AI × Natural Resources

Earth’s treasure.
AI is the finder.

Public data in. Undervalued mineral, energy, water & carbon assets out. Found, valued, and compounded by AI.

Thimar, scored positions

Illustrative data. Not a real asset.

5 basins scored

Parcel	Basin	AI valuation	Alpha	Score
VN-TR-4417	Vantrel Basin	$4,200	+$1.03M	94
QD-LM-3044	Lomaris Flat	$8,900	+$5.24M	96
HR-BK-4102	Brakewell Rise	$2,850	+$864k	89

By the numbers

Fifteen years, four asset classes, one target.

The record behind the platform, and the number it is being built toward.

15+ yrs

AI / ML

3

Companies founded

4

Asset classes

$1B

Goal by 2030

How it works

Public record in. Priced assets out.

Thimar reads the same public registries everyone can access, and prices what the market has not.

Public record →← Proprietary

01 IngestUSGS · BLM · RRC · TWDB

02 AnalyseDiscovery & valuation

03 ClassifyFour asset classes

04 CompoundToward $1B by 2030

Fig. 1. Thimar ingestion pipeline, public record to compounding position.

See the full architecture

What Maadin.AI is

Two engines.
One compounding mission.

A venture that acquires natural assets with AI, and an executive practice that helps industry leaders build their own AI capability.

The platform · VentureThimar, acquires & compounds assets

The practice · AdvisoryFractional CAIO / CRO

$1BOne mission · 2030

Fig. 2. Venture and practice, one mission.

The Platform · Venture

Thimar

Acquires and compounds critical mineral rights, O&G royalties and water assets, powered by a proprietary AI stack.

Explore the platform

The Practice · Advisory

Fractional CAIO / CRO

Arshad Khan embeds as a fractional AI or revenue executive, bridging AI architecture with enterprise commercial execution.

View engagements

The founder

15 years in AI.
Built for the physical world.

A rare mix: technical enough to architect the system, commercial enough to close enterprise deals. Now applied to the hardest problems in the physical world.

15 years · software → subsurface

NLP / CV

Deep learning

LLM / agentic

Physical world

Fig. 3. Fifteen years of AI, redirected at the subsurface.

Read the full story

What the engine covers

Four asset classes. One engine.

Each class is scored by the same pipeline, with models specialised to its data.

USGS MRDS

BLM LR2000

Texas RRC

TWDB / GCD

Satellite

ThimarScoring engine

Critical mineralsLithium · cobalt · REE

Oil & gas royaltiesPermian · Eagle Ford

Water rightsTX groundwater districts

Carbon marketsVoluntary + compliance

Fig. 4. Public sources fan into one engine, out to four asset classes.

The model

Royalties fund the next position.

Cash generated by acquired rights is reinvested. Platform subscriptions run alongside, so portfolio and software compound together.

01 DiscoverAI scores assets

02 AcquireRights secured

03 Cash flowRoyalties + SaaS

04 ReinvestLarger positions

$1BBy 2030

Fig. 5. The compounding cycle.

01 Discover

The engine scores undervalued tracts and rights against the public record.

02 Acquire

Rights and royalty streams secured at assessed-value baselines.

03 Cash flow

Royalty distributions and platform subscriptions generate recurring revenue.

04 Reinvest

Proceeds fund larger positions, the loop that compounds the portfolio.

Roadmap

Where the company actually is.

Phase status is unconfirmed. PLACEHOLDERS B5. No asset has been acquired to date.

Incorporate & build ThimarPhase 01 · Complete · Entity · pipelines · three prototypes

First asset acquisitionPhase 02 · In progress · Mineral rights or royalty · DOE / SBIR

Platform + portfolio scalePhase 03 · Target · Thimar as SaaS · 5–10 positions

$1B valuationPhase 04 · Target · Compounding · platform revenue

Fig. 6. Four phases, read top down. Phase 02 is current.

Status today

Entity & platformComplete

Working prototypes3 live

Assets acquiredNone to date

Current phase02 · In progress

The platform is built and the prototypes run. The portfolio does not exist yet, and the roadmap says so rather than implying otherwise.

Read the thesis

Proof of capability

Three working systems. Not slides.

Built on public data. Together they demonstrate the full path: discover, validate, monitor.

94

Mineral Rights Intelligence

Discovery, scoring and valuation across claim registries and county deed records.

5 BASINS SCORED · AI SCORE 83–96

AI Regulatory Filing Agent

Agentic AI preparing Texas RRC W-1 well permits end to end.

6 AUTOMATED STEPS · 100% VALIDATED

Texas Water Rights Monitor

Groundwater district tracking: permits, usage trends, acquisition windows.

4 DISTRICTS TRACKED · +15.4% 5-YR CAGR

Open the demos

Delivered for

Built where a wrong answer has consequences.

Fifteen years of shipping AI inside organisations that audit it.

Defence / federal

Mission systems and federal programme delivery.

Fortune 50 industrial

Heavy industry and large-scale operations.

Enterprise data platform

Platform architecture and data infrastructure.

Education

Teaching, curriculum and technical authorship.

Fig. 6. Four delivery sectors on one track.

Client names withheld pending permission. PLACEHOLDERS D7.

See the work

Investors

The thesis. The opportunity. The raise.

An uncrowded vertical, policy-backed demand, and an operator with AI depth and a business-development record.

01 Why now

Policy-backed demand

IRA & EU CRMA

02 Why Maadin.AI

Fifteen years of execution

AI × enterprise

03 The moat

Proprietary pipelines

Normalised public record

04 The model

Assets + SaaS + grants

Three revenue lines

Fig. 7. Four blocks of the investment case.

45%Thimar platform build

30%First asset acquisition

15%Team & operations

10%Data & infrastructure

Fig. 8. Phase 1 Seed, use of proceeds.

Request Seed Data Room (NDA)

Thinking in public

Market theses, in the open.

Four threads, one argument: the public record is mispriced and AI is what reads it.

Market thesis

Critical minerals

Why the convergence matters

Founder journal

Building in public

Thesis to platform

Technical depth

O&G automation

What operators get wrong

Policy

Carbon markets

Credits as a data problem

Fig. 9. Four threads, one thesis.

Market thesis

Why AI × critical minerals is the most important convergence of the decade

MARKET THESIS · 8 MIN

Founder journal

Building Maadin.AI: from thesis to platform in 90 days

FOUNDER JOURNAL · 7 MIN

Technical depth

What Permian Basin operators get wrong about AI adoption

TECHNICAL DEPTH · 6 MIN

Policy + opportunity

Carbon credits are a data problem. AI is the solution.

POLICY + OPPORTUNITY · 7 MIN

All insights

Contact

Let’s talk.

Investor, consulting client, or founder in natural-resource AI, it reaches Arshad directly.

Invest

Seed round · data room under NDA

Partner / build

Platform and data partnerships

Advisory

Fractional CRO / CAIO engagements

All three reach Arshad directly, not a support queue.

Fig. 9. Three routes, one inbox.

Next step

Two ways in.

Request the data room Work with Arshad

Request the seed data roomNDA required

Open the live demos3 prototypes

Work with Arshad2 slots

Earth’s treasure.AI is the finder.