIONSObstacle avoidance, dynamic replanning, multi-drone missions and docking.
DEMO MISSION · SIMULATED
AUTONOMY 94%
OBJECTS 143
ANOMALIES 03
AREA SCANNED 72%
MACHINE PERCEPTION  →
WORLD 12 · MACHINE PERCEPTION
Teaching machines to see.
Computer vision turns cameras into intelligent sensors. We develop systems that understand people, objects, movement, environments and events in real time.
RAW — a camera alone produces pixels. Thirty frames a second of data nobody has time to watch.
OBJECT DETECTION
SEGMENTATION
TRACKING
FACE VERIFICATION
OCR / DOCUMENT VISION
POSE & ACTIVITY
QUALITY INSPECTION
SPATIAL REASONING
CONSENT & AUDIT CONTROLS
RAW
DETECTION
TRACKING
SEGMENTATION
UNDERSTANDING
ACTION
WORLD 13 · DETECTION & RECOGNITION
Finding what matters in an infinite stream.
Detection is where perception becomes useful. Not «there are pixels» but «there is a person in a restricted zone, and it started forty seconds ago».
PEOPLE & BEHAVIOURPresence, count, direction, dwell time, falls, crowding, unusual movement.
OBJECTS & VEHICLESCustom classes, plates, tools, packages, PPE, equipment state.
DEFECTS & ANOMALIESProduction defects, thermal outliers, wear, leaks, structural change.
EVENTS, NOT FRAMESDetections become timelines, thresholds, escalation and audit trails.
SIMULATED STREAM · 12 SOURCES
INFERENCE 41 FPS
TRACKED 27
EVENTS TODAY 1,204
FALSE POSITIVE 0.4%
SECURITY SYSTEMS  →
WORLD 14 · INTELLIGENT SECURITY
Security that understands, not just records.
Most security infrastructure is a hard drive full of footage nobody watched. We build systems that interpret a site in real time and only speak when something matters.
PERIMETER — zones, fences and thresholds defined once; the model watches them permanently.
FIELD NOTES · HOW SECURITY LAYERS FAIL AND HOLD
NETWORK
APPLICATIONS
AI MODELS
AI IN SOFTWARE
PROMPT INJECTION
PERIMETER MONITORING · ACCESS & IDENTITY · ANOMALY BEHAVIOUR · ESCALATION RULES · PRIVACY & RETENTION
PERIMETER
INTRUSION
IDENTIFY
ESCALATE
RESOLVE
WORLD 15 · PRIVATE AI & INFRASTRUCTURE
Your intelligence should belong to you.
We deploy models inside your own environment — your servers, your network, your hardware. Nothing leaves the boundary. No third-party API sees your documents, your patients, your transactions or your factory floor.
ON-PREMISE DEPLOYMENTOpen-weight models served on your own GPUs, air-gapped if required.
PRIVATE KNOWLEDGERetrieval over internal documents with per-role access boundaries.
INFERENCE INFRASTRUCTUREServing, batching, quantisation, autoscaling, cost and latency budgets.
GOVERNANCE & AUDITLogging, evaluation, permissions, retention and full traceability.
DEPLOYMENT TARGETS
BOUNDARY SEALED
DATA EGRESS 0
GPU UTILISATION 68%
P95 LATENCY 240 MS
AUDIT LOG ON
MODEL TRAINING  →
WORLD 16 · MODEL TRAINING
A general model is a starting point.
We train models on your domain — your vocabulary, your documents, your images, your edge cases — until it handles your vocabulary, formats and edge cases the way a domain specialist would.
DATA — collection, cleaning, labelling and synthetic augmentation of the domain corpus.
FINTECH EXAMPLE: LABELLED TRANSACTIONS · FILINGS · RISK POLICIES
WHAT A PROJECT LOOKS LIKE
FINTECH · FRAUD MODEL
A large teacher model distilled into a small specialist that decides within a real-time budget — measured on held-out transaction backtests, not on demos.
DATA ANALYSIS · ANALYST AGENT
An agent fine-tuned on the warehouse schema and past queries. Every answer arrives with the SQL that produced it — so it can be checked, not believed.
DATA
FINE-TUNING
DISTILLATION
ALIGNMENT
EVALUATION
SIMULATED RUN
STEP 12,480
LOSS 0.412
DOMAIN EVAL 81.4%
BASE → SPECIALIST
WORLD 17 · DEEP SEARCH
Search that reasons before it answers.
Keyword search returns documents. Deep search returns an answer, built from many sources, each step defended by evidence you can open.
QUESTION DECOMPOSED → 4 SUB-QUESTIONS
RETRIEVING · INTERNAL CORPUS + OPEN WEB
CROSS-CHECKING SOURCES · CONFLICTS 2
SYNTHESIS · 11 CITATIONS ATTACHED
ANSWER · CONFIDENCE 0.91
Multi-hop retrieval across private corpora, filings, code, tickets, papers and the live web — returning a defended answer with its full evidence chain, not a list of links.
CODING AGENTS  →
WORLD 18 · CODING AGENTS
Software that extends itself.
Agents that read a codebase, plan a change, implement it, run the tests and explain what they did — inside your review process, not around it.
REPOSITORY UNDERSTANDINGArchitecture, conventions, dependencies and intent across the whole tree.
IMPLEMENTATION & TESTSFeature work, refactors, migrations — with tests written and executed.
REVIEW & GUARDRAILSDiffs, reasoning, rollback and human approval gates on every change.
WORLD 19 · EDGE AI
Intelligence where the event happens.
A cloud round-trip is too slow for a robot arm, a moving drone or a production line. We compress models until they run on the device itself — no network, no latency, no data leaving the machine.
QUANTISATION
PRUNING
DISTILLATION
EMBEDDED GPU / NPU
CAMERAS & SENSORS
ROBOT CONTROLLERS
OFFLINE OPERATION
REAL-TIME BUDGETS
OVER-THE-AIR UPDATES
CLOUD → DEVICE · TYPICAL SCALE
MODEL 34 GB → 780 MB
LATENCY 610 MS → 12 MS
NETWORK NOT REQUIRED
DEVICES ONLINE 148
TRANSMISSION
I am from , and I want to build .
AI AGENTS
PRIVATE AI
ROBOTICS
AUTONOMOUS DRONES
COMPUTER VISION
SECURITY SYSTEM
DEEP SEARCH
MODEL TRAINING
AI INFRASTRUCTURE
EDGE AI
SOFTWARE AGENTS
SIMULATION
RESEARCH PARTNERSHIP
TRANSMIT SIGNAL  →
READ BY A HUMAN WITHIN 24 HOURS · PUBLIC@NUJEXA.COM
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CARTOGRAPHY
Map of Intelligence
The whole lab is one system. Choose a node — the camera will travel to it.
INTELLIGENCE CORE
AGENTS
MEMORY
MULTI-AGENT
SIMULATION
MODELS
RESEARCH
DEPLOYMENTS
MULTIMODAL
CONTACT
PHYSICAL AI
ROBOTICS
DRONES
VISION
DETECTION
SECURITY
PRIVATE AI
TRAINING
DEEP SEARCH
CODING
EDGE AI
R-01 · NUJEXA RESEARCH · 2026
Long-Term Memory
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