Advanced Nonlinear Technologies · Confidential
or

Advanced Nonlinear Technologies

Own the AI
you use.

Frontier-grade AI, 8× lighter. Runs on your hardware.

London
gaurav@nonlinear.technology
The market

A market growing faster
than businesses can afford it.

$400B+
Enterprise AI by 2027
Software, services, and infrastructure combined.
80%
Goes to inference
Running models in production, not training them. The operating cost of AI.
70%
Of pilots fail
Most enterprise AI never reaches production. Cost and integration are the top two blockers.

Composite figures from IDC, Gartner, McKinsey 2025–2026 reports. Inference share of AI infra spend converges across analysts.

The problem

Every business wants AI.
Most can't deploy it.

Too expensive
We're spending too much on Claude Code.
Too sensitive
Our procurement won't let us send patient data to a US cloud.
Too generic
The model doesn't know our protocols.

Composite operators, 2026

The solution

We make AI
8× lighter.

8×
lighter, frontier-grade on your task
~80%
cheaper to deploy
100%
runs on the customer's hardware

Cheaper. Private. Fits your domain.
Same AI capability. Built to ship anywhere, including offline.

Not quantisation. Not pruning. A new neural architecture, nonlinear by design. Shipped as the Fern series of models.

The route

Cloud now. Device next. Silicon after.

Stage One · Now
Cloud
Two cloud surfaces today: EverydaySeries for operator partnerships, Fernfly for developers. Customers pay 20–40% less per query. Revenue today, growing through Series A.
Stage Two · Series A
Device
Purpose-built, device-resident models. Customers bring their SFT data; EverydaySeries fine-tunes and packages an ANT model for their device target. The fine-tuning factory.
Stage Three · Series B+
Silicon
Custom silicon. ANT-designed hardware optimised for ANT models. ASIC patents filed.
Concept validated on FPGA.
Alongside · All stages
OEM License
Architecture licensed at chip and firmware level. Once cloud, device, and silicon are proven, the licensing conversation writes itself.

Each stage funds the next. Each stage de-risks the next.
One vision, built in four stages.

Why small

Why small?
Both sides win.

For the customer
Easy to deploy. Easy to afford.

Fits where frontier models can't.

Runs on hardware they already own.

Lower cost per query. Lower total cost.

For us
Capital-efficient by design.

Trained on older GPUs.

Backed by UK government grants.

Architecture-led, not compute-led.

A capital-light path to frontier-grade AI.

Same AI outcome.
A fraction of the capital. A model that ships anywhere.

How the AI works

The AI gets cheaper, smarter,
and yours.

Month 1
Day one savings
Agents run on Claude, Claude Code, or Codex underneath. EverydaySeries' caching, smart routing, and prompt optimisation cut token costs from the first query.
Up to 90% less cost
Month 3
Hybrid routing
ANT fine-tunes a small model on your usage. Cheap tasks like classification, routing, and summary move to ANT.
+ ANT takes cheap tasks
Month 6
ANT-first
ANT handles 70%+ of the workload. The model gets sharper on your domain every week.
+ Tuned to your domain
Month 12
Customer-owned
Open-source frontier (Llama, DeepSeek, Qwen) + your ANT model, both delivered via EverydaySeries on your infrastructure. Your workflows don't change. You own the stack.
You own the AI
Frontier → Hybrid → ANT-first → Customer-owned
Why us

A different way
to build the model itself.

8×
lighter
can offer up to 60×
3
modalities validated
◆A new family of native small models. Same training compute, far fewer parameters, quality retained.
◆Validated across 3 modalities: text, vision, and decoder language models.
◆One architecture, designed for three deployment surfaces: cloud (today), device (next), silicon (after).
◆Patent-pending.

Not quantisation. Not pruning. A proprietary nonlinear architecture. The market is catching up. Google and MIT published independent confirmations in 2026.

The proof so far

Three modalities. Receipts.

Text encoder · BGE-large
7.2×
99.5% quality retained
Decoder LM · Pythia
11.4×
~90% retained
Vision encoder · DINOv2
9.4×
80% top-5 retained

SFT makes the results better. The curve only gets better from here.

Traction

Traction.

3
Paying customers
incl. a custom 100M ViT trained on 10M proprietary images
$22K
ARR
$250K
Compute used to get here

Backed by UK government compute grants.
Patents filed on construction methodology + ASIC architecture.
Concept validated on FPGA.
POCs in exploration: an NHS trust hospital and a large pharma.

Partner programs
Google for Startups
Microsoft for Startups
Microsoft Partner
NVIDIA Inception
AWS Activate
AIRR
NatWest
Aiven
Appwrite
Go-to-market

Two products.
One model family.

EverydaySeries · Top-down · Operators
The agentic platform and the training loop.

We target operators sitting on decades of proprietary data they cannot send to the cloud. Fern models run on their device or local hardware. Wrong responses flow back into EverydaySeries; the next version ships smarter. Co-ownership, not reselling.

Live proof: a hotel food-waste analytics company, 50MB on-device model trained on 10M annotated images, working in no-wifi kitchens.

everydayseries.com
Fernfly · Bottom-up · Developers
Drop-in intent-to-action AI for any app.

Free signup, drop-in 12M Fern-FC via one API key. Wrong or uncertain responses flow back into EverydaySeries' retraining loop, the model gets sharper with every conversation.

Live proof: a SOFR bond trader on RiskVal + Bloomberg, 12M local Fernfly model triggers their repeatable queries — more trades per day.

fernfly.com
The flywheel

Errors become training data.
The moat compounds.

01
Fern runs locally
On the customer's device or local hardware. Free per call. Fast.
→
02
Wrong answers surface
Misclassifications, uncertain calls, edge cases. Captured at the source.
→
03
EverydaySeries retrains
Real production errors become labelled training samples. Next-version Fern trains on what matters.
→
04
Smarter Fern ships back
Customer's device or app updates. Cost per correct call drops. The loop starts again.

The architecture is the entry moat. The flywheel is the compounding moat. Every customer makes every Fern model better.

Go-to-market · the detail

Push deals.
Pull developers.

Push · EverydaySeries into operators
We slot between their data pipeline and their teams.
  1. Partner already collects data or runs a pipeline. We pre-configure EverydaySeries to fit inside it.
  2. Day one: out-of-the-box cost reduction on their existing agentic LLM usage.
  3. Months later: we fine-tune on their data → specialised Fern models at a fraction of the cost. (e.g. 50MB on-device food-waste model, 10M annotated images, working without wifi.)
  4. The loop: wrong responses flow back into EverydaySeries → next-version Fern model trains on real production errors → ships back to the customer's device.
1 deal signed. POCs in exploration with an NHS trust and a large pharma. Each deal seeds SMB and mid-market discovery downstream.
Pull · Developers adopt Fernfly
Developers integrate Fern by using Fernfly.
  1. Free signup. One API key. Set up in an afternoon.
  2. App developers ship intent-to-action AI with no per-message cost, sustainable because the model is tiny. (e.g. SOFR bond trader on RiskVal + Bloomberg — 12M local model handles their repeatable queries.)
  3. The loop: every misfire becomes a training sample. Fern-FC retrains in EverydaySeries. The next release is sharper, the cost-per-correct-call drops.
  4. Enterprise pull-through: apps that need private, on-device Fern deployment, the use cases public APIs cannot serve.
Each integration validates the nonlinear thesis on production workloads and feeds the Fern series roadmap.

Two products. One model family. Errors become training data. The moat compounds with use.

Competition

Different market.
Different economics.

OpenAI / AnthropicDeepSeek / KimiThinking MachinesANT
Model size at deploymentHugeLarge (up to 685B)Same as baseSmaller than the base
Platform around the modelAPI onlyNoneAPI onlyEverydaySeries + Fernfly
How you use itAPI onlyDIY weightsAPI for engineersDIY or done-for-you
Developer accessPay per callDIY weightsPay per callFernfly · free per call
Cost per queryHighLowLowLowest

OpenAI: API.
DeepSeek: weights.
Tinker: fine-tunes someone else's model in the cloud.
ANT: a nonlinear model, on your hardware, inside a platform.

Inference runtimes (Cactus, ExecuTorch, MLC) and fine-tuning infra (Tinker, Baseten) are both commoditising. We sit above both, with a model that fits in them.

Team

Built for this.

Founder · CEO · London
Dr Gaurav Gandhi. PhD nonlinear systems. Five years chip design at STMicro + Cadence. Prior founder experience. Royal Acad. of Eng. LIF Fellow.
Head of Engineering · Part-time · India
ex-Automattic, ex-Deel. 15+ years experience. Production systems at global scale.
Software Architect · Full-time · India
MSc Applied Mathematics. 10 years building web and mobile products.
Full-stack Developer · Full-time · India
BSc Computer Science. 10+ years full-stack experience.
CCO · Part-time · London
Built, scaled, and exited tech businesses. Operational MBE. Defence and public sector procurement.
Head of research · Joining · London
Cambridge PhD, mathematics. Publication and IP.

Already in motion. Active part-time today, full-time at raise close.

What this raise buys

What this raise buys.

Build the Fern series
Fern-LangNative small general-purpose LLM. 8× lighter, frontier-grade on the workload. Fern-AgentNative agentic reasoning. The flagship that powers EverydaySeries at scale.
Scale Fernfly
Developer signupsFree tier flywheel. Integrations live in production apps. Function calls servedReal-world signal feeds back into the Fern series. Free → paid conversionPrivate and on-device deployments for the enterprise pull-through.
Grow EverydaySeries
Operator deploymentsConvert NHS, pharma, and supply-chain POCs into contracts. Custom Fern deliveriesThe ViT pattern, repeated across verticals. First on-device pilotReal users. No network. Same quality.
The Series A pitch is three things
◆Two more Fern models shipped and benchmarked
◆Fernfly developer adoption + EverydaySeries operator revenue
◆The first on-device Fern deployment in production

The UK government grant pipeline continues alongside this raise. Will pursue more non-dilutive grants.

The ask

The ask.

One or two co-leads

Check email for exact ask. Or contact gaurav@nonlinear.technology

By Series A · 18 months
10+ paying customers across SMB and mid-market. Growing revenue. First device deployment live. ARR running rate to support a Series A.
Advanced Nonlinear Technologies Ltd · London
gaurav@nonlinear.technology