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AI-powered logistics app development focused on smarter routing, real-time visibility, automation, and future-ready supply chains.

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Route leads, control vendor access, and close every deal on WhatsApp CRM software.

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MACH platforms engineered for enterprise growth and operational efficiency.

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Single Dart codebase for iOS, Android, and web for Enterprise-grade.

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Robust mobile apps with native performance using React Native technology.

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Move AI models from prototype to production with scalable deployment.

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Custom AI agents for intelligent automation.

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Build secure, scalable AI chatbots with RAG and smart integrations.

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Advanced RAG with hybrid retrieval, reranking, and agentic AI.

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Hermes – Enterprise Agentic AI Integration and Model Deployment Platform

Nous Research engineered the Hermes model family to support advanced function calling and structured reasoning. Junkies Coder developed the enterprise runtime integration, providing containerized code sandboxes, secure multi agent orchestration, and automated schema alignment. By deploying this solution in secure private clouds, organizations can launch autonomous software agents that execute complex tool calls. This setup guarantees that enterprise data remains completely private while maximizing agent utility.

Industry

Artificial Intelligence

Platform

Web & Enterprise API

Location

New York, USA

Outcomes

Production Ready Runtime

Consult with AI engineers

Hermes – Enterprise Agentic AI Integration and Model Deployment Platform

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Key Platform Features

Structured JSON Output and Schema Alignment

Enforces deterministic JSON output structures that align with predefined schema models, ensuring that the model output is directly consumable by enterprise backend systems without parsing failures.

Sandboxed Code Interpreter Runtime

A secure, containerized sandbox environment where the agent can write and execute code dynamically to solve data manipulation tasks, completely isolated from core system services.

Multi Agent Orchestration Pipeline

A coordination layer supporting multiple specialized agents working together, allowing planner, executor, and verifier agents to resolve complex enterprise tasks collaboratively.

Enterprise Tool Registry and Access Guardrails

A secure database registry defining allowed functions and system calls, backed by strict token limits and permissions to prevent unauthorized model behavior.

Our Services Provided

Agentic Architecture and Tool Design

We designed a robust system architecture defining how the agent interfaces with database systems, code interpreter environments, and external enterprise API gateways.

Orchestration and Sandbox Engineering

Our team engineered a secure, lightweight containerized sandbox runtime allowing the model to safely execute dynamically generated Python code without compromising infrastructure security.

Model Optimization and Local Deployment

We optimized Hermes inference pathways using vLLM and TensorRT LLM, deploying the system in secure private cloud networks to ensure absolute compliance with data protection policies.

End to End QA and Agent Verification

We developed comprehensive test suites to evaluate agent behavior, validating tool call accuracy, path planning reliability, and self correction success across hundreds of concurrent user sessions.

Challenges Faced in Deploying Agentic LLMs

Unreliable Function Calling and Hallucinations

Standard large language models often struggle to consistently execute complex function calls, leading to execution errors, parameter hallucinations, or invalid JSON structures that break production API integrations.

Security Risks of Untrusted Code Execution

Autonomous agents require the ability to run code in real time to solve math, process data, or interact with external tools, raising critical security risks if executed outside air gapped, sandboxed runtimes.

High Latency and Compute Overhead

Orchestrating multi agent workflows involves multiple LLM calls and self correction loops, compounding application latency and token costs if the serving infrastructure and model parameters are not highly optimized.

Data Sovereignty and Compliance Mandates

Regulated enterprises cannot send proprietary operational data to third party cloud APIs, requiring the agentic model family to be deployed locally or in private clouds while maintaining zero data leakage.

Tech Stack

Python

vLLM

LangChain

Docker

Kubernetes

PyTorch

Hermes – Enterprise Agentic AI and Function Calling Platform | Junkies Coder

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Delivered

Project Results

Here are the measurable outcomes and achievements we delivered for this project.

Key Outcomes

Production Ready Agentic Model Deployment

Delivered a fully integrated, containerized deployment framework for Hermes, enabling the client to run autonomous software agents with reliable function calling pipelines.

Frictionless Schema and API Integrations

Achieved zero JSON parsing failures by enforcing strict schema output alignments, allowing the model to interact reliably with payment gateways and inventory databases.

Secure Isolated Code Execution

Established a low latency sandboxed interpreter runtime that successfully isolates dynamic code execution, eliminating security vulnerabilities while preserving agent utility.

Enterprise Scale Compliance and Privacy

Enabled private cloud deployments that eliminate dependencies on public APIs, providing complete data sovereignty and satisfying strict financial compliance regulations.