Advanced Nonlinear Technologies · Confidential
or
Advanced Nonlinear Technologies

Frontier AI
without the data center.

8× lighter. Runs on your hardware. Already in production.

ANT builds the Fern series of nonlinear models.
We ship through two products: EverydaySeries for operators, Fernfly for developers.

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.

Our slice, Edge AI, is projected at $60–119B by 2030. Growing ~20% CAGR, the fastest segment in AI.

Headline figures: IDC, Gartner, McKinsey 2025–2026. Edge AI projections: Markets and Markets, Grand View Research.

The problem

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

Operator · No connectivity
Our staff annotate in the kitchen. There's no wifi. Cloud AI doesn't reach where the work is.
Developer · Per-call cost
80% of my queries are the same. Why am I paying frontier API rates for repeats?
Enterprise · Data leaves the building
We can't send the data to OpenAI. We need the model to come to the data.

Two of our customers chose to do something different. Here's what they did.

Customer story · Operator

On-device AI
in no-wifi kitchens.

A food-waste analytics company in Dubai. Hotel kitchens. No connectivity.
The pain
Chefs photograph food waste and annotate it. Errors at annotation compound downstream. Kitchens have no wifi, so cloud AI cannot help.
What we shipped
A 50MB on-device model trained on the customer's 10M annotated images. Runs on a tablet in the kitchen. Wrong responses flow back online to retrain the next version.
Annotation works without wifi. Accuracy climbs every release. Paying customer, in production.
via EverydaySeries
Customer story · Developer

Repeatable queries,
locally.

A SOFR bond trader in NYC. RiskVal and Bloomberg every morning.
The pain
80% of his queries are the same day to day. Frontier APIs charge per call. Repeats eat trading time and add up.
What we shipped
A 12M Fern-FC model dropped in via Fernfly. Triggers his repeat queries locally, with no per-call cost. Misfires retrain in the loop.
Faster turns. More trades per day. In pilot, under test.
via Fernfly
The solution

Two customers.
One methodology.

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

Not quantisation. Not pruning. A new neural architecture — nonlinear by design.
Shipped as the Fern model series.

✓ Shipped · live
Fern-FC
12M function-calling. Via Fernfly.
✓ Shipped · live
Fern-Vision
50MB on-device vision. Via EverydaySeries.
Next
Fern-Lang
General-purpose LLM, 8× lighter.
Flagship
Fern-Agent
Native agentic reasoning.
The flywheel

Errors become training data.
The moat compounds.

01
Customer runs any LLM
EverydaySeries runs frontier APIs, open-weights, or Fern. On their device or local hardware.
→
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.

Every food-waste mistake. Every SOFR misfire. Each one makes the next Fern smarter.

Receipts

Methodology in lab.
Models in production.

In the lab — methodology benchmarks
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
In production — shipped customer models
Fern-Vision · food-waste
50MB
on-device, no-wifi kitchen
Fern-FC · SOFR trader
12M
local, repeat queries, $0/call
Delivery time
Months
custom Fern, not years

Same nonlinear methodology. Specialised to the task, in production. The curve only gets better from here.

Go-to-market

Two products.
One model family.

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

Operators sitting on proprietary data they cannot send to the cloud. EverydaySeries runs any LLM, frontier APIs, open-weights, or Fern. Errors flow back; the next version ships smarter. Co-ownership, not reselling.

Live customer: the food-waste hotels story (slide 04).
everydayseries.com
Fernfly · Bottom-up · Developers
Drop-in intent-to-action AI for any app.

Free signup, drop-in 12M Fern-FC, one API key. Free per conversation because the model is tiny. Misfires retrain in the loop.

Live customer: the SOFR bond trader story (slide 05).
fernfly.com

Two products. One nonlinear moat. EverydaySeries works with any LLM. Fernfly is the Fern-only express lane.

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

A nonlinear model, on your hardware, inside a platform that retrains it.

Roadmap

From here
to Series A.

Today · 2026
Two products live
EverydaySeries + Fernfly in production. Fern-FC and Fern-Vision shipped.
6 months
Fern-Lang shipped
Native small general-purpose LLM. First enterprise device SDK in production.
12 months
Fern-Agent · Fern Pi OS
Native agentic reasoning shipped. Fern Pi OS beta image — Raspberry Pi wedge for the Fern OS line. Custom Fern deliveries across verticals.
18 months · Series A
10+ paying customers
SMB and mid-market. ARR running rate to support the round. Repeat customers.
24 months+
Silicon
Custom ASIC for Fern models. Patents filed. FPGA validated.

Dated milestones, not stages. UK government grant pipeline continues alongside the raise.

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.
CCO · London
Built, scaled, and exited tech businesses. Operational MBE. Defence and public sector procurement.
Head of Engineering · India
ex-Automattic, ex-Deel. 15+ years experience. Production systems at global scale.
Head of Research · London
Cambridge PhD, mathematics. Publication and IP.
Software Architect · India
MSc Applied Mathematics. 10 years building web and mobile products.
Full-stack Developer · India
BSc Computer Science. 10+ years full-stack experience.

Already in motion.

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 behind EverydaySeries at scale. Fern Pi OSFirst vertical OS line. Raspberry Pi wedge. Maker + IoT distribution.
Scale Fernfly
Developer signupsFree tier flywheel. Integrations live in production apps. Function calls servedReal-world signal feeds the Fern series. Free → paid conversionPrivate and on-device deployments.
Grow EverydaySeries
Operator deploymentsConvert NHS, pharma, and supply-chain POCs into contracts. Land with any LLMCustomers start with frontier APIs. Cost and privacy walls push them to custom Fern. Custom Fern deliveriesThe food-waste pattern, repeated across verticals. First device SDKReal users. No network. Same quality.

The UK government grant pipeline continues alongside this raise.

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