About
We built the annotation tool we needed but couldn't find.
Founded in 2024 by engineers who spent years watching datasets become the bottleneck in production CV pipelines.
Our story
Annotation was the bottleneck. We fixed it.
The team behind Annotgrove spent years working on production computer vision systems. In automotive perception, medical imaging, and retail detection, the pattern was always the same: the model architecture was ready, the training infrastructure was ready, and the engineers were ready. The dataset wasn't.
Drawing bounding boxes from scratch is slow. A frame with five objects takes an experienced annotator two minutes. A dataset of 50,000 frames takes months. Most teams either pay for expensive third-party annotation services or accept the delays.
We started Annotgrove in 2024 to change the ratio. AI pre-labeling doesn't fully automate annotation, but it shifts the job from drawing to correcting. That single shift changes the economics: correction takes one-third the time of drawing, and pre-labeled frames require less annotator expertise, which means teams can scale throughput without scaling headcount.
We're bootstrapped and building toward a product that genuinely solves the problem we experienced, not one that looks good in a demo but breaks on real-world datasets.
Mission
Make annotation fast enough that dataset quality becomes a competitive advantage, not a production blocker.
We're not solving annotation for companies that want to outsource to overseas workers. We're solving it for engineering teams that want to own the quality of their training data and move faster than their competition.
How we work
What we actually believe
Correctness over claims
We report pre-label accuracy numbers with methodology, test set details, and confidence intervals. If a benchmark doesn't hold on your dataset, we want to know why, not hide behind aggregate averages.
API-first, always
Every feature in the UI is available in the API before or at the same time. ML teams automate their pipelines; a tool that requires a UI click to export is a tool that becomes a manual step in someone's cron job.
Data stays yours
Your training data is competitively sensitive. We don't use customer datasets to train or improve our pre-labeling models. Your annotation work stays in your project, period.
Built for shipping, not demos
We care about the use case where your model needs to go to production by a deadline, not the use case where someone wants to run an annotation experiment once. Production pipelines are messier and that's what we optimize for.
The team
Three engineers who lived the problem.
The founding team came from production CV roles at automotive, medical imaging, and infrastructure companies before building Annotgrove.
VAVikram Anand
CEO and Co-founder
8 years in automotive perception CV. Previously led a dataset pipeline team responsible for annotating 12M+ frames per year. First-hand experience with annotation-as-bottleneck at scale.
PNPriya Nair
CTO and Co-founder
ML infrastructure background. Built the pre-labeling model architecture and the consensus scoring system that drives review queue prioritization.
MWMarcus Webb
Head of Product
Previously built annotation tooling at a medical imaging company. Designed the review interface to minimize annotator decision fatigue on high-volume batches.
Try Annotgrove on your own dataset.
10,000 free labels per month. No credit card. See pre-label accuracy on your actual frames.