Databases, Reimaginedwith Neural Intelligence
Traditional Databases Are Breaking Under AI
As datasets grow from gigabytes to petabytes, legacy row-and-column storage buckles under the weight of modern AI workloads.
Exponential Storage Costs
Sensor data, genomics, climate simulations, and media assets are doubling every 18 months. Row-based storage scales linearly — and so do your bills.
AI Cannot Natively Query
Neural networks speak tensors and gradients. Traditional SQL returns discrete rows — requiring costly ETL pipelines every time you want to train or infer.
Discrete, Not Continuous
Reality is continuous. Databases are discrete. Every interpolation, super-resolution, or missing value requires expensive post-processing that degrades accuracy.
| id | lat | lon | temp | hum |
|---|---|---|---|---|
| 1001 | 37.774 | -122.419 | 18.3 | 72 |
| 1002 | 37.774 | -122.419 | 18.4 | 71 |
| 1003 | 37.774 | -122.419 | 18.4 | 71 |
| 1004 | 37.775 | -122.420 | 18.5 | 70 |
| 1005 | 37.775 | -122.420 | 18.5 | 70 |
| ... | ... | ... | ... | ... |
From Raw Data to Neural Intelligence
Aedus transforms any structured dataset into a compact neural representation in three steps — no data scientists required.
Ingest Your Dataset
Connect any structured data source — time-series, spatial grids, sensor networks, scientific simulations. Aedus normalizes coordinates and values automatically.
Train the Neural Representation
A lightweight MLP is trained to fit your data as a continuous function. Aedus auto-tunes architecture depth, width, and encoding to match your accuracy targets.
Query Continuously
Query the trained network at any coordinate — including points never in the original dataset. Get interpolated, extrapolated, or derivative values in sub-millisecond time.
Built on SIREN Architecture
Aedus uses sinusoidal activation functions (SIREN) as its foundational architecture — enabling the network to learn high-frequency details and continuous derivatives of any dataset. Unlike ReLU networks, SIREN can represent fine-grained structure at any resolution.
Aedus vs Traditional Storage
Benchmark: 1 year of global weather sensor data (3.2 billion readings)
Built for Data-Intensive Science
Any domain with continuous, high-volume data can benefit from neural representation — here are the verticals we're targeting first.
Planetary-Scale Mapping
Replace multi-terabyte GIS datasets with INR models that store entire planet surfaces at arbitrary resolution. Query elevation, temperature, and satellite imagery at any lat/lon without tile pyramids.
Continuous Genomic Data
Encode protein structures, epigenomic signals, and variant databases as neural functions. Enable gradient-based biological queries impossible with SQL — find nearest neighbors in embedding space directly.
Weather & Climate Reanalysis
Compress decades of global weather reanalysis (ERA5, MERRA-2) into compact INR models. Query any atmospheric variable at any pressure level, time, and location — even between grid points.
Neural Video Compression
Encode video streams as spatiotemporal INR models. Stream any frame, crop, or timestamp on-demand with no keyframe dependencies — enabling truly random-access video at a fraction of H.265 storage.
Sensor Network Telemetry
Replace time-series databases like InfluxDB for IoT workloads. Train INRs on rolling windows of sensor data, enabling compressed, queryable, interpolated telemetry from millions of devices.
Physics & CFD Surrogates
Compress fluid dynamics, molecular dynamics, and finite-element simulations into neural surrogates. Query simulation state at any timestep or spatial point without re-running costly solvers.
The Science Behind Aedus INR
Built on a decade of neural representation research — NeRF, SIREN, Instant-NGP — and extended to general-purpose database workloads.
Full Stack Architecture
Core Innovations
Sinusoidal Positional Encoding
SIRENUnlike ReLU activations that lose high-frequency detail, sinusoidal activations (SIREN) preserve fine structure at all scales — critical for scientific and geospatial data.
Hash-Grid Encoding
Instant-NGPMulti-resolution hash tables accelerate training by 100× vs vanilla MLP approaches, enabling Aedus to fit large datasets in minutes rather than hours.
Differentiable Queries
Auto-diffEvery query is differentiable by construction. Compute gradients, Hessians, or integrals analytically — enabling physics-informed queries impossible in discrete databases.
GPU-Native Storage
CUDAINR weights live natively on GPU memory. Neural network inference is training-time too — eliminating all CPU↔GPU transfer overhead in ML pipelines.
Research Foundation
Ready to Compress
Your Data Universe?
Join the waitlist for early access to Aedus. We're onboarding select teams in climate science, genomics, and geospatial analytics first.
“We were storing 40 years of ERA5 reanalysis across a 12-node cluster. Aedus beta compressed it to a single NVMe card with better query performance than our Dask cluster. I genuinely couldn't believe it.”