SYSTEMS // ECOSYSTEM ARCHITECTURE

Technology Stack & Protocols

Organized strictly by research disciplines. We build on open protocols, verifiable standards, and sandboxed execution layers rather than proprietary lock-in.

// 01 ARTIFICIAL INTELLIGENCE

Probabilistic modeling and deterministic decision engines.

Machine Learning Runtimes
Agentic Planning Loops
Long-Context Transformers
Model Evaluation Frameworks
Loss Boundary Analysis
Episodic Vector Indexing

// 02 SYSTEMS & MCP

Communication interfaces, tool dispatch, and distributed runtimes.

Model Context Protocol (MCP)
Typed Tool Calling Schemas
WebAssembly (Wasm) Sandboxing
High-Concurrency RPC Routers
Streaming AST Parsers
Context Cache Sharding

// 03 SECURITY ARCHITECTURES

Defensive telemetry, containment gates, and formal auditing.

AI Agent Privilege Gating
Attack Surface Topology
Static Code Vulnerability Scanning
Protocol Handshake Analysis
Cryptographic Identity Tokens
Responsible Disclosure Pipelines

// 04 HARDWARE & SILICON

Physical electronic primitives, signal flow, and EDA modeling.

Analog Signal Circuit Models
Sub-Threshold Transistor Physics
SPICE Netlist Simulation
Open-Source Silicon Toolchains
Continuous Waveform Generators
Compute-In-Memory Crossbars

PROTOCOL STANDARD

Why Swadeshi AI Champions the Model Context Protocol (MCP)

The Model Context Protocol (MCP) establishes an open, vendor-neutral standard for connecting AI systems directly to data sources and execution environments. By decoupling tool implementations from the core LLM inference provider, MCP enables Swadeshi AI agents to seamlessly inspect codebases, execute hardware simulations, and enforce cryptographic permission boundaries at the transport layer.

MCP CLIENT ↔ [TYPED JSON-RPC 2.0 OVER STDIN/SSE] ↔ MCP SERVER (TOOLS / RESOURCES / PROMPTS)