Infinex Labs is an applied AI engineering studio in Bangalore. We build computer vision, AI agents, and document pipelines that take over the reading, checking, and deciding your team does by hand, and we ship them to production.
Most of the work inside a company still runs through someone reading, checking, and re-typing.
Rent rolls into spreadsheets. Site photos into escalations. Leads into inboxes.
Software couldn't touch that work because it couldn't see or read.
Now it can. We build the systems that take it on.
Every system we have shipped does some mix of three jobs: understand an input, decide what it means, act on it. We build all three, so the handoffs between them aren't where things break.
Turning messy input (a T12 statement, a technician's photo, a customer selfie, a camera feed) into structured data the rest of the system can trust.
Retrieval over your own documents, forecasting, simulation, and long-term memory. Every answer is grounded in a source you can check.
Agents and pipelines that post, negotiate, route leads, and execute trades in live systems. A human approves the steps where a mistake is expensive.
A few of the AI systems we've built for clients and for ourselves. Each one is live, and each has a case study covering what it does and how it's put together.
Autonomous agent framework that researches, posts, and manages community across Discord, Telegram, and X.
Automated pricing pipeline turning T12s and rent rolls into underwriting analytics.
AI hairstyle suggester that recommends styles from a client's photo across a Dubai salon chain.
Founders rehearse in front of AI VCs and pressure-test ideas across 10,000+ human-like agents before facing a real one.
End-to-end quant trading and LP-pool investing platform managing $1M+ in live capital.
A Singapore quantitative trading firm needed to run liquidity-pool strategies with live capital, and had no system that could hold strategy, execution, and security in one place.
We modelled pool behaviour and rebalancing costs before writing any production code, so the strategy engine was designed against real market dynamics rather than assumptions.
Market data feeds a strategy engine. Risk and allocation checks gate every position. A hardened execution layer moves capital into pools, and analytics report back on every move.
Live and managing more than $1M in capital, on an architecture that scales horizontally as assets under management grow.
We read the papers and run the benchmarks to find out what current models can reliably do, before we promise anything.
We map perception, reasoning, and action onto your problem and settle the design on paper, before it costs anything to change.
The smallest system that proves the hardest part works. Built fast, and dropped if it doesn't hold up.
Tested on your real data with real users. You see accuracy, latency, and failure modes before launch.
Shipped with monitoring and guardrails, and a human in the loop wherever being wrong is costly.
Data changes and models drift. We keep measuring after launch and feed what we learn back into the system.
Infinex Labs is an applied AI engineering studio based in Bangalore, India. We design, build, and deploy production AI systems: computer vision, AI agents, retrieval-augmented (RAG) assistants, document and data pipelines, and quantitative trading infrastructure. We also build our own products, including Intelligence OS and drift.
Companies with an operational problem that standard software can't handle: a team reading documents by hand, reviewing photos or video, or running the same judgement call hundreds of times a week. Our clients are in Dubai, the United States, and Singapore, and we work with them remotely from Bangalore.
12 systems are live today. Examples include Meridian, a quant trading and liquidity-pool platform managing over $1M in live capital for a Singapore firm; Underwrite, which turns T12 statements and rent rolls into underwriting analytics for US real-estate clients; Herald, AI agents that run marketing and community across Discord, Telegram, and X for a Dubai company; and Mirror, a computer-vision hairstyle recommender used across a Dubai salon chain.
Yes. We have shipped agents that research and publish content, manage online communities, negotiate trades of tokenized real-estate assets, and simulate thousands of investor personas to test a startup idea. Agents that take real actions run with guardrails, and a human approves the steps where a mistake would be expensive.
Six stages: research, architecture, prototype, validation, deployment, and post-launch measurement. We check what current models can reliably do before committing to a design, build the smallest prototype that proves the hardest part, and measure accuracy, latency, and failure modes on your real data before anything goes live.
Yes. Most of our client work is confidential. Our case studies describe the system, its architecture, and its results without naming the client.
Email info@infinexlabs.com with a few lines on the problem, the data you already have, and what a good outcome looks like. That is enough for a first conversation.
Send a few lines on the problem, the data you already have, and what a good outcome looks like. That's enough for a first conversation.