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Work That Shipped, then Scaled
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Industries
Automotive
We Serve to Vehicles, EV, Fleet & CKD.
Lifestyle
Apps for social, music, fitness & on-demand platforms.
Media & Entertainment
Explore top media and entertainment companies shaping the future of content and digital entertainment.
Manufacturing
Produces goods from raw materials or components.
Adult Entertainment
Digital platforms powering streaming, subscriptions, and modern entertainment experiences.
Real Estate
Smart real estate apps that simplify property discovery, virtual tours, lead management, and seamless transactions.
Healthcare
System improves physical and mental well-being.
Agriculture
The tech, science, and practice cultivate AgriTech.
FinTech
For digital banking, payments, compliance, and embedded finance Apps.
Retail & E-Commerce
Sells products to customers through physical stores and online platforms.
Logistics
AI-powered logistics app development focused on smarter routing, real-time visibility, automation, and future-ready supply chains.
Service
Custom Software Development
Custom Software Development
Enterprise-grade custom software for seamless integration and sustainable growth.
Legacy Application Modernization
Cloud migration, microservices, and secure refactoring to modernize legacy systems.
SaaS Application Development
Full-stack SaaS with multi-tenant architecture, AI integration, and cloud-native deployment.
Business Automation
Social Media Automation
Ship lifecycle campaigns, recover abandoned carts, and personalize every send with AI.
Email Automation Services
Ship lifecycle campaigns, recover abandoned carts, and personalize every send with AI.
Whatsapp CRM Software
Route leads, control vendor access, and close every deal on WhatsApp CRM software.
Mobile App Development
Mobile App Development Services
Trusted mobile app development company building custom Android and iOS apps.
iOS App Development
A results-driven iOS app development company building secure, scalable apps.
Android App Development
Innovative, scalable Android applications tailored to your business requirements.
Cross-Platform App Development
High-performance apps that run across iOS, Android, and web platforms.
Progressive Web App Development
Custom PWA development company engineering app-grade performance across all browsers.
MVP Development Services
Build faster, validate smarter, and scale with confidence.
Software Development
Software Integration Development
Real-time integration of enterprise apps, data systems, and 3rd-party services.
Software Development Outsourcing
Full-cycle engineering partnership from ideation to deployment and AMC.
Enterprise Software Development
MACH platforms engineered for enterprise growth and operational efficiency.
Cross Platform App Development
Flutter App Development Company
Single Dart codebase for iOS, Android, and web for Enterprise-grade.
React Native App Development
Robust mobile apps with native performance using React Native technology.
AI Development
AI Development Services
Enterprise AI solutions that enhance efficiency and data-driven growth.
Enterprise AI Product Deployment
Move AI models from prototype to production with scalable deployment.
Agentic AI Engineering Services
Production-ready AI agents built for real business needs.
Generative AI Development
Enterprise GenAI solutions using LLMs, RAG, fine-tuning, and AI agents.
AI Agent Development
Custom AI agents for intelligent automation.
AI Chatbot Development Services
Build secure, scalable AI chatbots with RAG and smart integrations.
RAG Development Services
Advanced RAG with hybrid retrieval, reranking, and agentic AI.
Hire
Mobile
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AI & Data
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Cloud & DevOps
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Full Stack
Hire React.js Developer
Hire WordPress Developer
Hire Shopify Developer
Hire MERN Stack Developer
Hire Golang Developer
Hire Rust Developer
Hire Angular Developer
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Blockchain
Hire Solana Developer
Hire Solidity Developer
Hire NFT Developer
Hire Web3 Developer
Insights
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Listicles
Curated lists of top Apps companies in 2026
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Latest Market News and Updates!
53% of executives report that their AI initiatives are derailed by legacy system integration challenges rather than the quality of the underlying AI model. A model that performs well in a data science notebook is a different challenge from serving reliable, real-time predictions to thousands of users in a production environment. Junkies Coder helps enterprises deploy AI-powered products by diagnosing and fixing the specific infrastructure blockers holding projects back, including data pipeline architecture, model serving, legacy integrations, and scaling challenges, without requiring a multi-year enterprise transformation programme.

Model to Production in Weeks
MLOps Built In, Not Bolted On
Empowering awards, featured in news, and recognition to Drive Innovation and Success with
our unparalleled expertise and commitment to excellence.
10+
Years of experience
15+
Countries Served
25$
Average cost P/H
95%
Positive Feedbacks
500+
Projects delivered
50+
Experts & Engineers
CORE FEATURES
End-to-end deployment engineering from infrastructure diagnostics and model serving architecture to MLOps implementation and post-launch monitoring, so an accurate model actually becomes a product your users depend on.
Deployment Readiness Diagnostics
Model Serving and Scaling Architecture
Real-Time Data Pipeline Integration
MLOps and CI/CD for Machine Learning
Governance, Audit Trails and Compliance
Drift Detection and Performance Monitoring
The SHIP Standard scopes a specific fix over generic infrastructure: S: Scope Blocker; H: Harden Infrastructure; I: Increment Rollout (gradual release with rollback); P: Performance Monitoring (track accuracy, cost, latency, and system health).
The specific gap, serving latency, legacy integration, data access, is identified and confirmed with you before any infrastructure work is proposed or costed.
Serving infrastructure is sized against your actual expected traffic, not a generic template, with auto-scaling and cost controls built in from the start.
The AI product ships to limited traffic first, with a tested rollback path available at every stage before full production exposure.
Accuracy, latency, and cost are monitored continuously from launch, not added only after a problem is reported by users.
Documentation and internal training are delivered as part of the engagement, so your team can operate and extend the system without ongoing dependency on us.
Every deployment in the portfolio is live, serving users at the targeted load and compliance standard. E.g., a Healthcare AI model deployed into an EHR system within 9 weeks via governed API integration, eliminating manual data entry workflows.

Industry
Agro Logistics
Technology
Web / IoT / ERP
Location
India
Outcomes
50% Reduction
Vaishnodevi Agro Resources Pvt. Ltd needed a digital tracking system to replace manual agro-logistics processes in Radhanpur, India, covering truck entries, weighbridge, seed processing, lab reporting and dispatch management.

Industry
Manufacturing
Platform
Corporate Website & E-Commerce
Location
Dammam, Saudi Arabia
Outcomes
Unified Digital Platform
Arnon Plastic Industries needed a modern digital presence to match its scale as a 25-year manufacturing leader, and Junkies Coder delivered a corporate website with an integrated product catalogue, dual service-line navigation, and a dedicated e-commerce store, giving Arnon a unified platform across its food packaging and building insulation divisions.

Industry
Artificial Intelligence
Platform
Web & Enterprise API
Location
New York, USA
Outcomes
Production Ready Runtime
Nous Research developed the Hermes model family, featuring advanced agentic reasoning and complex function calling capabilities. Junkies Coder engineered the enterprise integration runtime and secure sandbox environment for Hermes, enabling businesses to deploy autonomous software agents within secure, production ready workflows.
Whether you're looking to develop a digital solution from scratch, scale your current offerings, or fully modernize your system, we are here to help.
OUR EXPERTISE
A model that scores well in testing is a different problem from a model serving real users reliably. Junkies Coder runs a mandatory blocker diagnostic before any infrastructure work begins, so you're paying to fix the actual gap, not a generic rebuild.
We diagnose your specific deployment blocker before proposing any fix, so your AI initiative reaches real users on infrastructure actually built to support it.

Beyond standard deployment setup, we bring engineering depth in the advanced capabilities enterprise clients need as they scale from a first deployment to organization-wide AI operations.
01
Serving infrastructure architected across regions with automatic failover, so a single region outage doesn't take your AI product down.
02
Low-latency serving architecture built for use cases like fraud detection and live personalization, where a delayed prediction loses its value.
03
Every model version deployed is tracked and reversible, so a bad update can be rolled back safely without downtime while it's diagnosed.
04
GPU allocation matched to actual inference demand, avoiding both under-provisioning latency and over-provisioning budget waste.
05
Logging that captures what data informed a decision, what the model output, and whether a human reviewed it, built for regulated industries.
06
One deployment layer serving AI capability consistently across web, mobile, and internal tools, instead of separate builds for each surface.
Deployment reliability matters most where decisions are automated, data is sensitive, and the cost of a failed rollout is immediate and measurable.
AI deployment isn't a standard software rollout. It requires infrastructure diagnosis, staged load testing, and MLOps discipline that a generic DevOps process doesn't cover. Ours is built around that reality.
We assess your current infrastructure, data pipelines, and the specific blocker preventing production deployment, rather than assuming a full rebuild is required.
We design the specific fix, serving layer, real-time pipeline, or legacy connector, scoped to your actual gap rather than a generic enterprise template.
We implement serving infrastructure and the real-time data access layer your product needs, connecting to existing systems through governed APIs.
We implement CI/CD for machine learning, versioning, automated testing, safe rollback, and the audit logging your industry's compliance requirements demand.
We deploy incrementally, monitoring real performance at limited scale before expanding to full production traffic.
We implement ongoing drift and performance monitoring, and support internal training so your team trusts and adopts the deployed system.
Shalehin Modasia
Marketing DirectorENGAGEMENT MODELS
Deployment engagements look different depending on whether you have one specific blocker, need a full serving infrastructure build, or are embedding AI across multiple products. We offer three engagement structures for each.
A time-boxed assessment identifying the exact blocker preventing your AI product from reaching production, with a scoped fix plan, timeline, and price at the end.
Book a Free DiagnosticAn embedded team building and operating your full model serving, data pipeline, and MLOps infrastructure for organizations deploying AI across multiple products.
Talk to Our TeamA focused sprint engagement to get one specific, already-built AI model integrated into an existing product and live in production quickly.
Scope Your SprintReal stories from real partners who experienced clarity, accountability, and measurable business growth.
We select serving infrastructure, MLOps tooling, and monitoring based on your existing stack, data sensitivity, and compliance environment, not a default cloud-only assumption.
Featured Technologies
OpenAi
Claude
Falcon
Gemini
Mistral
Grok
Meta
Regulated industries expect audit trails and governance controls around automated decision-making. Junkies Coder builds this into the deployment architecture itself, not as a pre-audit retrofit.

GDPR
ISO 27001

PCI-DSS
SOC 2
CCPA
HIPAA
FISMA

Data Protection Act

AI Ethics Guidelines
NIST

IEEE

AI EU Act

Explainable AI
ISO 9001

ISO 42001
Most deployment vendors sell infrastructure capacity. We deliver production reliability. The difference is a mandatory blocker diagnosis before any architecture work begins, and a fix scoped to that blocker instead of a platform-wide commitment you didn't ask for.
We identify the specific gap, serving latency, data access, legacy integration, before proposing any infrastructure work, so you're never paying for a rebuild you didn't need.
We build on your existing cloud and tooling rather than requiring you to adopt a new platform ecosystem to get your AI product deployed.
We deploy incrementally to limited traffic first, catching performance issues at manageable scale before your full user base ever sees them.
Auto-scaling and model quantization are built into the architecture from the start, so inference costs match actual usage instead of becoming a post-launch surprise.
Audit trails and explainability logging are designed into the system itself, not added before a compliance review.
We support internal training so your team trusts and actually uses the deployed system, technical success without adoption doesn't deliver business value.

Model serving is the infrastructure layer (e.g., vLLM, NVIDIA Triton) that hosts a trained machine learning model, allowing it to receive input data and return predictions in real time to end users.
Focused deployment engineering sprints, targeting a specific architectural blocker like a legacy API gateway, typically complete in 4 to 9 weeks depending on complexity.
Yes, our core expertise is taking models that already exist (in a notebook or pilot phase) and engineering the robust pipeline required to put them into live production.
Cloud deployment offers elastic GPU scaling for variable traffic, while on-premise (or air-gapped) deployment provides absolute data sovereignty and security for highly regulated industries.
We embed model quantization (INT8/INT4), intelligent KV caching, and dynamic GPU auto-scaling into the deployment architecture to drastically optimize compute usage.
Absolutely. We heavily utilize Kubernetes (EKS, AKS, GKE) for scalable, containerized model orchestration and serving.
We deploy MLOps telemetry that continuously tracks inference latency, infrastructure cost, and statistical data drift (like KL divergence), triggering alerts before accuracy degrades.
It is the process of taking a trained AI model and making it reliably available to real users at production scale.
It consistently traces back to integration and deployment challenges, not model quality.
IBM requires platform adoption. We work directly with your existing stack to solve your specific deployment blocker.
MLOps covers version control, testing, retraining, and rollbacks. Without it, model performance silently degrades over time.
Not necessarily. We build integration layers connecting AI products to existing systems through governed APIs.
Costs scale with infrastructure complexity, but focused engagements typically start in the low five figures.
We build cost monitoring and auto-scaling architecture into the deployment from day one.
Model drift occurs when real-world data patterns shift. We implement monitoring that flags drift before it affects business results.
Yes. We build data handling, audit trails, and access controls appropriate to GDPR, HIPAA, and SOC 2.
The engagement starts with a deployment readiness diagnosis to map the specific blockers standing in your way.
Common risks include prompt injection, data poisoning, and unauthorized access to model weights. We mitigate these by implementing API gateways, role-based access control, and strict input validation boundaries.
Real-time deployment requires low-latency serving infrastructure (like Triton) and streaming pipelines (like Kafka), whereas batch deployment focuses on high-throughput data processing scheduled during off-peak hours.
Yes. We prioritize knowledge transfer, providing comprehensive runbooks, CI/CD pipeline documentation, and training so your team can handle routine updates and monitoring independently.