Dec 12, 2025
Deploying LLMs in Production: A Practical Guide
From AI Idea to Production-Ready System
Everyone is talking about AI. Very few are shipping it. At Deoxai, we build AI systems that move from notebook to production — solving real business problems, handling real data, and delivering measurable ROI. Whether you're exploring your first AI project or scaling an existing one, we bring the engineering discipline to make it work.
Our AI team combines deep expertise in machine learning, large language models, computer vision, and intelligent automation. We don't chase hype — we focus on the use cases that create actual value: reducing costs, improving decisions, automating workflows, and unlocking insights from data you already have.
Our AI Development Services Include:
End-to-end AI capability — from strategy and data preparation to model training, deployment, and ongoing optimization. Every solution is production-grade, not a demo.
1
AI Strategy & Consulting
We assess your business, identify high-impact AI opportunities, and define a realistic roadmap. You get a clear plan — what to build, why it matters, and expected ROI — before writing any code.
2
Machine Learning Solutions
Custom ML models for prediction, classification, recommendation, and anomaly detection. Built with scikit-learn, TensorFlow, PyTorch — trained on your data, tuned for your business.
3
LLM & Generative AI Apps
Intelligent chatbots, document assistants, code generators, and content tools powered by GPT, Claude, Gemini, and open-source LLMs. We handle prompt engineering, fine-tuning, and RAG pipelines.
4
Computer Vision
Image classification, object detection, OCR, facial recognition, and video analytics — for manufacturing quality control, retail analytics, security, and medical imaging.
5
Natural Language Processing
Sentiment analysis, entity extraction, document summarization, multilingual translation, and voice interfaces. Turn unstructured text and audio into structured, actionable data.
6
Intelligent Process Automation
AI-powered workflow automation — document processing, email triage, invoice extraction, and decision support. Free your team from repetitive work and reduce error rates dramatically.
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Our AI Development Process
AI projects fail when they skip steps. Our process is rigorous, iterative, and focused on shipping working systems — not just impressive prototypes.
Step 01
Discovery & Use Case Validation
We define the business problem, confirm AI is the right solution, and assess data readiness. Not every problem needs AI — we'll tell you honestly if it doesn't.
Data cleaning, feature engineering, and model training. We iterate rapidly, evaluate multiple approaches, and measure performance against clear success criteria.
Step 03
Integration & Deployment
We integrate AI into your existing systems — APIs, dashboards, mobile apps — and deploy to cloud infrastructure with monitoring, logging, and versioning.
AI models drift. We monitor performance continuously, retrain on new data, and optimize for cost, speed, and accuracy — keeping your AI system sharp over time.
Frequently Asked Questions
Real questions from businesses exploring AI. We give real answers — including when AI isn't the right answer.
Do I need a lot of data to build AI?
Not necessarily. Classic ML models can work with modest datasets (thousands of records). LLM-based solutions often need no training data at all — just good prompts and your domain knowledge. We'll assess your data during discovery and recommend the right approach.
How much does an AI project cost?
Costs vary widely depending on scope. A focused AI feature or automation pilot might start in the low lakhs range; a full AI product with custom models, integrations, and infrastructure is significantly more. We provide a detailed, itemized estimate after discovery — no surprises.
What's the difference between ML, LLMs, and AI automation?
Machine Learning (ML) uses statistical models to predict or classify from data. LLMs (Large Language Models) are AI systems trained on text — great for understanding and generating language. AI automation combines AI with workflow tools to replace or assist human tasks. We use all three, matched to your use case.
Is my data secure with AI systems?
Yes. We can deploy AI models entirely within your infrastructure (on-premise or your private cloud), ensuring no data leaves your environment. For cloud-based solutions, we use enterprise-grade encryption, access controls, and where possible, self-hosted open-source models.
How long does it take to build an AI solution?
A focused AI feature (chatbot, automation, prediction) can go live in 4–8 weeks. More complex ML systems with custom training and integration typically take 8–16 weeks. Enterprise AI platforms with multiple use cases may span several months. We deliver working software in increments.
Can you work with our existing AI tools and data?
Absolutely. We regularly integrate with existing AI services (OpenAI, Anthropic, Google AI), data platforms (Snowflake, BigQuery), and internal systems. We don't rebuild what works — we connect, extend, and optimize.
