There is a lot of hype around AI. Most of it distracts from what actually matters -- using AI to solve real business problems that show up on your P&L. Kodechi's AI Labs builds practical AI solutions for businesses across India and Canada -- not proof-of-concept demos that get abandoned in a quarter, but working AI systems that reduce costs, unlock revenue, or improve customer experience in measurable ways.
We work with three types of clients: startups building AI-native products, enterprises adding AI to existing systems, and mid-market companies exploring where AI genuinely fits versus where it does not. Our approach starts with the business problem, not the technology.
If AI is not the best solution, we will say so. Sometimes a rule-based system beats machine learning at a fraction of the complexity.
Data pipelines, model serving, monitoring, and retraining -- the last mile is what makes AI actually work.
Sometimes it is a fine-tuned open-source model. Sometimes it is a commercial LLM API. Sometimes it is a linear regression.
Building applications on top of OpenAI, Anthropic, Google, or open-source LLMs -- RAG systems, custom AI assistants, document intelligence, and workflow automation, including vector database setup and prompt engineering.
Modern AI agents that can research, reason, use tools, and take actions -- customer support automation, sales assistants, internal knowledge agents, and workflow copilots.
Predictive models for classification, regression, forecasting, and clustering -- recommendation systems, fraud detection, credit scoring, churn prediction.
Text classification, sentiment analysis, entity extraction, summarization, and translation for structured business data or unstructured customer feedback.
Image classification, object detection, OCR, medical imaging analysis, quality control automation, and video understanding.
Sales forecasting, demand prediction, customer lifetime value modeling, and inventory optimization.
Extracting structured data from invoices, contracts, forms, and unstructured documents using fine-tuned OCR and LLM pipelines.
Product recommendations for eCommerce, content recommendations for media platforms, and personalization engines.
AI readiness assessments, use case prioritization, build-versus-buy decisions, and roadmap development for companies exploring where to invest.
Assessment of data readiness, technical infrastructure, and business use cases, producing a prioritized AI opportunity list with feasibility, effort, and expected impact.
For non-trivial AI projects, we start with a short POC to validate feasibility with your real data before committing to a full build.
For custom ML: data preparation, feature engineering, training, and evaluation. For LLM integrations: prompt engineering, RAG setup, function calling, and cost optimization.
Model serving infrastructure, API integration, monitoring, error handling, and fallback logic -- the engineering that makes AI actually work in production.
Ongoing model performance monitoring, data drift detection, and retraining pipelines, because AI models degrade over time.
| Engagement | Timeline | Investment (INR) |
|---|---|---|
| AI readiness assessment | 2-3 weeks | 1,50,000 - 4,00,000 |
| Proof of concept (POC) | 2-4 weeks | 2,50,000 - 6,00,000 |
| LLM integration project | 6-12 weeks | 5,00,000 - 15,00,000 |
| Custom ML model development | 8-16 weeks | 8,00,000 - 25,00,000 |
| Full AI product build | 16-28 weeks | 20,00,000 - 60,00,000+ |
| Dedicated AI engineer | Monthly | 1,80,000 - 3,50,000/month |
| AI strategy advisory | Ongoing | 50,000 - 3,00,000/month |
Often, plain automation works better -- it is cheaper, more reliable, and easier to maintain than ML. We use AI where the problem genuinely requires it: unstructured data, natural language, image understanding, or prediction from complex patterns. If simple automation solves your problem, we will tell you.
Highly variable by scope. LLM integrations for existing products: INR 5-15 lakh. Custom ML models: INR 8-25 lakh. Full AI product builds: INR 20-60 lakh+. Ongoing dedicated AI engineer: INR 1.8-3.5 lakh/month. Advisory-only: INR 50K-3 lakh/month.
POCs: 2-4 weeks. LLM integrations: 6-12 weeks. Custom ML models: 8-16 weeks. Production-ready AI features: 12-20 weeks. Full AI products: 16-28 weeks.
Not always. LLM-based applications often need little or no custom training data. Traditional ML typically needs thousands to millions of examples depending on the problem. We assess your data situation during discovery.
Depends on your requirements. Commercial APIs are faster to start and easier to maintain, but come with per-token costs and less data control. Open-source models offer full data control but require infrastructure investment. We help you make the right choice.
For sensitive data, we can deploy models on your infrastructure so data never leaves your environment, or use enterprise-grade APIs with data processing agreements, or fine-tune open-source models on your data.
Yes. We work with founders building products where AI is the core value proposition, pairing our Custom Software team with AI Labs for the AI-specific engineering.
Yes. Model monitoring, retraining pipelines, A/B testing frameworks, and cost optimization are standard practice for us, not an afterthought.
Book a free AI strategy consultation. Share your business context and challenges; we will give you an honest assessment of where AI fits, where it does not, and what a realistic first step looks like.
Email: info@kodechi.com | Phone: +91 8375945272
Response time: within 24 hours. Serving clients across Delhi NCR, India, Canada, and globally.