Compression you can compute on

Every team that works with real-world data eventually makes the same uncomfortable choice. The sensors, cameras, and markets you depend on produce more history than you can afford to keep hot — so you downsample it, push it to cold storage, or delete it. Each option quietly throws away something your models will want later.

We started Precica because we think that tradeoff shouldn't exist.

The idea

pdata compresses data by orders of magnitude — up to 1000x on real-world workloads — and keeps it directly queryable. There is no decompression step. You search, recall, and forecast against the compressed representation itself.

That single property changes what's practical:

• Energy — keep a full decade of granular meter and market readings, and forecast straight from them.
• Physical AI — give a robot a memory it can think back through, instead of re-scanning the room every time.
• Security — retain years of footage with every frame still searchable.

What's next

We're running our first industrial pilots now, and we'll use this blog to share what we learn — results, engineering notes, and the occasional deep dive into how compression-native querying works.

If you'd like a pilot on your data, get in touch.