AI & Machine Learning Development Services
We build AI systems that reach production. Generative AI, agentic workflows, LLM integration, RAG pipelines, computer vision and full MLOps — engineered on a foundation that can actually carry them.
What does Kirshi's AI and machine learning service include?
Kirshi Technologies builds production-grade AI systems across six areas: generative AI and LLM applications, agentic AI systems, retrieval-augmented generation (RAG) pipelines, computer vision, natural language processing and MLOps infrastructure.
Every engagement begins with a data and infrastructure readiness assessment, because AI deployed on unstable data foundations fails in production regardless of model quality. Delivered projects include AI video analytics for SOP compliance at Lucas TVS, brain tumour segmentation across T1, T1CE and T2 MRI sequences, smartphone-deployable cataract detection, and an OpenCV-based firearm training app with a proprietary ML scoring algorithm.
What is included.
Generative AI & LLM Apps
Custom applications built on GPT, Claude and open models — with prompt versioning, evaluation harnesses and cost controls designed in from the start.
Agentic AI Systems
Autonomous agents that plan, call tools and complete multi-step tasks — with reasoning traces logged, permissions scoped narrowly and a rollback path on every deployment.
RAG Pipelines
Retrieval-augmented generation over your own documents and databases, with chunking strategy, embedding selection and retrieval evaluation tuned to your corpus.
Computer Vision
OpenCV and deep learning systems for defect detection, SOP compliance, medical imaging segmentation, object tracking and shot-placement scoring.
Natural Language Processing
Document intelligence, classification, extraction, summarisation and multilingual processing — including Tamil and other Indic languages.
MLOps & Deployment
Model serving, versioning, drift monitoring, evaluation pipelines and CI/CD for models — so the second model ships in weeks, not months.
What we build with.
How we deliver.
Readiness assessment
We audit your data estate, infrastructure and use case before proposing a model. Most failed AI projects were doomed at this stage, not at training.
Prototype & evaluate
A working prototype with a measurable evaluation set, so 'is it good enough' becomes a number rather than an opinion.
Production hardening
Guardrails, logging, cost controls, human escalation paths and rollback. This is the step that separates a demo from a system.
Deploy & monitor
Live deployment with drift monitoring and a feedback loop, so quality is tracked rather than assumed.
Related case studies.
Healthcare AI Initiative
Deep learning models trained on brain MRI datasets to autonomously identify, classify, and segment tumors across T1, T1C…
Read case studyHealthcare AI Initiative
ML models trained on retinal and lens imagery, deployable on smartphones for early cataract screening in underserved reg…
Read case studyLucas TVS
AI-driven video analytics monitoring Standard Operating Procedure compliance on the manufacturing floor in real time.…
Read case studyCommon questions.
A readiness assessment takes two weeks. A working prototype with an evaluation set typically follows in four to six weeks. Production hardening and deployment depend on scope, but most clients see a live system within three to four months of the first call.
Not always. Computer vision and document intelligence projects usually need your data. Some use cases can start with public or synthetic datasets and fine-tune on yours later. The readiness assessment tells you which situation you are in before you commit budget.
Yes. We embed AI agents, RAG systems and LLM workflows directly into existing APIs, databases and enterprise platforms without replacing what already works.
You do. Clients retain full intellectual property ownership of all custom code, trained models and derived artefacts from day one.
Ready to start?
Fixed-price contracts. Start in one to two weeks. Full IP ownership from day one.