Advanced Nonlinear Technologies · Confidential
or

Advanced Nonlinear Technologies

Own the AI
you use.

Frontier-grade AI, compressed 8× smaller. 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× smaller.

8×
smaller models, same quality
~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 way of compression.

The route

Cloud now. Device next. Silicon after.

Stage One · Now
Cloud
Cloud-hosted agentic AI through EverydaySeries. 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 compress the model itself.

8×
parameter compression
can offer up to 60×
3
modalities validated
◆A new family of efficient 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 compression method. 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

Bootstrapped. On a small compute grant. SFT makes the results better. The curve only gets better from here.

Traction

Traction.

3
Paying customers
1 already on current model
$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 motions.
Both already turning.

Top-down · Operators
Data-rich partnerships

We target companies sitting on decades of proprietary data they cannot send to the cloud: agriculture, healthcare, manufacturing, supply chain.

We give them AI. They give us data and distribution into their network. Co-ownership, not reselling.

Bottom-up · Self-serve
Workflow automation wedge

Individuals sign up for $29/mo. They automate their team's morning standup. Their manager notices.

The company onboards. The platform pulls itself into the org.

Go-to-market · the detail

Push deals.
Pull users.

Push · Pre-configured for partners
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 models at a fraction of the cost.
  4. Year-end: clubbed with open-source frontier (ANT or others) → offline delivery they own.
1 deal signed. POCs in exploration with an NHS trust and a large pharma. Each deal seeds SMB, mid-market, and operator customer discovery downstream.
Pull · Low-cost touchpoint
Every employee's AI reports for them.
  1. Scrum, standups, status updates exist because human time was the cheapest way to sync information.
  2. Cheap agentic AI flips the math. Each employee's AI reports their progress, drafts updates, answers questions on their behalf.
  3. Teammates trigger each other's agentic work on demand from inside the PA.
  4. Low-cost entry. The PA quietly diagnoses the team's most critical workloads.
Diagnosed workloads convert to low-cost agentic operations. The upsell path inside the same org.

Push seeds the larger accounts. Pull spreads from inside. Both feed the same flywheel.

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
How you use itAPI onlyDIY weightsAPI for engineersDIY or done-for-you
Cost per queryHighLowLowLowest

OpenAI: API.
DeepSeek: weights.
Tinker: fine-tunes someone else's model in the cloud.
ANT: a smaller 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 model
1.5B production releaseCompete with Qwen. 7B with reasoningTest emergent behaviours at scale.
Light up the cloud business
ANT models live in EverydaySeriesTrack adoption rate weekly. EverydaySeries on Microsoft MarketplaceDistribution into every Azure customer. Grow the customer baseConvert cost savings into recurring revenue.
Prove stage two
Shippable on-device SDKExtend the watch demo. First iPhone deploymentReal users. No network. Same quality.
The Series A pitch is three things
◆The 1.5B production model, benchmarked
◆The EverydaySeries adoption rate chart
◆The first on-device SDK 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

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