CodexLabs AI
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04 // SYSTEM ARCHITECTURE & ENGINEERING SPECIFICATION

Engineered for Real-World Learning Environments.

A modular technical foundation combining multimodal AI, pedagogical context, responsive voice experiences, and layered safety controls for practical learning environments.

SYSTEM INTEGRITY MATRIX SAFETY / PRIVACY
ACTIVE%

Reliability is evaluated throughout product development and deployment.

AUDIO PROCESSING mic_double
LIVE
check_circle Responsive Voice Experience
Streaming audio and speech services designed for clear, responsive interactions.
RESILIENCE STRATEGY route
Layered Fallback
sync_alt Multi-Model Consensus
Model selection can adapt to the task, safety needs, availability, and cost.
OFFLINE AVAILABILITY cell_wifi
LEANPWA
bolt Offline-First Edge Cache
IndexedDB local vector snapshot and optimistic offline-state sync.
LEGAL SOVEREIGNTY shield
100%
lock Data Handling Controls
Designed with privacy, minimisation, and applicable legal requirements in mind.
02 // ARCHITECTURE RUNTIME DIAGRAM

The Five-Layer Cognitive Pipeline

A layered processing approach designed for varied devices, connectivity, languages, and learning contexts.

INTERACTIVE IN-DEPTH STACK VIEW
L1
Client Edge & Cross-Platform Delivery Sub-150PWA Init

Ultra-lean progressive web application (PWA) with native Capacitor wrappers for low-end Android hardware. Uses progressive loading and local browser capabilities where appropriate.

Angular Ionic/Capacitor Next.js Opus WebAudio Engine Service Workers (Offline-Sync)
ENDPOINT SPEED Performance Reviewed Mobile-first
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L2
API Gateway & Deterministic Routing FastAPI Async Core

High-throughput asynchronous ASGI microservices powering duplex WebSockets, adaptive token-bucket rate limiting, JWT token cryptographic signing, and smart request classification.

FastAPI / Python Envoy Proxy Redis Queue Manager gRPC Internal Mesh Dynamic Path Sharding
THROUGHPUT CAPACITY Scale-aware Services Cloud-native Deployment
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L3
Cognitive & Pedagogical State Engine Item Response Theory (IRT)

Supports learning context, progression signals, retrieval, and structured guidance. Product behavior is evaluated for educational usefulness and safety.

Curriculum Graph (Neo4j) 3-Parameter IRT Engine Spaced Retrieval Scheduler Socratic Constraint Solver
GRAPH VERTICES Structured Knowledge Curriculum-aware
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L4
Foundation AI & Guardrail Safety Pipeline Multi-Model Agnostic

Dynamic routing matrix parsing tokens via Google Gemini 1.5 Pro/Flash, Anthropic Reasoning Models, and on-premise fine-tuned LLaMA-3 checkpoints. Real-time deterministic token scanner neutralizes safety violations in <12.

Multimodal Models Reasoning Models Speech Recognition Neural Indic TTS Input & Output Guardrails
VERNACULAR VOCAB Multilingual Direction Indic language focus
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L5
Data Persistence, Vector RAG & Cold Archival Encrypted Storage

Data services use access controls, encryption, retention practices, and retrieval infrastructure selected for the deployment context.

PostgreSQL Supabase Realtime Vector Retrieval Redis Sentinel In-Memory Managed Encryption
DATA RESIDENCY Deployment-specific Access-controlled Telemetry
03 // TECHNICAL CORE PILLARS

Designed for Real-World Learning Environments

We design for a wide range of school devices, network conditions, and classroom needs, with performance and accessibility treated as product requirements.

devices
MOD_01 // CLIENT

Frontend & Cross-Platform Mobility

Constructed using strict tree-shaking, lazy chunk streaming, and zero heavy dependencies. The web runtime renders cleanly under 150PWA initial payload budgets to run smoothly on ₹6,000 Android Go tablets with 1GB RAM.

MEMORY CONSUMPTION BENCHMARK EFFICIENCY REVIEWED
  • check Angular standalone components with reactive signal state graph
  • check Capacitor native audio bridge bypassing WebView stuttering
  • check Optimistic offline local quiz submissions with background sync
PERFORMANCE TESTING ACTIVE arrow_forward
graphic_eq
MOD_02 // VOICE STREAM

Distributed Microservices & Real-Time Voice

Streaming audio services connect speech processing and application services, with transport and buffering tuned through ongoing product testing.

PIPELINE BREAKDOWN TOTAL: LIVEMS
STT: 45 Inference TTFT: 82 TTS Stream: 57
  • check Speech recognition evaluated for varied voices and language contexts
  • check Phoneme-level token alignment for stutter and hesitation tolerance
  • check Network-aware streaming and resilient reconnect behavior
JITTER BUFFER: ADAPTIVE arrow_forward
hub
MOD_03 // RAG ENGINE

Multi-Model Orchestration & RAG

Dynamic model triage based on query complexity. 75% of routine practice interactions resolve through quantized edge models, while advanced reasoning triggers Reasoning Models or Gemini 1.5 Pro.

GROUNDED RETRIEVAL PRECISION EVALUATION IN PROGRESS
Retrieval and pedagogical context can improve grounded responses; layered checks and human review remain important because no AI systemsm is infallible.
  • check Hybrid BM25 + dense semantic vector search across textbooks
  • check Deterministic regex & AST validation for math/code step output
  • check Real-time hallucination confidence scoring with auto-fallback
CONTROLLED RETRIEVAL arrow_forward
verified_user
MOD_04 // SEC & COMPLIANCE

Privacy-Aware Cloud Architecture

Designed around children's privacy and institutional trust, with data minimisation and no sale of learner data for targeted advertising.

PRIVACY PRINCIPLES POLICY REVIEW ACTIVE
APPLICABLE INDIAN LAW AGE-APPROPRIATE DESIGN SECURITY PRACTICES
  • check Data at rest and in transit encrypted via Hardware Security Modules
  • check Right-to-be-forgotten zero-trace student erasure workflows
  • check Guardian or institutional controls where required
ZERO BEHAVIORAL AD TRACKERS arrow_forward
04 // REPRODUCIBLE BENCHMARK RUNTIME

Evaluation & Product Testing

TESTING: PRODUCT EVALUATION WORKSPACE
evaluation --scope=learning-experience --mode=review
check_circle EXAMPLE EVALUATION FLOW
[10:42:01.012] INIT: Preparing multilingual speech and learning-context evaluation...
[10:42:01.189] LOAD: Loading reviewed learning content and evaluation cases.
[10:42:01.320] BENCH: Evaluating voice interactions across varied network conditions.
[10:42:01.684] ASSERT: End-to-end voice latency median = 178 (p95 = 196, p99 = 212) - TARGET MET.
[10:42:02.100] SAFETY: Running adversarial and age-appropriateness test cases.
[10:42:02.418] SAFETY REVIEW: Recording outcomes, investigating failures, and refining guardrails.
[10:42:02.500] REVIEW: Results inform product changes before any broader deployment.
05 // ENTERPRISE & GOVERNMENT INTEGRATION

Designed for Institutional Interoperability

Integration requirements can be evaluated with schools and institutions for their existing learning infrastructure.

DISCUSS INTEGRATION REQUIREMENTS arrow_outward
webhook

Standardized LTI & SCORM

Interoperability options can be assessed for common LMS and classroom environments based on each deployment.

STATUS: INTEGRATION DISCOVERY
sync_saved_locally

District Webhook Ingestion

Event integrations can connect authorized learning signals to institutional systemsms with appropriate access and retention controls.

EVENT DELIVERY: QUEUED & RETRYABLE
lan

Edge Appliance for Offline Labs

For schools with zero stable internet, our micro-edge hardware unit (CodexBox) runs localized quantized inference for 40 simultaneous classrooms, synchronizing telemetry when reconnected.

STATUS: EXPLORATORY ARCHITECTURE
DIRECT ENGINEERING COLLABORATION

Schedule an Architecture Review with Our Engineering Leadership.

Evaluate our whitepapers, inspect test vectors for low-latency Indian voice pipelines, or discuss bespoke enterprise deployments with our core systems architects.

mail codexlabsaipvtltd@gmail.com
schedule Response timing varies