AI & Machine Learning Services in India Services

Seven capability areas: GenAI applications, Retrieval-Augmented Generation (RAG), LLM fine-tuning (LoRA / QLoRA / DoRA / PEFT), feature engineering and classical ML, computer vision and edge AI, MLOps, and ML-accelerated engineering simulation. We deliver across the full lifecycle — data strategy through production deployment and monitoring.

Yes. We build production-grade RAG systems with hybrid search (BM25 + dense embeddings), neural rerankers, agentic query decomposition, graph-RAG over knowledge graphs, and grounded-citation pipelines. Every engagement includes a retrieval evaluation harness (RAGAS metrics) and a faithfulness check — because 40–60% of RAG projects fail to reach production without these. Common engagements: customer-support copilots, engineering-documentation search, sales enablement, and compliance Q&A.

Yes. We run LoRA, QLoRA, and DoRA fine-tuning pipelines on Llama 3, Qwen2 (text and VL), Mistral, Mixtral, and Phi-3 — typically on a single A100 or RTX 4090 with Axolotl, Unsloth, or the Hugging Face TRL stack. Outputs include adapter artifacts, evaluation reports against your domain benchmark, and serving guidance (multi-adapter on one GPU when the use case fits).

Common project shapes and price bands in 2026: GenAI / RAG pilot (one corpus, one use case): ₹4–9 lakh, 6–10 weeks. Production RAG with evaluation harness and MLOps: ₹15–35 lakh, 4–6 months. LLM fine-tuning program (data prep + LoRA training + serving): ₹8–22 lakh, 8–14 weeks. Computer vision pilot: ₹3–8 lakh. Production CV deployment with MLOps: ₹15–30 lakh. ML surrogate FEA / CFD: ₹15–40 lakh. Edge AI MLOps platform (multi-device fleet): ₹25–80 lakh. Team-augmentation retainers scoped monthly.

Use RAG when the answer lives in a body of documents that change over time — customer support, knowledge bases, compliance, engineering documentation. Use fine-tuning when you need the model to adopt a specific style, format, or domain vocabulary it can't pick up from in-context examples — structured-output generation, code in a proprietary API, brand voice, low-resource languages. The two are complementary: many production systems use both — a fine-tuned generator on top of a RAG retriever.

Yes. MLOps is a stand-alone engagement category: model registries (MLflow, SageMaker), feature stores (Feast, Tecton), CI/CD for ML, signed-OTA deployments on edge fleets, canary rollouts with automatic rollback, drift detection, and evaluation dashboards. Typical engagement: 8–16 weeks to take an existing ML system from ad-hoc operations to a documented, observable, retrainable pipeline.

Yes. Time-series, signal-processing, and tabular feature pipelines are a recurring engagement — predictive maintenance, demand forecasting, churn, anomaly detection, energy management. Outputs are documented feature definitions materialized to a feature store (Feast or Tecton), plus the downstream model that uses them. We default to gradient boosting (XGBoost, LightGBM, CatBoost) for tabular and switch to deep models only when the data shape justifies it.

Yes — vision-based quality inspection, defect detection, OCR for batch tracking, pose estimation for robotic pick-and-place, predictive maintenance, energy forecasting, and inspection-report GenAI are recurring projects. We deploy on-premise where data sovereignty or latency matters, which is the norm for Indian factory floors.

Yes. Model optimization is a core service — quantization (INT8 / FP16), pruning, TensorRT / ONNX conversion, hardware-specific operator fusion, and benchmarking against latency / throughput / power targets. We also optimize LLM serving with vLLM, TGI, and llama.cpp for self-hosted inference.

Yes. NDAs are routine on all our AI / ML work, including for confidential product datasets, vision footage, training corpora, and proprietary model architectures. We can sign your template or use ours.

Our core team is in Surat, Gujarat. We work remotely with clients across India (Mumbai, Bangalore, Delhi, Pune, Hyderabad, Chennai, Ahmedabad) and internationally (US, EU, UK, UAE, Singapore). Client communication is over Slack / email / Google Meet — we don't require on-site presence for most engagements.

Yes. We offer team augmentation engagements where Yantrix engineers join your team for a quarter or longer to drive ML strategy, build pipelines, and ramp up your in-house team. Scoped on a monthly retainer.