Tilebox

Want to vibe with SPACE DATA? Now you can. Claude + Tilebox = any satellite data, any insight, one prompt. 🛰️ In the last five years we sent more satellites into space than in the 50 years before that. China alone requested licenses for 200,000 satellites this year. SpaceX asked for a million. For context: right now there are 15,000 active ones up there. Space is getting crowded. And it's producing insane amounts of data. The problem was never the data, the problem was using it. Until now, satellite data was for hedge funds monitoring Walmart parking lots. You needed massive teams, massive infrastructure, and 80-90% of the work was just data cleanup and sourcing before any actual analysis happened. Tilebox just changed that. Their pitch: the AWS for SPACE DATA. You connect Claude Code to their MCP, write a prompt, and it figures out the rest. You don't need to know where the data is stored, how it's formatted, or which AI model to use. It finds the data, runs the compute co-located with it, and gives you answers. And this is just day 0 - imagine how this will look once there are 100 thousands of satellites in space (we got 15k now). This is exactly the kind of software infrastructure Europe should be building. 🇪🇺 Disclaimer: we're investors (obviously). Also means we got first dips in showing this to the world.

A lot of satellite and geospatial work gets stuck between “the analysis works” and “someone can use this reliably.” We’re testing a Tilebox implementation sprint to help close that gap. Bring us an algorithm or workflow that already works in a notebook, script, or internal pipeline. We’ll help implement it in Tilebox and turn it into a repeatable workflow that can be run, monitored, debugged and operationalized. We’re looking for 3 to 5 teams to try this with. DM me if you have something in mind!

If you're curious, ask your favorite model to research for you how much CO2 is emitted by transferring 1 PB of data between cloud regions, vs the CO2 emissions of an afternoon of agentic coding! Surprised me quite a bit, it's a factor 2,000x to 400,000x The absolutes (in driven mile equivalents for example) are shocking Better to invest a few tokens and an afternoon and co-locate your algorithms with the underlying data, now that the infra difficulties are removed tilebox.com/blog/climate-t…

Tilebox 2.0: Earth data for wherever your agents work producthunt.com/products/tileb… Upvote if you'd like to support us and make physical world data more accessible and actionable!

Physical-world data has reached a strange point: we can capture it faster than most teams can use it. The data is still operationally far away from the software trying to use it. Tilebox gives teams the missing loop: discover data, run workflows where the data lives, observe what happened, debug, and rerun.

.@tileboxio announced an update aiming to make AI agents more effective geospatial-data analysts. payloadspace.com/exclusive-tile…

As part of the launch we built datacenterbuildout.com with an agent on Tilebox. The site tracks US data center construction using satellite imagery, with a web frontend, backend workflow, before/after imagery, rankings.. Here's what we didn't have to do: - set up workflow infrastructure - build a complex CI/CD pipeline to roll out changes - wire up observability - copy-paste logs, traces, errors, and failure modes back into the agent - explain where the satellite data lives or how to load it - tell the agent how to parallelize the work - manually deploy each workflow iteration That's the development loop that this launch unlocked data context → workflow code → deployment → observable jobs → inspectable outputs → iteration

Today we launched a major Tilebox update, covered exclusively by Payload. The core idea is: AI agents should not just answer questions about geospatial data. They should be able to build, run, inspect, and improve the workflows that produce those answers. For physical-world data, the hard part is not only the model. The data is huge, fragmented, governed, and spread across providers, clouds, internal systems, sovereign environments, and eventually orbit. If an agent has to guess the dataset, schema, runner, logs, or output provenance, it will eventually make things up. So we built Tilebox around the engineering loop: discover typed data, define a workflow, run it where the data lives, observe logs/traces/task status/outputs, debug, and rerun. That is what makes agents useful for EO: reviewable workflow execution, not black-box answers. Thanks to @payloadspace for covering the launch. payloadspace.com/exclusive-tile…

We're excited to sponsor Berlin Aerospace Hack, and I'm personally excited to be there at the kick-off tomorrow! To offer the most, we're boosting @tileboxio Community Access tier capacity for this weekend, and can't wait to see what Berlin will build 140 people, one day and two nights Let's go 🚀

There's still time to sign up!

🚀 Hackathon Challenge #3: Mini-Constellation Tasking Planner Build a 24–72h imaging plan for a 3–6 sat constellation: maximize AOI coverage, avoid clouds, respect sun-angle + off-nadir limits. This is real mission-planning work. Use @tileboxio to: 🛰️ Query ephemerides, terrain masks, cloud forecasts ⚙️ Run batch schedule evaluations 💾 Reuse cached visibility + weather lookups 🌍 Pull open EO data (S1/S2/S3, Landsat) Deliver: • Coverage map (footprints by sat/time) • Tasking schedule (CSV/JSON) • KPIs: coverage %, off-nadir, cloud-free area • Compare vs. naïve baseline This is exactly how real operators plan imaging windows. Join the Berlin Aerospace Hackathon ↓ luma.com/scw0gyac

🆕 #CNG2025 session video Lukas Bindreiter shows how the @tileboxio workflow orchestrator helps process geo data at scale. He explains how its decentralized "task runner" architecture enables you to transition from local development to production-scale. ▶️ youtu.be/sxXBL0cibrI

🆕 #CNG2025 session recording See how @tileboxio uses S2 Geometry for fast spatial queries on massive datasets. Lukas Bindreiter hows their polygon indexing for scalable catalogs ▶️ youtu.be/s0lZd5I64RU

Friday closing #LPS25 challenge: In your metadata catalog, how many Sentinel 2A MSI L1C granules do you find in this AOI for 2024 between Tonga 🇹🇴 Samoa 🇼🇸 and Fiji 🇫🇯? Bonus points if you know what issue I'm referring to

We'll be in Vienna Saturday till Tuesday. DM me or reply and let's meet and talk all about workflow orchestration, spatio-temporal datasets, catalogs, data pipelines and cal/val, in-orbit processing and clustered computing in space

10m resolution cloud-free Sentinel 2 mosaic over Ireland, Spring 2025 Open source code, ready to use, re-use, adapt, deploy as a service, and monetize The code for this, dev to interactive map, was developed, deployed, executed within a bit more than 2 person days

Tilebox is ready to Go! We just released our Go SDK and Go documentation, one of the most widely used languages for cloud-native apps. Each code snippet in the @tileboxio docs is now available in both Python and Go.

Tilebox is sponsoring the EUSPA Cassini Hackathon Austria taking place in Innsbruck this weekend, all participants get two months free Tilebox Starter tier to speed up Copernicus data access, prototyping and service development Participants, see you online!

Tilebox spatio-temporal datasets are now fully supported in all languages and you can create your own! Core problems this solves: - finding relevant data quickly (e.g. all Sentinel 2 granules along the US coastline) – Tilebox is now >100x faster than what's out there - storing auxiliary geographic data (e.g. weather stations, ground truth, aeronet data, ...) - cataloging higher level data

Tilebox at @cloudnativegeo in Snowbird! We present - the performance of spatio-temporal datasets - the flexibility of Tilebox workflows Ask Megan or Lukas for a coupon code if you want to try it out at high capacity!