Meet Ardent, your data engineering team

The market of LLM wrappers, agents, and agentic everything has exploded over the last two years. Crane spent most of 2023-2024 saying no. But in 2025, we’ve begun saying yes.

It’s become clear, at least to us, that the barrier to entry is effectively zero to get an AI product off the ground. And yet it’s also incredibly hard and incredibly tedious to build one that actually works and does what you say it’s going to do consistently over time.  You have to be obsessed with solving a problem and you have to be obsessed with quality and you have to be obsessed with measurement, feedback, and constant iteration. You have to assume that whatever you’ve achieved has a very short half-life, that it’s constantly degrading, and you constantly have to work to maintain performance and quality.

Trust and reputation are what create durability in our new world. Trust and reputation come from consistently delivering a product that does what you say it’s going to do in the face of unprecedented competition.

Vikram Chennai, Ardent’s founder, had all this figured out before we even met him.

The Problem: there is more data engineering work to do than there will ever be data engineers

The basic infrastructure needed to get started with data engineering takes days to weeks of work to stand up. For very experienced data engineers, it takes hours at the least. This is not a one time activity. Data infrastructure takes ongoing change, maintenance, and repair to deal with the changing needs, and changing scale, of the business. It’s also not a cookie-cutter activity. No two data infrastructures look alike and no two organization have the same exact needs or scale in the same exact ways at the same exact times.

Data engineers, the software developers and operators who specialize in data infrastructure are in short supply. Not quite as short as AI researchers, but way shorter than programmers in general. When we first spoke with Vikram, there were 6000 unfilled data engineering publicly posted jobs in the US alone, growing at 20-30% a year, paying an average of $100k for entry level, and up to $250k+ for senior roles, in base cash comp. Even at very conservative discounting, the activity of data engineering is worth billions of dollars a year in the US, with a deficit of hundreds of millions of dollars worth of roles unfilled. Without counting the rest of the world.

What Ardent Is Building

Today, Ardent is the first AI that can create a fully operational data pipeline in “one shot”, meaning on the first try, with minimal human input—just the parameters or specification of needs necessary. Ardent dutifully connects to your existing systems to understand the other parts of your infrastructure that pipelines must interact with. It debugs both its own work and the ongoing operation of pipelines.

Even in alpha, Ardent did this at a sufficiently high degree of accuracy and quality to have paying customers using Ardent to create pipelines in production, which the rest of their infrastructure relies on every day.

Tomorrow, Ardent is your first data engineer who can stand up an end to end data infrastructure—pipelines, databases, integration, ETL/ELT, etc.

The day after that, Ardent is an entire data engineering team proactively building, fixing, and upgrading your data infrastructure to respond to requests from the product team or improve the performance of long running queries or refactor a core database implementation to handle the next stage of your growth.

Why It Matters

Every company, even if it’s not a software company, is a data company. Every company will use AI. And every AI needs to be fed with data.

Data infrastructure is the life blood of AI: From training, to inference, to fine tuning, to RAG, to testing and evals—everything having to do with implementing and using AI in a business context requires building and operating data pipelines, in the first case, and ultimately an entire bulwark of data movement, processing, and storage. This is one of the key bottlenecks to adoption and production-ization of AI.

Data infrastructure becomes rapidly complex and brittle. Startups set up their first data pipeline right after they set up their cloud accounts with AWS, GCP, or Azure. Where a startup runs 1-10 pipelines; mid-sized companies run 10-100, and enterprises run 1000s to 10s of 1000s. The downstream impact of being slow at building/operating/scaling pipelines compounds and becomes a drag on the business. We’ve seen this time and again with customers of our portfolio companies.

The Team

We were first introduced to Vikram by another founder, someone we respect a great deal but haven’t found a chance to work with yet. At the time, Vikram was sleeping on a friend’s couch after having moved to SF six months earlier. He had shipped the first version of Ardent and somehow acquired real paying customers for a product that had just been released by a single person right before they purchased it.

While we were in initial conversations, Vikram closed another $60k ARR customer. By himself. From his friend’s couch. With an alpha.

Everyone we introduced, even grizzled technical founders, came away impressed.

What’s Next

Ardent has grown from one to two people, from an alpha to a beta, and is on its way to becoming a data engineer’s engineer and then your always on data engineering team which will look and feel like an always evolving data infrastructure that is an accelerant for your business.

Find them at tryardent.com

— Aneel Lakhani, Crane Venture Partners