Bitruvius
Earth from orbit at dawn — a Substrate film still

First Principles,
Engineered.

The infrastructure layer powering the next generation of spatial computing.

Seed round now open US$8–12M
0×faster point-cloud read vs LAZ
0×in-browser decode vs incumbents
0%smaller splats vs Niantic SPZ
GPU + browserdecode shipped · bit-exact
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01 The Problem
Every map.
Every satellite.
Every LiDAR scan.
Every digital twin.
Every AI model.
Still runs on infrastructure designed decades ago.
Storage tax
Bandwidth tax
Compute tax
AI tax
User-experience tax

Spatial computing evolved. Its plumbing didn't. The world is measured continuously — then buried in formats built for machines that no longer exist.

02 The Ah-Ha

One modern stack, rebuilt from the physics up.

Not a faster library bolted onto legacy formats — a coherent infrastructure layer purpose-built for cloud, AI, browsers, and GPUs. Bitruvius owns the representation; Irreduce owns the data plane.

Replace the plumbing under geospatial once, and every application on top gets faster, smaller, and cheaper at the same time.

ApplicationsGIS · AI · Robotics · Twins
Open SDK EcosystemJS · Rust · Python · MapLibre
Vault — Cloud PlatformManaged
Irreduce™ — Data Plane & File FormatIn dev
Spatial Processing Engine™SIMD · MT
CodecsRIPT™BVC™Turbo
Modern Cloud InfrastructureObject store native
03 Imagine the Engine Swap

Imagine if the car you drive now could do the impossible.

The car stays. The engine changes.
Same car.
Same roads.
Same tank.
A completely different order of performance.

No migration. No rip-and-replace. Bitruvius swaps the data engine.

Only the engine changes.
Efficiency138MPG
5.2–5.7× smaller than raw, lossless
Range2,490miles per tank
18-gallon tank · was 461
Speed1,760MPH
32× faster decode in the browser
138 MPG / 2,490 miles per tank / 1,760 MPH. That is exactly what Bitruvius does for spatial data.

Automotive analogies from measured BVC point-cloud benchmarks (Autzen, 10.65M points, lossless). MPG: today’s car is 25.6 MPG (2024 average, short-wheelbase vehicles and light trucks) = raw LAS at 34 bytes/point; BVC stores 6.0–6.5 bytes/point, 5.2–5.7× smaller (LAZ: 4.5×), so 25.6 × ~5.4 ≈ 138 MPG. Range: an average 18-gallon tank, 461 → ~2,490 miles. Speed: 55 MPH × 32×, BVC’s measured in-browser decode against incumbent formats (Chrome, Apple M3). Single-core read is 17–25× faster than LAZ.

04 Proof · Measured, Reproducible

The numbers, shipped in beta.

Points · BVC vs LAZ
0×
17–25× faster read
Single-core, x86 & Apple M3 — at a smaller file (−4.5% to −14.4%). LAZ is serial by format; BVC is parallel by design.
Decode · BVC
GPU + browser
Decode shipped
End-to-end, bit-exact on Metal AND Vulkan, both profiles. ~32× in-browser decode vs incumbents.
Splats · BVC vs SPZ
0%
Smaller than Niantic SPZ
At ~6× decode, with better color fidelity (4.3 vs 6.6 per 255). Beats the reference splat codec on both axes.
Lossless imagery · Turbo
Byte-identical
Faster, provably lossless
turbo-webp byte-for-byte identical at faster encode; turbo-lepcc ~1.75× byte-exact vs Esri C++.
Wire format v1.0.0 frozen. Every figure above is reproduced by committed benchmark harnesses (tech-docs/04) across x86 and Apple silicon, with cross-architecture byte-identity verified. No projections on this slide.
05 Spatial Engine · The Codecs at Work

Same files as the industry standard. Faster.

Reproject imagery
4.4×
Faster to fit imagery to a map
Same method, pixel for pixel. Windows: won all 203 runs. Mac: 3.5×, all 218.
Memory
0.1×
Memory grows incrementally
A 1.5 GB satellite strip reprojected in 233 MB and 4 seconds. The standard tool: 2.3 GB and 21 seconds.
Convert for the cloud
3–3.7×
Faster to write, same file
Windows 3.1–3.7×, Mac 3.0×. Same size, and every file opens in the standard tool.
Elevation · lossless
0%
Of the standard tool's file size
29 MB against 71 MB, zero error, in a newer format version the standard tool can read but cannot write.
Spatial Engine v0.1 is the first application on the codecs: the map and tile server, plus the tools that prepare data for it. The industry standard is GDAL, the open-source toolkit the geospatial industry runs on; measured against GDAL 3.13.3 on 10 production files from ICEYE, Planet and USGS, macOS and Windows; Linux run pending. A pilot is one day on the customer’s own files. Full figures: bitruvius.com/spatial-engine.
06 The Substrate — Vision Film
90 seconds. The whole thesis. Two thousand years after Vitruvius — a new treatise on how we build.
07 Products

Three ways the value monetizes.

Platform

Vault

Cloud-native spatial platform — managed storage, streaming and serving on the new formats. Recurring, consumption-priced.

Licensing

Codecs — Paid Encoders

RIPT, BVC and Turbo encoders licensed to hyperscalers, sensor OEMs and platforms. High-margin representation IP.

Adoption

Open SDKs

JavaScript · Rust · Python · MapLibre. Free decoders drive ubiquity; paid encode and enterprise integration capture it.

08 Market

Everyone who touches a pixel or a point on Earth.

Defense
Earth Observation
Utilities
AEC
Cities
Mining
Telecom
Hyperscalers
Robotics

Infrastructure sells horizontally. The same representation layer serves a defense GEOINT pipeline, an EO constellation, a digital-twin platform and a robotics fleet — one codec, every vertical. Cross-cutting, and sticky once it's the format.

09 Same Infrastructure · Twenty Times the Road

The storage you already own, twenty lanes wide.

You already bought the storage and the racks. We change what they can carry — without touching the wire format.

Incumbent formats100–600 MB/s
With Bitruvius · same hardware1.6× faster decode · 30–80% fewer bytes · 0 lines rewritten
FOR THE OPERATOR

More workload per rack, per campus, per dollar.

The petabytes you already store fit in a fraction of the space — and read back faster.

FOR THE GRID

Less power drawn. Less water evaporated.

Capacity you don’t have to build is the cheapest capacity there is — and the cleanest.

AND IT COMPOUNDS

Every new sensor makes the saving bigger.

Spatial data grows ~28% a year. The saving grows with it.

The cheapest data centre is the one you never build.

Not an offset. Not a pledge. Decode throughput and byte reduction vs incumbent libraries — measured, reproducible, bit-exact across x86 · Metal · Vulkan.

10 Climate Infrastructure

Advanced imagery codecs that help reduce data-center infrastructure demand.

Every byte we don’t store is a byte nobody powers, moves, or cools. Cumulative 2027–2030.

The wall the AI build-out is hitting
945 TWhpower by 2030 — double 2024
~28%/yrspatial data compounding
$1.7T/yrbuild-out by 2030
Electricity never drawn
25.1 TWh
Equivalent to 2.4M home-years of power
CO₂e avoided
8.9 Mt
Equivalent to 1.9M car-years off the road
Cooling water saved
45 bn L
Equivalent to 18,000 Olympic pools
Same pixels. Same points. Same science — a fraction of the footprint. Not a carbon offset. Not a pledge. A physical reduction in what gets stored, moved, and read. The only climate story that gets stronger as data grows.

MODELED from IEA Energy & AI (2025) and Dell’Oro (2026), on conservative realization of MEASURED Bitruvius benchmarks. Value created across the ecosystem — not revenue captured. Equivalents: 10,500 kWh per US home-year · 4.6 tCO₂ per car-year · 1.8 L/kWh cooling · 2,500 m³ per Olympic pool.

11 Team · Insider Architecture

The people who hit these limits — then left to fix them.

Geoff Taylor
Founder · Co-CEO & CTO
Architecture & core engineering. 14+ yrs in 3D/imagery/remote sensing; led solutions architecture at Nearmap (SDKs, APIs, petabyte pipelines); VP SA at PropertyFi; ~decade at Esri.
Steven P. Santovasi
Co-Founder · Co-CEO & CPO
Product & GTM. 30+ yrs geospatial/imagery/digital-twin/AI; Esri Senior PM, Nearmap Director of Product, Esri SAG Award.
Josh Budinger
Co-Founder · COO
Operations & commercial. 8 yrs at Nearmap (VP Commercial Sales, N. America); led sales at PropertyFi; currently VP BizDev at Kiln.
Gengen He, Ph.D.
VP R&D & Strategic Partnerships
R&D and strategic partnerships. Spatial computing & algorithms depth across the representation and compute stack. Previously Esri and Alibaba AMAP; taught at the University of Nottingham Ningbo.
Dan Hedges
VP Solutions Architecture
Solutions architecture. Enterprise integration and reference deployments across geospatial platforms. Previously Esri and Rendered.ai.
Steve Swenson
VP Business Development
Enterprise sales & BD. ~6 yrs at Nearmap (AE → Strategic AM → Sales Manager); Enterprise Sales Manager at PropertyFi.

4 operators previously worked together at Nearmap, 4 at Esri, and hit the exact format and infrastructure limits Bitruvius exists to solve.

12 Roadmap

Shipping ahead of the raise — not after it.

OCT 2025
Provisional patents · initial prototypes
JAN 2026
Codecs shipping · provisional patents extended
APR 2026
Bitruvius SDK · MapLibre integration
JUL–SEP 2026
JS SDK GA · Turbo beta · Spatial Engine v0.1 · Microsoft for Startups
◄ You are here
OCT 2026
Python SDKs · Vault preview
JAN 2027
Seed close · Irreduce

The technical risk is largely retired. What the capital buys is speed to standard — becoming the format before an incumbent copies the idea.

13 The Path · Seed, then Series A and B

Each round is raised on proof from the last.

SEED · NOW · $8–12M

Spatial Engine, RIPT, BVC and 10+ Turbo codecs to GA · closes Jan 2027

Core codec risk is retired; the seed funds productization. Spatial Engine, the first application on the codecs, taken to general availability. Two new codecs, RIPT and BVC, built, hardened and released to GA. 10+ Turbo codecs brought to production-grade GA. Core engineering and go-to-market teams in place.

SERIES A · $37–48M

Platform, GPU & standard · 2027 → 2028

Rust, Python, JS, C++, .NET, Go and Java SDKs; Vault and Irreduce; ArcGIS Pro / QGIS integration; sales motion starts. Then GPU-native codecs, sovereign cloud, Vault on-prem. Then OGC / ISO submission and hyperscaler and sensor-OEM integration. Growing to ~59 people.

SERIES B · $75–110M

Scale & category leadership · 2029 → 2030

Raised once the standard is set and Vault revenue is live. Irreduce data plane at petabyte scale; defense and sovereign accreditation; hyperscaler and OEM licensing across all three clouds; default format across verticals. 75 → ~180 people.

14 Vision
Storage ↓→ Processing ↓→ Data plane ↓→ Spatial intelligence
Collapse the cost of representing the world, and you unlock the physical-intelligence infrastructure everything else gets built on.

Less storage to buy. Fewer data centers to build. Less power to burn. The same bytes, everywhere, decoded anywhere — the substrate under the next decade of spatial AI.

15 An Invitation to Invest
US$8–12M
Seed round · now open
Seed · now
$8–12M
To GA: Spatial Engine, 10+ Turbo codecs, RIPT and BVC · engineering and GTM teams in place
Closes Jan 2027
Series A · follow-on
$37–48M
SDKs across languages · Vault cloud · GPU & sovereign · standard submission
Raised on seed milestones
Series B · follow-on
$75–110M
Irreduce data plane · defense & sovereign · hyperscale licensing
Raised once the standard is set and Vault revenue is live
The Unicorn Thesis
HorizontalInfrastructure sells into every vertical at once — winner-take-most, not winner-take-one.
StandardBecome the format and licensing compounds. The moat is the install base, and it's already forming.
InevitableSpatial & physical AI needs a substrate. Someone builds it this decade — we're already shipping it.
Horizontal infrastructure + a defensible standard + an inevitable market = a pure, category-defining unicorn opportunity.
Own the format, own the category. The seed is where it starts.
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Confidential — for institutional review only. Performance figures are measured and reproducible from committed benchmark harnesses (BVC vs LAZ/SPZ; Turbo vs reference codecs). Spatial Processing Engine throughput and Irreduce capabilities are development targets, not yet productized, and are labeled as such. Round sizes and sequencing are indicative and subject to definitive documentation; the Series A and Series B are anticipated follow-on rounds, not commitments. © 2026 Bitruvius / Irreduce.