The Intel Inside for BCI

Real-time, Explainable Neural Inference

What is a BCI?

A Direct Pathway Between Brain and Machine

A Brain-Computer Interface (BCI) is a technology that captures brain signals, analyzes them, and translates them into commands that are relayed to an output device to carry out a desired action.

Essentially, BCI allows for the direct control of computers or other devices using only thought.

From non-invasive EEG headsets to implantable neural interfaces.

Non-Invasive BCI
(e.g., EEG Headset)

Invasive BCI
(e.g., Neural Implant)

Slide 3: The Problem

The BCI Revolution Faces a Bottleneck

High latency (100–300ms)

Breaks immersion — low latency is a hard requirement for viable BCI products.

Low adaptability

Static models can't track changing brain states; retraining costs $10K–$50K per clinical session.

Black-box models

Uninterpretable outputs block regulatory approval and erode user trust.

These bottlenecks stall adoption across medicine, assistive tech, and VR/AR.

High Latency: A 100–300ms gap between thought and action breaks immersion and usability. At that delay, the brain perceives a disconnect — making low latency not a feature, but a hard requirement for any viable BCI product.
Low Adaptability: Static models can't track the brain's changing states. Retraining costs $10K–$50K per session clinically, plus weeks of engineering time — making iteration prohibitively expensive.
The "Black Box" Issue: Uninterpretable models block regulatory approval and erode user trust.

Together, these bottlenecks stall adoption across medicine, assistive technology, and immersive VR/AR.

Slide 4: Our Technological Core

Introducing Nimbus SDK: The Reactive Inference Engine

Nimbus SDK is a production-ready inference engine built for real-time brain signal processing. Unlike static ML models, it runs probabilistic models that continuously update as new data arrives — adapting to the brain's changing states without retraining.

At its core, Nimbus SDK uses reactive message passing on factor graphs — a technique pioneered by our team through RxInfer, an open-source Bayesian inference library developed by Lazy Dynamics, our co-founding company.

docs.nimbusbci.com →

<10ms Inference Latency

Real-time processing of EEG and neural signals with sub-10ms end-to-end latency.

Continuous Adaptation

Models update on every new sample — no retraining cycles, no downtime.

Interpretable by Design

Probabilistic outputs with uncertainty estimates — built for regulatory transparency.

Slide 5: The Competitive Landscape

Why BCI Processing Methods Fall Short

Criterion	LDA	SVM	Deep Learning	Bayesian Inference on NimbusSDK
Real-time Performance	High Very fast for simple, linear problems.	Medium Slower inference, especially with complex kernels.	Low Computationally heavy, often requires offline processing.	High (Reactive) Designed for streaming data and continuous updates.
Adaptability	Low A static model that must be fully retrained.	Medium Can be adapted, but it's often complex and inefficient.	Medium Requires large new datasets and extensive retraining.	High (By Design) Continuously learns and updates from new data points.
Robustness (to noise)	Low Highly sensitive to outliers and noisy signals.	Medium More robust than LDA but can still be affected.	Medium Can learn to ignore noise but requires vast data.	High (Explicit Modeling) Models uncertainty directly, making it resilient to noise.
Interpretability	High Feature contributions are clear and understandable.	Medium-Low Becomes a "black box" with non-linear kernels.	Very Low ("Black Box") Decision-making process is completely opaque.	Very High ("White Box") The entire model structure and reasoning is transparent.

Full LDA / SVM / deep-learning comparison on tablet and desktop.

Benchmark Validation

Same Accuracy. 1000× Faster Compute.

Session-to-session adaptation is one of the biggest challenges in EEG. Nimbus updates user-specific models in milliseconds while maintaining benchmark accuracy.

BNCI 2014-004 motor imagery · frozen CSP · CPU batch=1 · nimbusbench S3

~Same

Adapting-head accuracy

~10×

Cheaper updates vs sklearn LDA

~1000×

Cheaper updates vs PyTorch MLP

Final-round accuracy for session-adapting heads on BNCI 2014-004: Nimbus partial_fit matches sklearn LDA and MLP refit and PyTorch MLP refit within error bars

Head update cost vs evaluation round on BNCI 2014-004: Nimbus partial_fit stays lowest on a log scale while PyTorch MLP refit is orders of magnitude slower

Latency chart shown on tablet/desktop — key speedups above.

Slide 6: The Solution: RxInfer Inside

Real-time, Explainable Inference for BCI

We are building an SDK/API powered by RxInfer to serve as the core processing engine for the next generation of BCI devices.

Factor Graphs

For "white box" models that regulators and users can trust.

Variational Inference

For scalable computation balancing accuracy and speed.

Reactive Message Passing

For adaptive, real-time performance on streaming data.

The result: We reduce BCI latency from over 200ms to a target of 10-20ms.

Slide 7: Product Offering

The "Intel Inside" for Neurotechnology

A Core Engine: A powerful inference engine deployable in the cloud or at the edge.
Pre-built Models: A library of models for common BCI paradigms (e.g., motor imagery, SSVEP, P300).
Developer Tools: A comprehensive SDK and API for seamless integration into any neurotech stack.

Slide 8: Visual Pipeline Builder

Nimbus Studio

nimbusbci.com/studio

⚡ Key Features:

• Drag-and-drop interface • Real-time pipeline execution • Visual execution logs • No-code BCI development

Slide 9: Nimbus Studio Competitors

Why Existing Tools Fall Short

NeuroPype

Limitation $2,000–$5,000/year, desktop-only, no embeddable SDK

Nimbus 10× cheaper, web-based, embeddable SDK for hardware partners

OpenBCI GUI

Limitation No ML pipeline, no model training, visualization only

Nimbus Full ML pipeline from offline training to real-time deployment

MNE-Python

Limitation Code-only, requires deep Python expertise, no visual interface

Nimbus Visual drag-and-drop interface accessible to non-coders

MATLAB / BCILAB

Limitation Expensive license ($2K–$5K/year), legacy codebase, no cloud

Nimbus Modern cloud-native stack, no license fees, open integration

Custom dev team

Limitation $120K+/year per developer, 12–18 months to first working prototype

Nimbus SDK co-development at a fraction of the cost, 6× faster to market

Alternative	Limitation	Nimbus Advantage
NeuroPype	$2,000–$5,000/year, desktop-only, no embeddable SDK	10× cheaper, web-based, embeddable SDK for hardware partners
OpenBCI GUI	No ML pipeline, no model training, visualization only	Full ML pipeline from offline training to real-time deployment
MNE-Python	Code-only, requires deep Python expertise, no visual interface	Visual drag-and-drop interface accessible to non-coders
MATLAB / BCILAB	Expensive license ($2K–$5K/year), legacy codebase, no cloud	Modern cloud-native stack, no license fees, open integration
Custom dev team	$120K+/year per developer, 12–18 months to first working prototype	SDK co-development at a fraction of the cost, 6× faster to market

No existing tool combines a visual interface, Bayesian probabilistic outputs, and an embeddable hardware SDK in a single platform.

Slide 10: Target Customers

Three Validated Customer Segments

S1 Researchers & BCI consumers $49–199/mo

Validation ✓ 80+ on waiting list (zero marketing)

Who Postdocs, PI-led labs, enthusiasts, r/neuralinterfaces community

Channel Academic conferences, Discord, NeurotechX, r/neuralinterfaces

S2 BCI hardware startups $20K–150K

Validation ✓ BrainBit & PiEEG codevelopment contract

Who BrainBit, PiEEG, and similar hardware-first neurotech companies

Channel Direct outreach, BCI Society events, hardware accelerators

S3 Clinical teams (Q4 2026) $6K–60K/yr

Validation Planned Q4 2026 enterprise tier

Who Medical device manufacturers, hospital neuro departments, clinical research orgs

Channel Enterprise direct sales, medical device conferences, regulatory consultants

SEGMENT 1

Researchers, PhD Students & BCI Consumers

Who Postdocs, PI-led labs, enthusiasts, r/neuralinterfaces community

Channel Academic conferences, Discord, NeurotechX, r/neuralinterfaces

Price / Validation $49–$199/month ✓ 80+ on waiting list (zero marketing)

SEGMENT 2

BCI Hardware Startups

Who BrainBit, PiEEG, and similar hardware-first neurotech companies

Channel Direct outreach, BCI Society events, hardware accelerators

Price / Validation $20K–$150K contract + equity ✓ 1 commercial codevelopment contract (BrainBit & PiEEG)

SEGMENT 3 — Q4 2026

Medical Device & Clinical Teams

Who Medical device manufacturers, hospital neuro departments, clinical research organisations

Channel Enterprise direct sales, medical device conferences, regulatory consultants

Price / Validation $6K–$60K/year Planned Q4 2026 enterprise tier

Slide 11: Market Size & Growth

A $400 Billion Market Opportunity in the US Alone

~$400B

Total US TAM

BCI implants — Morgan Stanley, 2024

$50–100M

SAM 2025

$500M–1B

@ 5% pen.

$1–2B+

Maturity

$80.8B

Early TAM

~2.8M patients with critical impairments (ALS, stroke, SCI, epilepsy, depression)

First-wave adopters — horizon: 2035

$320B

Intermediate TAM

~6.8M follow-on adopters with moderate impairments as technology matures

Follow-on wave — horizon: 2045

~$400B

Total US TAM

Combined addressable market for BCI implants in the United States

Source: Morgan Stanley BCI Primer, 2024 →

▶ Nimbus Addressable Segment — The Software Layer Inside Every BCI Device

$50–100M

SAM Today (2025)

Developer tools & pipeline software for BCI labs and researchers

$500M–$1B

SAM at 5% BCI Penetration

SDK royalties + Studio SaaS as BCI devices reach early clinical scale

$1B–$2B+

SAM at Market Maturity

Per-unit royalties across millions of deployed BCI devices globally

Slide 12: Business Model

Two Independent Revenue Streams

1 Nimbus Studio SaaS $49–199/mo

ModelFreemium SaaS for researchers & BCI consumers

Validation✓ 80+ waiting list

2 Nimbus SDK $20K–150K

ModelB2B co-development + per-unit royalties

Validation✓ BrainBit & PiEEG contract

STREAM 1

Nimbus Studio

Freemium SaaS — researchers & BCI consumers

$49 – $199

/month per seat

✓Free tier — unlimited pipeline building, community support
✓Pro ($49/mo) — real-time execution, export, priority support
✓Team ($199/mo) — shared workspaces, advanced models, SLA

✓ 80+ on waiting list — zero paid marketing

STREAM 2

Nimbus SDK

B2B co-development — hardware startups

Nimbus BCI Engine documentation at docs.nimbusbci.com

$20K – $150K

per contract + equity/royalties

✓9-month co-development — full team + advisors access
✓RxInfer Pro license — embedded in partner hardware
✓Per-unit royalty — recurring revenue on every device shipped

✓ 1 commercial codevelopment contract: BrainBit & PiEEG

Slide 13: Partnership Model

We are the adaptive layer of the BCI stack.

Applications

motor imagery · P300 · assistive · VR

Adaptive layer

Nimbus SDK

Bayesian BCI inference

or

Deep learning

learned, end-to-end

Device middleware

streaming · filters · vendor SDK

Hardware

headsets · amplifiers · edge compute

Sensors

EEG · fNIRS · implants

Nimbus SDK
device middleware
stack layers

Nimbus SDK is our on-device inference engine for brain-computer interfaces. Hardware partners plug it in as the adaptive layer — closing the Sense → Learn → Act loop through your device stack, without hiring an ML team or shipping frozen end-to-end models.

What’s unique — it knows what it doesn’t know, so it can:

Gates decisions by confidence

Posteriors and entropy drive rejection and usable throughput.

Stays robust in noisy sessions

Uncertainty-aware decoding when electrodes slip or signal quality drops.

Adapts to neural drift

Online updates across trials — no weekly refit or cloud retraining.

Gates decisions by confidence

Posteriors and entropy drive rejection and usable throughput — not just the top class.
Stays robust in noisy sessions

Uncertainty-aware decoding when electrodes slip or signal quality drops.
Adapts to neural drift

Online updates across trials — no weekly refit or cloud retraining.

Slide 15: Traction

Early Validation — Before Funding

80+

Waiting List

Researchers & labs signed up with zero paid marketing

1

Commercial contract

Signed codevelopment with BrainBit & PiEEG — Nimbus SDK on hardware roadmap

✓

Live Product

Nimbus Studio publicly accessible with working pipeline builder

✓

RxInfer.jl — 500+ GitHub stars, used in 10+ academic publications

The inference engine powering Nimbus already has proven scientific traction

✓

Advisory board with Jeff Beck (Duke), Ryan Smith (LIBR), Bert de Vries (TU/e), Alexander Kuck

World-class scientific and industry validation secured

✓

Nimbus SDK & Nimbus Studio — production-ready

Both core products are built and available for partner integration today

Slide 16: Revenue Projections

Path to $1.1M+ ARR by Year 3

Year 1

$7K

Studio: Free beta — 0 revenue

SDK: 1 co-dev contract ($7K)

Year 2

$50K

Studio: early Pro adoption — $10K/yr

SDK: 1–2 contracts — $40K

Year 3

$1.1M+

Studio: 300 seats — $770K/yr

SDK: 3+ contracts — $300K+

▲

Upside: $4.5M ARR by Year 3

If one SDK partner ships 10,000+ devices with per-unit royalty, or enterprise clinical tier launches on schedule in Q4 2026, cumulative Year 3 revenue reaches $4.5M. This is structurally similar to the Arm licensing model applied to BCI hardware.

Slide 17: Fundraising

Raising $500K Pre-Seed

The ask

$500K

Pre-seed round (USD)

Use of funds

Product & inference hardening, benchmarks, and developer docs.

Partner delivery with BrainBit & PiEEG integration milestones.

Convert the 80+ researcher waitlist into Studio revenue.

Senior inference engineers and partner-facing technical hires.

The ask

$500K

Pre-seed round (USD)

Accelerate Nimbus Studio toward paid launch, scale the RxInfer-powered SDK for OEM partners, and fund execution on the signed BrainBit & PiEEG hardware codevelopment track.

Product & inference: production-hardening, latency and reliability benchmarks, developer docs.
Partner delivery: SDK integration milestones with hardware teams and pipeline-to-device tooling.
Go-to-market: convert the validated researcher waitlist into recurring Studio revenue.
Team: senior inference / tooling engineers and partner-facing technical roles.