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AI

AI, Machine Learning & LLM Engineering

Expertly crafted, data-driven AI keeps you ahead of the curve, but only when a model earns its place. We build production AI into products: supervised and unsupervised machine learning, deep learning and natural language processing, retrieval-augmented generation, multi-agent workflows and fine-tuned models that run on your own hardware, each with a model maintenance strategy and security designed in from the start.

Discuss your requirement

What we deliver

Scope of work

Every engagement is scoped in writing before it starts. A typical engagement includes:

  • Supervised and unsupervised machine learning, deep learning and NLP pipelines
  • Retrieval-augmented generation with vector search and evaluation
  • Multi-agent workflows with LangGraph, MCP tool integration and human-in-the-loop controls
  • LLM fine-tuning with LoRA and QLoRA, and self-hosted or air-gapped deployment
  • Classical machine learning ensembles where they outperform an LLM
  • Evaluation harnesses: machine-graded tasks, LLM-judge rubrics and regression suites
  • Model maintenance: packaging, versioning, drift monitoring and retraining

What you get

Outcomes

  • AI features that are measurably better, not just new
  • Predictable cost and latency
  • Systems that are secure and auditable from day one

Track record

Experience behind it

  • Built a fine-tuned reasoning model and a gradient-boosted ML ensemble into Cyron API Security
  • Architected a LangGraph multi-agent suite adopted by three engineering teams
  • Designed machine-graded evaluation tasks for a frontier AI lab's cybersecurity benchmark
  • Founding technical lead of an NLP-driven structured-data product; Stanford and DeepLearning.AI Machine Learning Specialization

Technologies and frameworks

  • LangGraph
  • MCP
  • Python
  • vLLM
  • llama.cpp
  • LoRA / QLoRA
  • ONNX
  • LightGBM
  • XGBoost
  • scikit-learn
I found Shreyans a true Polyglot Programming Engineer who could not only contribute to Research items but also capable to develop a quality code for products. I am amazed by his strong fundamentals and practical knowledge in Machine learning and Artificial Intelligence.
Dhaval Thanki Head of Research and Development, Milestone Inc.
Managed Shreyans directly, 2020

FAQ

Common questions

Something else on your mind? Ask us directly.

Can we run an LLM on our own infrastructure?

Yes. Open-weight models can be fine-tuned for your task and served on your own servers, including air-gapped environments, with encrypted model distribution.

Do we need a large language model at all?

Not always. For classification and scoring, a well-built classical model is often faster, cheaper and more accurate. We recommend the simplest approach that meets the goal.

How do you know the AI is working?

We build an evaluation harness before we build the feature: a fixed set of graded tasks that every change is measured against, so quality is tracked rather than assumed.

Related services

Often combined with

Have a system to build, modernise or secure?

Tell us where things stand today. The first conversation is free, and you will leave it with an honest view of the work involved.

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