15 Years of Engineering. Now Building the Intelligence Layer.
From mission-critical legacy .NET monoliths to autonomous multi-agent AI ecosystems - we architect, modernize, and engineer production software platforms for businesses that need technology to actually work.
Enterprise Production Metrics
What are you trying to solve?
Every business problem requires a distinct architectural approach. Choose your primary challenge to inspect proven solutions and blueprints.
Production Systems
Real-World Projects
Production platforms we have architected and deployed globally. From Fortune 500 conference systems to high-throughput multi-tenant SaaS.
Engineered for Concurrency, Reliability & AI Scale
From zero-downtime .NET legacy modernizations to deterministic multi-agent swarms - explore our battle-tested engineering disciplines.
Deterministic Multi-Agent Swarms & MCP Integration
LangGraph state-machine agents with tool-calling capabilities, guarded execution loops, and automatic failure recovery across SQL Server, SAP & REST APIs.
Strangler Fig .NET 9/10 Monolith Migrations
Deconstruct 15+ year ASP.NET WebForms and .NET 4.8 monoliths into microservices with zero downtime, preserved business logic, and 10x performance.
High-Availability Multi-Region Mesh
Autoscaling Kubernetes clusters, sub-50ms regional edge caching, and zero-trust IAM security across Azure and AWS.
Sub-5ms Query Latency & Sharded Pipelines
SQL Server deadlock elimination, PgBouncer sharding, TimescaleDB IoT sensor streams, and vectorless BM25 semantic caches.
Architectural Blueprints
Enterprise Solutions
Proven, reusable architecture patterns engineered to eliminate scaling bottlenecks, compliance hurdles, and high inference costs.
Live Production Systems & Enterprise Interfaces
Actual software platforms, AI runtimes, and architectural systems engineered and delivered by SHIVAM ITCS operating at scale across enterprise clients.
Agentic AI Engineering & Autonomous Execution Mesh
Autonomous AI & Agentic SystemsAutonomous multi-agent execution plane orchestrating specialized AI workers with deterministic tool calling, error mitigation loops, and state isolation.
Legacy Monolith vs. 2026 Intelligence Mesh
Drag the slider to see how SHIVAM ITCS transforms brittle 15-year enterprise monoliths into high-velocity AI platforms.
Kafka streams & LangGraph deterministic autonomous agents with tool-calling guardrails.
PgBouncer distributed connection pools, hybrid vectorless RAG & Redis semantic cache.
Single failure crashes entire system. Massive stored procedures with no unit test coverage.
Single-threaded blocking calls, slow database deadlocks & escalating hosting cloud costs.
Production Architecture Snippets
Explore actual C# .NET 9/10, LangGraph Python, and TypeScript retrieval patterns engineered by our principal architects.
01020304050607080910111213141516171819# SHIVAM ITCS - Deterministic Multi-Agent State Graph
from langgraph.graph import StateGraph, END
from pydantic import BaseModel, Field
class AgentState(BaseModel):
objective: str
tool_calls: list[dict] = Field(default_factory=list)
guardrail_passed: bool = False
def supervisor_router(state: AgentState):
"""Decomposes goal and delegates to worker agent nodes."""
if not state.guardrail_passed:
return "compliance_guard_node"
return "sql_database_executor"
workflow = StateGraph(AgentState)
workflow.add_node("supervisor", supervisor_router)
workflow.add_edge("compliance_guard_node", "supervisor")
app = workflow.compile()
.NET 9/10
Next.js 16
React 19
TypeScript
Angular (2-20)
Framer Motion
Python 3.12
FastAPI
Azure Cloud
AWS Cloud
Google Cloud (GCP)
Kubernetes
Docker
Power Platform
LLM Orchestration
LangGraph
Model Context Protocol (MCP)
Semantic Kernel
LangChain
PostgreSQL
pgVector
MS SQL Server
Redis Cluster
Apache KafkaGlobal Engagement Models
Services & Delivery
Flexible partnership frameworks tailored for enterprises needing rapid architecture validation, dedicated engineering pods, or organizational AI rollouts.
One Company. Many Generations of Technology.
"We didn't arrive at AI yesterday. We evolved into it." Explore our 18-year architectural progression from monolithic ASP.NET systems to modern autonomous AI execution layers.
Agentic AI & Next-Gen Systems
Autonomous Agent Swarms, Hybrid RAG & Edge Rendering
Autonomous goal-directed agent swarms, sub-second hybrid RAG pipelines, and edge-rendered frontends.
Architecture Intelligence Explorer
Hover and inspect each layer of our modern full-stack topology - from edge Next.js clients down to asynchronous Kafka message buses and AI inference routers.
AI Orchestrator & RAG Engine
Dynamic prompt triage, hallucination guardrails, source citation attribution, and tool calling.
Prevents vendor lock-in, slashes token bills by 40%+, and guarantees exact factual citations.
Semantic embedding cache in Redis reducing repeat inference load by up to 35%.
PII redacting filters, prompt injection sanitization, and strict egress API boundaries.
The AI Engineering Lab
Explore how we engineer high-precision vectorless BM25 retrieval, stateful multi-agent meshes, and token-cost optimizers.
Vectorless BM25 vs Hybrid RRF Architecture
Test and evaluate query fidelity, token cost reduction, and sub-45ms execution metrics.
$ Optimized Cost
Requires high-dimensional vector hosting.99.1% Recall
Lexical indexes prevent embedding drift on specialized part codes.< 90ms
Sub-second streaming direct to user chat and search interfaces.Comprehensive Engineering Mastery
We organize our 18 years of technical mastery into six dedicated disciplines spanning architecture, modernization, cloud, and artificial intelligence.
Product Engineering
From Idea → Architecture → Scalable Production Product
We engineer robust multi-tenant SaaS platforms, responsive web portals, and cross-platform mobile apps architected for high concurrency, security, and sustained business growth.
AI Engineering & LLMs
Production-Grade Intelligence That Executes, Not Just Chats
We design production LLM middleware, autonomous multi-agent meshes, hybrid RAG systems, and custom tool-calling agents integrated directly into your core business database and workflow.
Automation Engineering
Turning Manual Friction Into Deterministic Execution Pipelines
We build event-driven workflow automation pipelines, document processing engines, and human-in-the-loop task orchestrators that eliminate operational overhead and latency.
Cloud & Systems Architecture
Resilient Distributed Infrastructure Engineered for Scale
We architect cloud-native distributed backends on Azure and AWS utilizing Kubernetes, containerization, microservices, and geo-distributed caching to maintain 99.99% availability.
Legacy Modernization
Evolving Mission-Critical Systems Without Unnecessary Disruption
15+ years of deep domain knowledge allows us to deconstruct monolithic ASP.NET / SQL Server architectures and incrementally modernize them into high-performance .NET 9/10 cloud APIs and modern web frontends.
Data & Database Intelligence
High-Integrity Data Foundations for High-Speed Reasoning
We engineer relational, document, time-series, and vector database architectures optimized for sub-millisecond query performance, multi-region replication, and compliance auditing.
From Legacy Core to Autonomous Intelligence
How we evolve 15-year-old monolithic ASP.NET & SQL Server codebases into modern cloud APIs and AI-native workflows.
Monolithic Core
ASP.NET WebForms / MVC, monolithic SQL Server databases, and on-premises client-server operations.
Cloud & Micro-APIs
Strangler Fig deconstruction into .NET Core microservices, Next.js web applications, and Kubernetes clusters.
AI & Autonomous Systems
Multi-LLM orchestration, hybrid RAG knowledge engines, and autonomous tool-calling agents embedded in workflows.
"We don't throw away working systems. We evolve them."
Unlike agencies that insist on rewriting business codebases from scratch (and introducing massive downtime risks), our 18 years of accumulated engineering knowledge lets us modernize mission-critical systems incrementally. We wrap legacy transactional cores in high-speed .NET 9/10 microservices, deploy modern Next.js interfaces, and embed AI intelligence without interrupting your daily business operations.
One Shared Ideology. Three Specialized Perspectives.
Legacy modernization is not just code translation - it requires deep-tech runtime mastery, disciplined 7 R's delivery methodologies, and battle-tested production proof. Cross-navigate between our technology blueprint, service engagement model, and enterprise ERP case study below.
.NET 10 & Strangler Topology
Deep CLR architecture, YARP reverse proxy gateways, C# 13 microservices, and AI-assisted dependency extraction.
7 R's Modernization Service
Methodical 4-phase transformation delivery pods with guaranteed zero downtime and automated regression testing.
Manufacturing ERP Deep-Dive
16-week production migration of a 3-million-line legacy .NET Framework 4.8 monolith without a single second of factory downtime.
No risky big-bang rewrites. We place YARP reverse proxy gateways in front of legacy monoliths and incrementally cut over microservices.
Retire, Retain, Rehost, Replatform, Refactor, Rearchitect, and Rebuild. We optimize for highest business ROI with lowest risk.
Dual-running synchronization and shadow traffic verify state consistency before switching live factory or financial transactions.
Led by co-founders Ajay & Vijay Paliwal with continuous hands-on mastery from VB6/COM to modern containerized .NET 10 on Linux.
Industry & Business Domains
Explore typical architectural bottlenecks, proven system topologies, and high-impact AI opportunities engineered across core enterprise sectors.
Healthcare & HealthTech
HIPAA-Compliant Platforms, Clinical Portals & Medical AI
50+
Hospital-OS Platform100%
Zero Cloud Data Egress70%
Ambient Medical AI99.99%
High-Availability TopologyFragmented patient records, manual ICD-10 billing code errors, strict HIPAA compliance liabilities, and clinician burnout from administrative documentation.
Model Context Protocol (MCP) Server Configurator
Select enterprise data sources, cloud ERPs, and LLM swarms to generate a production-ready C# / .NET 9/10 MCP topology schema.
4 Connected Mesh Nodes
{
"mcpServers": {
"shivam-enterprise-mesh": {
"command": "dotnet",
"args": [
"run",
"--project",
"./src/Shivam.McpServer.csproj"
],
"env": {
"SHIVAM_ROUTING_MODE": "hybrid_commander",
"MAX_CONCURRENCY": "50000",
"CONNECTORS_ACTIVE": [
"postgresql",
"sap-erp",
"claude-sonnet",
"qwen-local"
]
},
"tools_exposed": [
{
"name": "sap-erp_bridge",
"protocol": "OData v4 / RFC",
"sla_latency": "< 35ms"
},
{
"name": "postgresql_bridge",
"protocol": "mTLS Pooler",
"sla_latency": "< 4ms"
},
{
"name": "claude-sonnet_bridge",
"protocol": "Anthropic API",
"sla_latency": "< 350ms"
},
{
"name": "qwen-local_bridge",
"protocol": "Local vLLM / OpenAI API",
"sla_latency": "< 45ms"
}
]
}
}
}Type-Safe C# .NET 9/10 & TypeScript Implementation • SOC 2 Ready
Research & Architectural Notes
Pragmatic engineering insights on vectorless RAG, stateful multi-agent systems, and zero-downtime .NET legacy modernizations.
Vectorless RAG vs Vector RAG: Achieving 10x Lower Costs & 99% Exact Recall
Why brute-force vector embedding searches fail on strict part numbers, legal clauses, and domain acronyms - and how BM25 lexical expansion paired with cross-encoder reranking solves it at a fraction of the cost.
›Dense embeddings struggle with exact lexical matches (part codes, SKU numbers, legal references).
›BM25 + structured metadata filtering delivers sub-100ms retrieval without $1000+/mo vector database bills.
Designing Production-Grade AI Agents: Beyond Fragile Chatbot Demos
A pragmatic engineering framework for deterministic state management, sandboxed tool execution, loop prevention, and human-in-the-loop interruption in production autonomous agents.
›Unconstrained agent loops are a massive operational risk in enterprise production environments.
›Explicit state machine graphs (LangGraph/Temporal) prevent catastrophic hallucination loops.
Modernizing 15-Year-Old ASP.NET Systems to .NET 9/10 Without Downtime
The battle-tested Strangler Fig pattern for migrating monolithic .NET Framework systems into high-speed .NET 9/10 microservices while keeping 24/7 business operations active.
›Big-bang rewrites fail in 80% of legacy projects; incremental strangler facades succeed.
›YARP (Yet Another Reverse Proxy) routes live traffic seamlessly between old ASP.NET and new .NET 9/10 modules.
Multi-LLM Orchestration: Optimizing Token Economics and Latency
How to build intelligent model routers that triage incoming prompts between frontier models (GPT-4o, Claude 3.7 Sonnet) and fast local mistral weights, slashing token bills by 40%+.
›70% of enterprise user queries do not require frontier model reasoning capacity.
›Lightweight classifier models can triage prompt complexity in under 20ms.
Hybrid LLM Routing: A Practical Cost Optimization Guide
A pragmatic, production-verified blueprint detailing how to pair cloud frontier models (Claude 3.7 Sonnet, GPT-4o) with self-hosted small models (Qwen 2.5, Mistral) to achieve up to 70% cost reduction without sacrificing output fidelity.
›Token economics breakdown: why 80% of enterprise extractions belong on sub-cent local models.
›Building zero-latency semantic classification proxies that categorize intent in under 15ms.
From Legacy to Agentic: A .NET Modernization Playbook
The definitive architectural guide to modernizing monolithic ASP.NET WebForms and .NET Framework 4.x legacy codebases into event-driven .NET 9/10 microservices augmented with Microsoft Semantic Kernel.
›The Strangler Fig pattern in practice: using YARP reverse proxies to decouple monolithic routes live.
›Automating schema normalization and dual-write synchronizations on enterprise SQL Server databases.
Building Multi-Agent Systems with MCP Protocols
An in-depth engineering analysis of implementing Anthropic’s Model Context Protocol (MCP) and Agent-to-Agent (A2A) specifications to enable secure, standardized tool discovery and inter-agent coordination.
›How MCP decouples tool execution from model prompts, eliminating prompt bloating and security risks.
›Designing stateful agent handoffs using cyclic graph state machines (LangGraph) and contract validation.
Enterprise AI & Agentic Readiness Assessment
Evaluate your data architecture, model routing, and security guardrails to receive an instant maturity score and tailored implementation roadmap.
How is your enterprise data organized and accessible for AI models?
Frequently Asked Questions
Direct answers to architectural, technical, and operational questions.
Ready to Build Your Enterprise Intelligence Layer?
Book an architectural discovery sprint or schedule an engineering consultation with our principal architects. Turnkey modernization, multi-agent swarms, and vectorless RAG architectures.