Applied AI Infrastructure

We make AI
run faster. finish the job. prove itself. hold your bar. work all night.

VitalOps works across the AI stack. We tune LLM serving so models answer sooner and push more tokens per second, we build coding agents that are measured on public benchmarks, and we run entire expert workflows to the quality bar your best people would hold.

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Products

Research-grade engineering, delivered as production systems

Three things we do well: making inference fast, making agents good enough to trust with real work, and putting both to work inside enterprise workflows.

01

LLM Inferencing

Inference speed work across the serving stack: tokens per second, time to first token, and cost per token, tuned to your model and your hardware.

02

Coding Agents

Agent design, harness engineering, and rigorous benchmarking, so progress is verifiable on public boards rather than self-reported.

03

Enterprise Automation

Agentic software for finance and supply chain, built alongside consultancies and deployed inside large enterprises.

Inference

LLM Inference Optimisation

We make large language models run faster on the hardware you already have. Serving-stack configuration, scheduler and kernel-level tuning, and careful ablation studies that lift tokens per second and cut the time your users spend waiting for a response.

✓Higher tokens per second on the same GPUs
✓Lower time to first token and end-to-end latency
✓Tuning across SGLang and vLLM serving stacks
✓Numbers measured independently, not self-reported
Throughput tuning

Batching, scheduling, and parallelism settings tuned per model so a single deployment serves more traffic before you add a GPU.

Latency work

Time to first token and inter-token latency pulled down where it is felt most, in interactive and agentic traffic.

Kernel and stack depth

Ablation studies down to kernel and scheduler level on current NVIDIA hardware, with the winning configuration handed back to your team.

RPA

Desktop Process Automation

Automate any desktop workflow: data entry, form processing, report generation, cross-application transfers. AI agents interact with your existing software exactly as a human would, no API integrations required.

✓Works with any desktop application
✓Record once, replay on schedule
✓Cross-platform: Mac, Windows, Linux
✓Remote machine management via encrypted LAN
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No integration work

Agents drive the software through its own interface, so legacy tools with no API are automated the same as modern ones.

Fleets, not single machines

One agent drives many machines over an encrypted link, running the same workflow in parallel across a fleet.

Sandboxed by default

Every action an agent takes runs inside our own sandbox runtime and is recorded in full, so nothing touches the host unchecked.

Data

Intelligent Data Pipelines

Transform, clean, and enrich datasets with AI that understands context. Classify products, extract structured data from unstructured text, anonymize PII, and build multi-step data workflows at any scale.

✓AI-powered map, filter & enrich operations
✓HIPAA-compliant PII anonymization
✓Works with any data size, no context limits
✓Connects to DuckDB, PostgreSQL, BigQuery
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Past the context window

Row-level semantic work across datasets far larger than any model context, distributed over Dask or Spark.

Built on DataTune

Our open source transformation library, in production with paying customers and peer-reviewed at DATA 2026.

Any provider, any store

Bring your own LLM provider and your own warehouse. Nothing is locked to a single vendor or a hosted endpoint.

Developer Tools

Coding Agents

Autonomous coding agents that write code, run tests, fix bugs, and refactor from the command line, built and tuned to perform on public coding benchmarks. Most of the distance between a good model and a good agent is the harness around it: tool design, context handling, and recovery. That is the part we engineer.

✓Benchmarked against frontier lab agents on public boards
✓Multi-step task execution with file & bash tools
✓Persistent sessions, MCP integration, pluggable models
✓Granular permission controls
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Harness, not just model

Tool design, context handling, and error recovery are where agents win or fail. We build that scaffolding around whichever model you use.

Measured, then improved

Every change is validated on a public benchmark, so progress is verifiable outside our own reporting.

Yours to run

Self-hosted, headless, and pip-installable, with your own model backends behind it.

Quality Assurance

QA & Testing Automation

AI agents that navigate your web and desktop applications, run through test scenarios, capture screenshots, and flag regressions continuously, without writing a single test script.

✓Visual regression detection with screenshots
✓No test scripts to write or maintain
✓Scheduled or continuous test runs
✓Works across web and native desktop apps
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Nothing to maintain

Agents read the screen instead of chasing selectors, so a redesign does not send your suite red overnight.

Runs where you build

Scheduled, continuous, or wired into your pipeline, on self-hosted runners if compliance requires it.

Evidence with every run

Screenshots, steps, and decisions are captured in full, so a regression report is something you can actually act on.

Enterprise

Agentic AI for Finance and Supply Chain

We build agentic SaaS with consultancies and deploy it inside large enterprises. Procurement, spend analytics, and RFP or RFI workflows, layered onto the processes and systems your teams already run, rather than asking them to start again somewhere new.

✓Reconciliation, compliance checks, and reporting
✓Supplier discovery, sourcing events, and scoring
✓Multi-stage approvals with a human at every gate
✓Works over messy ERP, CSV, and Excel data
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Delivered end to end

Several SaaS applications built and shipped alongside a supply chain consultancy, in use with their enterprise clients.

Layered, not ripped out

Agents sit on top of existing processes and systems, so nothing has to be rebuilt or replaced to get value.

Data at real scale

Transformations run across datasets far larger than any model context window, which is where most enterprise data actually lives.

Customer Stories

Trusted by teams moving fast on data

“

VitalOps helped us optimise our data pipelines for the finance databases which directly translated into a smoother and more intuitive experience for our users.

Chatfin.ai
Ashok Manthena CEO, Chatfin
“

Datatune by VitalOps has become a foundational layer in our Source-to-Pay automation stack. It helped us bring structure to messy procurement data, improve standardization across master data and events, and accelerate how quickly we can translate raw inputs into usable workflows. The team is highly responsive, pragmatic, and product-minded, and the best part is that they understand what it takes to operationalize data, not just analyze it. If you are building serious automation on top of enterprise data, Datatune is a strong core to build on.

Straightops.com
Ganesh Sankar CEO, Straightops
Our Story

About VitalOps

We're a team of engineers who worked as core contributors to AI software that created millions of impressions worldwide. Our extensive background in AI research and experience in fast, scalable deployment of multiple AI-native softwares helped shape our journey into entrepreneurship.

Agentic AI, from scratch Making sense of LLMs A roadmap into ML and GenAI
Abhijith Neil Abraham

Abhijith Neil Abraham

Engineer and AI Researcher
Creator of TableQA, Published author and Speaker

Fariz Rahman

Fariz Rahman

Open Source Contributor
Machine Learning Engineer | Speaker

How it works

Simple to start. Built to last.

01

Tell us how your department runs

No forms to fill. No software to learn. We sit with your team and learn exactly how work moves through your department, the normal cases, the exceptions, and the things that always seem to fall through the cracks.

At the end of this, you have a clear picture of what's automatable and what genuinely needs a human. Your team helps define both.

"We just talked through what our team does every day. VitalOps turned that into something that actually runs."

✓Your team's knowledge stays with you
✓Every edge case is accounted for
✓You decide what agents are allowed to do
Open Source

The end-to-end stack

We build every layer an agent needs to actually run on its own. Memory, sandboxing, desktop control, coding, data. Put together, it is the tooling that lets you walk away from the computer and trust what happens next.

OpenDesk

Open source desktop automation framework. AI agents control computers with clicks, keyboard input, screenshots, OCR, workflow recording, and remote machine management across Mac, Linux, and Windows.

Powers our Desktop RPA, QA Testing, and enterprise workflow deployments.

View on GitHub →

OpenVibe

A modular, pip-installable AI coding agent that runs headless. Automates multi-step development tasks: writing code, running tests, editing files, and fixing issues. Supports pluggable LLM backends, MCP server integration, and persistent sessions.

Powers our Coding Agents solution.

View on GitHub →

DataTune

Agentic data transformation library. AI-powered map, filter, and enrichment operations that give LLMs row-level intelligence over datasets of any size.

Powers our Intelligent Data Pipelines solution.

View on GitHub →

Temenos

A secure runtime for AI agents. Your agent runs on the host, but the code it executes runs in a sandboxed box. Run one agent or a thousand without worrying about what they touch.

Keeps every agent we ship safe by default.

View on GitHub →

Coherence

An embeddingless memory framework for long-running LLM agents. One JSON file, no vector database, no embedding model. Your memory is text you can read, grep, and version-control.

Gives our agents the memory they need to run for days, not minutes.

View on GitHub →

Let's build it properly.

Inference that keeps up. Agents that finish the job. Workflows that run without you. See where VitalOps fits in a 30-minute call.

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