Full-stack AI & data firm
Welzin is a full-stack AI and data firm: data science, predictive analytics, AI engineering, MLOps/AIOps, and the product engineering to keep it running.
Startup or enterprise, we move your numbers - revenue, risk, uptime, margin. Senior engineers, in production from day one.
Data Science & ML
Predictive analytics, Data Engineering, Statistical and ML modeling that turns messy data into decisions.
AI Engineering
Generative AI and AI agent development - RAG, Tool use and Agents grounded with evaluation and guardrails.
DevOps/MLOps & Operations
Scale on Kubernetes with DevOps/MLOps services that keep systems healthy in production: pipelines, drift monitoring & retraining.
Production systems for teams measured on the result. Each engagement starts with one metric; here is how far it moved once the system shipped.
Everything a production AI system needs - built, shipped, and operated by the same senior engineers.
We turn messy, real-world data into models and insight you can act on - rigorous, validated, and tied to a business question that matters.
Forecast demand, churn, risk, and revenue - wired into the decisions they drive.
Production GenAI and agents, grounded with evaluation and guardrails.
Pipelines, monitoring, and observability that catch drift before your users do.
APIs, apps, cloud, and data platforms - the product engineering around your AI.
Vision models for inspection and safety, plus the labelled datasets behind them.
Perception, policies, and simulation for robots and factory automation.
What it looks like when a senior team stays accountable - shipped systems, returning clients, uptime, and speed to first result.
See our workproduction systems shipped and running
of clients return for the next build
uptime across platforms we operate
median time to first measured result
The team you meet on the first call is the team that ships.
From first conversation to a system you own in production - most clients see a measured result inside six weeks.
Talk to a senior engineerA senior engineer - not a salesperson - maps your metric, data, and constraints, then scopes the smallest build that can move the number.
We build in your environment and reach production early - evaluated pipelines, grounded GenAI, agents with guardrails - with you in the loop every week.
We operate what we shipped, report against the metric we agreed, and hand over a system your team can run without us.
A firm that ships products is a firm you can check up on - here's what we run ourselves, in production, under our own name.
Our product · Education
Ship-ready AI & data upskilling
Our product · Compliance & Finance
India startup compliance + the VC math
Our product · Fintech
Credit infrastructure for modern lenders
“Fraud losses fell 41% in the first quarter without adding review headcount. The engineers who scoped the model were the ones on call at launch.”
“Forecast error is down 32% across 1,400 SKUs, and their pod still reviews drift with us monthly. The first vendor that put the metric in the contract.”
“ETA accuracy went from 71% to 93% in eight weeks. No discovery theatre - a senior engineer was in our warehouse data by day three.”
“They rebuilt our inference stack before Series A diligence - serving costs down 47%, eval pass rates doubled, and the runbooks were ours from day one.”
What running AI in production teaches us each week - written by the engineers doing the work, for the people measured on it.
Physical AI
In five months the largest first-person dataset went from 10,000 hours to a million. Humanoids are clocking real shifts at BMW, Mercedes, and Tesla. NVIDIA open-sourced the whole robot brain. Here is what is actually happening in egocentric data, robot learning, and factory automation, and what it means if you run a plant or a data team.
AI Models
Anthropic's most capable model is the first from its Mythos tier, a rung above Opus. The positioning, the specs that matter, the benchmarks, and the practitioner's rule for when its 2x price is actually worth paying.
AI Tooling
Everyone publishes the concept. Almost nobody publishes the fields, the ingest contract, or the dispatcher. This is a working LLM wiki taken apart piece by piece - the permission model, the three frontmatter fields that carry the system, the five-step ingest, the invariant that makes automation safe, and the two things that actually break.
GenAI & Agents
Large Language Models (LLMs) like GPT-4, LLaMA, and Claude now power critical real-world applications - from customer support bots that might accidentally...
Engineering
In this article, we’ll guide you step by step to build your very first React project using Lovable AI, connect it to Supabase, and publish it online. By the...
We are a full-stack AI & data firm across seven disciplines: data science, predictive analytics, AI engineering (RAG, copilots, agents), MLOps/AIOps, product engineering, computer vision and data annotation, and robotics and physical AI. One senior team owns the path from the first model to a system running in production - not slideware.
Anything where data should be driving a decision and is not yet: forecasting, risk and fraud scoring, churn, document and workflow automation, copilots and agents, and the data platforms underneath them. If it touches a number you care about, it is in scope.
Welzin is based in Chandigarh, India, and works remotely with startups and enterprises across the United States, United Kingdom, United Arab Emirates, and India.
Yes. Welzin builds production GenAI and retrieval systems and structures its own and clients' content so it is accurate, well-marked-up, and citable by AI answer engines like ChatGPT, Perplexity, and Google's AI surfaces.
A metric that’s stuck, a system that needs rescuing, or a build you want scoped - a senior engineer reads every note.
