AINNA NeuralOps Investment Thesis

Sovereign Infrastruktur AI for Malaysian PKS

AINNA NeuralOps is a Malaysian-built AI orchestration and private-infrastructure platform designed to make enterprise AI more controlled, efficient and locally governed. A detached-systems AI platform delivering predictable, subscription-based intelligence while keeping data in-jurisdiction.

Operator-built AI infrastructure proven first inside AINNA's own complex commerce operasi. This seed round is intended to convert internal technical validation into external commercial scale.

RM1.78MYear-1 recurring ARR target
RM88.4M5-year combined revenue target
259Senario library · 14 production systems
RM15M+Lifetime retail sales (operating company)
Thesis at a glance

AI builds. Sistem run. Knowledge stays tempatan. Nilai compounds in Malaysia.

We sell outcomes, not tokens a flat monthly subscription replacing unpredictable per-token AI pricing, with full data sovereignty for Malaysian enterprises.

RoundSeed
RaiseRM2,000,000
Ekuiti10%
ValuationRM18M pre · RM20M post
ModelSaaS + API + Perkhidmatan
DataOn-premise · local-first
Ringkasan Eksekutif

Financial Snapshot

One screen for bankers and VCs the model in a single view. Semua figures are TARGET projections unless labelled otherwise.

Year-1 HasilRM2.85M
Year-1 Recurring ARRRM1.78M
Year-1 Customers500
Blended ARPURM202
5-Yr RecurringRM55.3M
5-Yr CombinedRM88.4M
5-Yr Infra KosRM4.98M
Funding AskRM2.0M

Recurring MRR RM148,668 → ARR RM1,784,016 · Setup RM294,000 · Perkhidmatan RM774,000 · Total Year-1 RM2,852,016. Target

Masalahnya

Why Semasa AI Pelaksanaan Fails for Malaysian PKS

AI Tradisional creates a paradox: the more PKS adopt it, the more it costs and the more control they lose.

∅ The PKS AI Gap
  • Per-token pricing costs scale indefinitely with usage
  • Data leaves Malaysian premises privacy & sovereignty risk
  • Black-box models no customisation or control
  • Foreign jurisdiction regulatory uncertainty
  • Recurring costs forever no equity or asset built
  • Subject to API rate limits & downtime
✓ The NeuralOps Alternative
  • Predictable monthly subscription pricing
  • Data stays local designed for data locality and PDPA-aligned deployment
  • Full customisation your models, your rules
  • Malaysian jurisdiction, designed for data locality
  • Infrastruktur as an asset build equity
  • 24/7 autonomous controlled workload routing
Why Now

Why This Opportunity Exists Now

Macro tailwinds in enterprise AI connect directly to AINNA's operator-tested execution opportunity.

From Experiments to Operasi

Organisations are moving from isolated chatbot experiments to operational AI embedded in daily workflows. This creates demand for reliable, governed, cost-predictable AI infrastructure rather than per-token novelty.

§

Kos & Control Matter

Inference cost, governance and data control are becoming primary selection criteria. Enterprises increasingly want private/local deployment with predictable economics and auditability.

Operator-Tested Seni Bina

AINNA's architecture was built and validated inside its own complex commerce operasi. This puts the platform ahead of the typical vendor that must learn workflow reality at customer cost.

Penyelesaiannya

NeuralOps Sistem Berasingan Seni Bina

A three-layer platform serving different deployment needs. Detached AI infrastructure built from live operators and real workflows. The operating model is the Kecekapan Flywheel (Segmentation → Smart Routing → Distillation → Sistem Berasingan → Peribadi Infrastruktur). Semasa production layers are live for suitable workloads; larger-scale items are Phase 2 / funding-dependent. See the flywheel →

259Senario library
14Production systems
7-modelOn-premise LLM orchestra
87%Internal token benchmark
Tempatan-firstOptional controlled fallback
PDPA-alignedBy design (not certified)
259Senario library
14Production systems
7-modelOn-premise LLM orchestra
87%Internal benchmark
Tempatan-firstWith controlled fallback
PDPA-alignedBy design (not certified)

Platform NeuralOps

Teras subscription for AI orchestration, model management and workflow automation, with a VPN backbone for secure on-premise deployment.

  • 7-model LLM orchestra
  • Smart routing & workload management
  • On-premise deployment

NeuralOps API

API add-on enabling integration with existing systems, custom workflows and third-party applications. Usage-based AI credits.

  • RESTful API endpoints
  • Kredit packs: 10K=RM30 · 50K=RM120 · 100K=RM200
  • Tempatan-first routing (optional controlled fallback)
Δ

Vertical SaaS

Industry-specific applications built on NeuralOps, starting with financial-services compliance for Malaysia.

  • Bank Recompiler RM50/user/mo
  • Street Akaun Report RM100/user/mo
  • Lagi verticals in development
Commercial Model

Harga Seni Bina

Three-tier subscription with transparent add-ons. Semua prices are TARGET.

PKS

RM100

per month · 1–2 users

  • Platform NeuralOps access
  • Up to 2 users
  • Basic workflow automation
  • Setup: RM300 Target
  • API add-on: RM50/mo Target
Paling Popular

Perniagaan

RM299

per month · up to 5 users

  • Platform + advanced workflows
  • Up to 5 users
  • Custom model tuning
  • Setup: RM1,000 Target
  • API add-on: RM150/mo Target

Enterprise

RM1,000

per month · up to 20 users

  • Full platform + dedicated support
  • Up to 20 users
  • Custom development billed separately
  • Setup: RM5,000+ Target
  • API add-on: RM500/mo Target

Add-on Harga

Add-onPKSPerniagaanEnterprise
API add-onRM50/moRM150/moRM500/mo
Setup feeRM300RM1,000RM5,000+
AI credits (10K)RM30
AI credits (50K)RM120
AI credits (100K)RM200

Semua prices are TARGET. Custom development is NOT included in any plan and is billed separately. Setup fees and usage charges apply as listed.

Financial Model

Four Hasil Streams

Platform + API + Vertical SaaS + Perkhidmatan diversified, recurring-first model.

Recurring Hasil (MRR)

StreamMRR (RM)ARR (RM)
Platform subscriptions100,8801,210,560
API add-ons21,200254,400
Usage overage (10%)10,088121,056
Dedicated deployments6,00072,000
Bank Recompiler (110 users)5,50066,000
Street Akaun (50 users)5,00060,000
Total Recurring148,6681,784,016

Perkhidmatan & Jumlah Hasil

CategoryBulanan (RM)Annual (RM)
Bulanan service income64,500774,000
Setup fees (one-time) 294,000
Recurring revenue148,6681,784,016
Total Year-1 Hasil213,1682,852,016

Semua figures are TARGET projections. Actual results depend on customer acquisition, pricing validation, and deployment execution.

Year-1 Model

Year-1 Pelanggan Target

500 NeuralOps customers in Year 1. Target

350

PKS

RM100/bulan · 1–2 users

MRR contribution: RM35,000

120

Perniagaan

RM299/bulan · up to 5 users

MRR contribution: RM35,880

30

Enterprise

RM1,000/bulan · up to 20 users

MRR contribution: RM30,000

Total Platform MRR: RM100,880 Target

Total Platform ARR: RM1,210,560 Target

Pertumbuhan

Five-Year Projection Pengurusan Pertumbuhan Senario

500 → 8,000 NeuralOps customers · RM88.4M 5-year cumulative combined revenue target. This is a management scenario, not guaranteed growth.

MetricYear 1Year 2Year 3Year 4Year 5
NeuralOps Customers5001,0002,0004,0008,000
Recurring Hasil (RM)1,784,0163,568,0327,136,06414,272,12828,544,256
Setup fees (RM)294,000588,0001,176,0002,352,0004,704,000
Perkhidmatan (RM)774,0001,548,0003,096,0006,192,00012,384,000
Jumlah Hasil (RM)2,852,0165,704,03211,408,06422,816,12845,632,256
Total Recurring Hasil (5yr)
RM55,304,496
Total Combined Hasil (5yr)
RM88,412,496

Pelanggan growth assumes 100% YoY increase. Vertical SaaS scales linearly with customers. Perkhidmatan and setup fees scale proportionally. Projection

Pertumbuhan Senario Pengurusan Senario

Alternative customer-growth paths. These are directional senario, not forecasts of guaranteed outcomes.

SenarioYear-5 CustomersYear-5 ARRAssumptions
Conservative2,000~RM13.8MSlower conversion; ~400 baharu customers/year
Asas8,000~RM55.3MPengurusan scenario, ~100% YoY growth
Upside15,000~RM103.7MHigh conversion + channel leverage
Operasi

Infrastruktur Kos

VPS + GPU H200 RM4.98M over 5 years. Estimate

YearCustomersVPS KosGPU KosTotal InfraKos/User
Year 150050,000264,000314,000628
Year 21,000100,000264,000364,000364
Year 32,000200,000528,000728,000364
Year 44,000400,000792,0001,192,000298
Year 58,000800,0001,584,0002,384,000298
Total 1,550,0003,432,0004,982,000

VPS: RM100/user/year. GPU H200 (4× H200 141GB) server lease: ~RM22,000/bulan. Each server serves ~1,500 customers for LLM inference. Initial GPU servers funded from RM500K infrastructure allocation. Estimate

Unit Economics

Unit Economics Rangka Kerja

Based on target pricing final unit economics require validated commercial data.

Blended ARPU

RM202

Bulanan blended (Platform + API)

Estimate

VPS Kos/User

RM8.33

Bulanan infra cost per user

Estimate

GPU Kos/User

To Validate

Inference, acceleration, peak load

Estimate

Δ

Gross Margin

To Validate

ACV − VPS − GPU − support − direct

Estimate

Unit Economics Validation Table

MetricNilaiStatus
Blended ARPU (Platform + API)RM202/bulanEstimate
VPS cost per userRM8.33/bulanEstimate
GPU cost per userTo Be ValidatedEstimate
Total infra cost per userVPS + GPU + supportEstimate
Gross MarginTo Be ValidatedEstimate
CACTo Be ValidatedEstimate
LTVTo Be ValidatedEstimate

Final unit economics will be calculated using confirmed pricing, VPS provisioning, GPU inference, support overhead, and retention data.

Contribution Margin Equation (to be validated)

Hasil per customer − VPS − allocated GPU inference − support − payment fees − external-API fallback − storage/backup = contribution margin. Pengurusan will measure each line during the round; none of these are yet commercially proven.

Proof

Semasa Traction

Internal Peruncitan Kajian Kes verified historical data, clearly separated from external AI revenue.

Internal Peruncitan Operasi Historical Actual

AINNA's NeuralOps platform was developed and proven within the founding team's own retail operations managing 80,000+ SKUs across 30 active stores on Shopee, TikTok and Lazada, processing ~9,000 pesanan bulanan.

Penting: These are AINNA's own operating-company results, NOT external NeuralOps SaaS revenue. RM15M+ is cumulative lifetime retail sales of the operating company, not annual revenue and not NeuralOps platform revenue.

80,000+SKU Diurus
~9,000Pesanan Bulanan
30Aktif Stores
2019Ditubuhkan
RM15M+Lifetime Peruncitan Jualan
RM325KZakat & Taxes
259Senario library · 14 production
80,000+SKU Diurus
~9,000Pesanan Bulanan
30Aktif Stores
2019Ditubuhkan
RM15M+Lifetime Peruncitan Jualan
RM325KZakat & Taxes
259Senario library · 14 production

Internal Workload Benchmark Internal Benchmark

Smart routing demonstrated a 87% token reduction vs naive full-LLM routing in internal benchmarks an internal operational result, not a universal industry claim.

Naive Penghalaan
7.5M Tokens
Smart Routing
1.0M Tokens

Based on AINNA's internal operational workload. Keputusan may vary across models, infrastructure and use cases.

Commercial Validation

Validation Status

Transparent separation of internal validation, external pilots, and external revenue. Nothing is overstated.

Internal Validation Internal Benchmark

AINNA's NeuralOps platform runs real AINNA operating deployments (14 production systems) inside the company's own complex commerce operasi. This is the strongest validation currently available.

Luaran Pilots / POC Pipeline

Selected external pilots and proof-of-concept deployments are intended as the next commercial milestone. Confirmed paying external NeuralOps customers are not currently reported.

Luaran Paying Customers To Validate

None confirmed at the date of this deck. Luaran NeuralOps SaaS revenue is not claimed. The seed round is intended to convert internal validation into external commercial scale.

Pipeline / Strategic Discussions Estimate

Strategic discussions may exist but are not presented as partnerships, LOIs or revenue. No partnership, contract, certification or adoption is claimed without documented evidence.

NeuralOps has completed substantial internal validation. The next commercial milestone is conversion of selected external pilots into recurring customers.

Market

Market Opportunity

Bottom-up market sizing based on addressable customer count × realistic annual ARPU. No GDP or national-economic-conribution shortcuts.

TAM

Total Addressable Market

1.2M

MSMEs in Malaysia

  • 1.2 million theoretically addressable MSME customers
  • Annual theoretical revenue TAM ≈ RM2.9B Estimate
  • Formula: 1,200,000 × (RM202 × 12)
  • Sumber: PKS Corp / DOSM official MSME establishment data
SAM

Serviceable Addressable Market

120K

Digital-ready segment (10% of TAM) Estimate

  • 120,000 target customers
  • RM290.9M serviceable revenue Estimate
  • Digital/operational readiness assumption
  • Focused verticals: retail, logistics, financial services
SOM

Serviceable Obtainable Market

8,000

Year-5 target Target

  • 8,000 PKS deployed (Year-5 target)
  • Pengurusan growth scenario, not guaranteed
  • High-touch onboarding + channel leverage
Market Sizing Methodology

TAM = eligible MSME count × blended annual ARPU = 1,200,000 × (RM202 × 12) ≈ RM2.9B theoretical annual revenue TAM.

ARPU = RM202/bulan blended (management estimate across PKS RM100 / Perniagaan RM299 / Enterprise RM1,000 tiers, plus API add-ons). Estimate

SAM = digital/operationally ready share (management assumption of 10%) = 120,000 customers × RM2,424 annual ARPU ≈ RM290.9M. Estimate

SOM = Year-5 obtainable customer target of 8,000 = 0.67% of TAM count. Target

MSME count: ~1.2 million Malaysian MSMEs. Sumber: PKS Corp Malaysia / Department of Statistics Malaysia official establishment statistics, 2024. The precise 2024 figure is subject to verification against the live official release before publication. Luaran Market Data

Methodology: bottom-up customer-based sizing. This deliberately does NOT multiply MSME GDP, gross output, or national economic contribution to claim AINNA TAM.

Competitive Landscape

AINNA vs AI Tradisional Providers

A structural differentiation built on data sovereignty, pricing model and operator credibility.

∅ AI Tradisional Providers
  • Per-token penetapan harga (kos skala infinitely)
  • Data leaves premises (privacy risiko)
  • No customisation (black-box models)
  • Foreign jurisdiction (data sovereignty)
  • Recurring kos forever (no equity)
  • Subject to API rate limits & downtime
✓ AINNA NeuralOps
  • Predictable monthly subscription pricing
  • Data stays local (data-locality & sovereignty)
  • Full customisation (your models, your rules)
  • Malaysian jurisdiction (PDPA-aligned by design)
  • Infrastruktur as asset (build equity)
  • 24/7 autonomous (controlled workload routing)
Produk & Moat

Mengapa AINNA Could Win

An investor-quality moat argument. These are capabilities and cumulative IP, not generic AI features. Where a proposed advantage is easily replicated, we do not exaggerate it.

1

Operator-Built Knowledge

Built from years of actual commerce operations, not slide-deck use cases. The platform solves problems AINNA experienced first-hand.

2

Seni Bina

Segmentation → Smart Routing → Specialised Parsers → Sistem Berasingan → Peribadi Infrastruktur. Each layer reduces unnecessary cost and keeps execution predictable.

3

Reusable Aliran Kerja IP

259 published senario built from operational problems, reusable across customers as deployment accelerators. An accumulating, defensible library.

4

Data & Operational Learning

Lessons from real workflows inform routing and parser design, reducing unnecessary frontier-model calls and lowering cost per outcome.

5

Kos Seni Bina

Tempatan-first routing avoids unnecessary per-token costs while retaining an optional controlled external fallback where required.

6

Sovereign Pelaksanaan

Peribadi/local deployment with governed control over data location, access, routing policy, model choice and auditability. Optional fallback does not weaken sovereign control.

Why Is This Difficult to Reproduce in 6–12 Months?

The accumulated workflow library and operator-derived operational learning are the hardest parts to copy quickly. Umum-purpose AI providers can offer private infrastructure or customisation on request, so those alone are not a moat. AINNA's differentiating asset is the stock of reusable, production-tested workflow patterns built from real operasi.

Go-To-Market

Multi-Channel Strategy

How the 500-customer Year-1 target is intended to be reached. Acquisition logik is a GTM assumption, not an achievement. Target

1

Direct Jualan

In-house sales team targeting Malaysian PKS through events, referrals and digital outreach focused on retail, logistics and financial services.

2

Channel Partners

Teknologi consultants, system integrators and industry associations as reseller and referral partners under a revenue-share model.

3

Produk-Led Pertumbuhan

Self-service onboarding for the PKS tier, free demos and proof-of-concept deployments that demonstrate value before conversion.

Year-1 Acquisition Logic (GTM assumption)

The channels below are illustrative acquisition assumptions, not verified conversion data. CAC / conversion will be measured during the round.

ChannelTarget LeadsConversionExpected Customers
Direct outbound1,5008%120
Channel partners1,00010%100
Associations / vertical80010%80
Perintis / POC conversion15040%60
Produk-led / self-service3,0003%90
Strategic enterprise50100%50
Total Year-16,500500

Figures are management GTM assumptions pending validation. Actual conversion depends on market response, pricing validation and execution.

Peta Hala Tuju

Produk & Infrastruktur Peta Hala Tuju

Three-phase strategy from shared VPS to dedicated data center. Target

1

Phase 1 · Asas

Year 1 · 500 customers · RM2M funding

  • Shared VPS infrastructure
  • Up to 500 customers
  • VPN NeuralOps backbone
  • 30% engineering · 25% infra · 20% GTM
2

Phase 2 · Skala

Year 3 · 2,000 customers · Self-funded

  • Dedicated GPU servers
  • 500–2,000 customers
  • vLLM cluster deployment
  • Vertical SaaS growth & channel expansion
3

Phase 3 · Skala & Redundancy

Year 5 · 8,000 customers · Pengurusan target

  • Full data center
  • 2,000–8,000 customers
  • Multi-region redundancy
  • Exit / IPO preparation

Pelanggan growth assumes a management scenario of ~100% YoY growth. This is a target scenario, not a guarantee.

The Ask

Funding Ask

RM2M for 10% RM18M pre-money, RM20M post-money.

Total Funding Required: RM2,000,000

ASK

RM2,000,000

10% equity

PRE-MONEY

RM18,000,000

Before this round

POST-MONEY

RM20,000,000

After this round

Use of Funds

CategoryAllocationRM AmountPurpose
Engineering30%RM600,000Produk development, platform hardening, model optimisation
Infrastruktur25%RM500,000GPU servers, vLLM cluster, VPN backbone, VPS provisioning
Go-to-Market20%RM400,000Jualan team, marketing, channel development, pilots
Onboarding & Support10%RM200,000Pelanggan onboarding, documentation, support team
Kepatuhan & Perundangan5%RM100,000Data-protection alignment, legal structure, IP protection
Runway Reserve10%RM200,000Operating runway buffer, contingency
Total100%RM2,000,000

Detailed cap table & financial model available on investor request.

Why RM20M Post-Money? Pengurusan Proposal

RM20M post-money is a management-proposed valuation, not an externally proven figure. The rationale is based on the following assets and stage, not on comparable-company valuation multiples.

Operating Sejarah

RM15M+ cumulative lifetime retail sales of the operating company since 2019.

Proprietary Seni Bina

NeuralOps: segmentation, smart routing, specialised parsers, detached systems, private infrastructure.

Reusable Aliran Kerja IP

259 published senario across 33 verticals; 14 production systems.

Existing Infrastruktur

7-model local LLM orchestra and private-infrastructure deployment capability.

Founder Execution

Operator-built platform validated inside AINNA's own complex commerce operasi.

Seed Capital Need

RM2M funds engineering, infrastructure, GTM and onboarding to reach the next commercial milestone.

Seed ask RM2M for 10%: post-money = RM2M / 10% = RM20M; pre-money = RM20M − RM2M = RM18M.

Pasukan

Founding Pasukan

Manusia operator-led team. Proprietary AI development infrastructure (Agent TC) is presented separately as a technology asset below.

Nur Ain Syuhada

Co-Pengasas & CEO

Drives vision, partnerships and business development. Multi-platform ecommerce operator managing 80K+ SKUs.

Academic Background

Ijazah Perniagaan (Perniagaan Digital) — focus on Transformasi Digital, E-dagang & Strategi Inovasi (2023–Kini)

Experience

Ditubuhkan AINNA in 2019 and has since built it into a multi-platform ecommerce operator managing 80K+ SKUs across Malaysia and Indonesia. Leads vision, partnerships and business development, driving the company's growth from a local operation to a cross-border enterprise.

Rozni binti Enana

Co-Pengasas & Pengarah Strategi Pelanggan

Strategic planning, operations and company growth. Oversees cross-border trade corridors Dumai–Melaka and Medan–Port Klang.

Academic Background

Perniagaan Pengurusan & Strategi Pelanggan — Strategic Leadership & Mampan Perniagaan (Pembangunan Profesional)

Experience

2018–NowPengasas & Pengarah Strategi Pelanggan, AINNA
2016–2018Usahawan & Usaha Niaga — Various Industri

Masli Yahaya

Co-Founder & Pengarah Teknikal

30 years IT & Engineering. Architect of the Sistem Berasingan, 7-model LLM orchestra, and the broader scenario/workflow library.

Academic Background

  • Sijil of Competency — Marine Engineer Officer Class IV (Foreign Going), Jabatan Laut Malaysia (1995)
  • Diploma Kejuruteraan (Marin) — Gred A / Kelas Pertama, Ungku Omar Polytechnic (1994)
  • Kejuruteraan Mekanikal — Sek Men Teknik Ipoh (1989)

Experience

2019–NowPengarah Teknikal / CTO, AINNA
2014–2019Pengurus Media Sosial (IT), ICYM
2008–2014Pengurus IT, Allianze University College
2006–2008Pengurus Teknikal, QMEMS Sdn Bhd
2002–2006Jurutera Reka Bentuk Produk, Simyxta Microelectronics
1997–2002Jurutera Reka Bentuk Produk, ATL Electronic

Badrul Haziq

Kewangan & Perakaunan

Bachelor of Pendidikan (Accountancy) with hands-on experience in administrative work, asset management, and teaching. Applies accounting knowledge to support AINNA's financial operasi.

Academic Background

Bachelor of Pendidikan (Accountancy) with Honours — Sultan Idris Pendidikan University (UPSI), CGPA 3.5

Matriculation Program: Perakaunan — Melaka Matriculation College, CGPA 4.00 (Dean's List)

Experience

2026Trainee Teacher, SMK Tinggi St. David
2025Administrative & Kewangan Assistant, UPSI
2025Pelajar Worker, Pusat Penyelidikan Kanak-Kanak, UPSI
2024–25Part-Time Instructor (Ping Pong), UPSI

Muhamad Hakim Hafizi

Co-Founder & Ketua Logistik

Indonesia operations, vendor relations and logistics. Manages Dumai and Medan trade corridor operasi.

Academic Background

Ijazah Sarjana Muda Kejuruteraan Mikroelektronik — Universiti Malaysia Perlis (UniMAP), focus on Automasi, Sistem Kawalan & Logistik Teknologi (2022–Kini)

Experience

2019–NowCo-Founder & Ketua Logistik, AINNA

Muhammad Hakam Hafizul

Co-Founder & Head of R&D

Malaysia operations, warehousing and logistics. Leads R&D on sensor-based systems — optical sensors, RFID triangulation.

Academic Background

  • Ijazah Kejuruteraan Elektronik (Sedang Berjalan) — Industri Instrumentasi, Automasi Control, Sensor Calibration & Signal Memproses (2024–Kini)
  • Diploma Kejuruteraan Elektrik (Measurement & Control) — MJII (2020–2023)

Experience

2019–NowCo-Founder & Head of R&D, AINNA

Muhammad Firdaus Bin Nordin

Operasi & Pembangunan Perniagaan

Connects live operations with business development across warehouse, restaurant and e-commerce units. Supports AINNA's commercial delivery and customer-facing operasi.

Academic Background

Bachelor of Technopreneurship & Teknologi Pengurusan (Sedang Berjalan) — focus on Teknologi Pengurusan, Inovasi, Jualan & Pembangunan Perniagaan.

Sijil Profesional TRIZ · BTEC (Course Related), KESSUMA 2015 & 2016 — Inovasi & Penyelesaian Masalah.

Experience

2026–KiniPembantu Gudang (Separuh Masa), J&T Bukit Rambai, Melaka
2024–Jan 2026Penolong Pengurus Restoran, Cosmo Restaurant Sdn Bhd · Burger King, Melaka
2023Eksekutif Pembangunan Perniagaan (Latihan Industri), Ferryrich Sdn Bhd — 20% sales increase on TikTok in 6 months
TerdahuluCatering Staff (2021–2023) · Kakitangan Dapur (2021)

Mohd Shafiq Haiqal Bin Masli

Operasi E-Dagang

Operates storefronts, orders and catalogue workflows across marketplaces. Built and merged 10 online stores into AINNA, generating RM1M in sales across ~25,000 SKUs.

Academic Background

Pemesinan Industri — technical qualification in industrial machining, foundation in precision engineering and technical problem-solving.

Experience

2024–KiniOperasi E-Dagang, AINNA — merged 10 stores, oversees listings, pricing, campaigns, platform compliance
2021–2024Usahawan E-Dagang Bebas — 10 online stores, RM1M sales, ~25,000 SKUs
Teknologi Asset

Agent TC

Proprietary AI Development Infrastruktur · Internal Agent

Agent TC is AINNA's internal AI-assisted development and operational agen. It helps design and manage automation workflows, data pipelines and integrations across AINNA's stack. It is presented as a technology asset, not a human founder.

Peranan

Internal AI-assisted development and operations agen supporting engineering velocity. Specific model identity, context length and benchmark rankings are not claimed pending verifiable evidence.

Supporting Aset

14Detached systems in production
7Tempatan base models orchestrated (DeepSeek, Qwen, Llama, Mistral, Gemma, Phi, Command-R)
259Published scenario-library items

Based di Melaka, Malaysia · Full team bios & references available in data room. Agent TC is an internal technology asset, distinct from the human founding team.

Risk

Risiko & Mitigation

Transparent assessment of key risks and planned mitigations. Estimate

Pelanggan Acquisition

Targeting 500 customers in Year 1 requires effective GTM. Mitigation: multi-channel approach with direct sales, partners and PLG; pilot deployments to prove value before scaling.

Harga Validation

Target prices (RM100/RM299/RM1,000) need market validation. Mitigation: phased rollout, early-adopter pricing, continuous feedback, willingness to adjust tiers.

Infrastruktur Penskalaan

VPS cost estimate may vary with provider pricing and utilisation. Mitigation: modular design, multi-provider strategy, infra cost monitoring as a KPI.

Teknologi

Hybrid routing uses external API fallback not zero API dependency. Mitigation: transparent disclosure, continuous optimisation to minimise external API calls.

Competition

Established AI providers and baharu entrants. Mitigation: Malaysian PKS niche focus, data sovereignty advantage, operator-built credibility.

Regulatory

Data-protection and financial-services regulations. Seni Bina is designed for PDPA-aligned deployment and auditability, but formal PDPA certification for a given deployment requires separate legal evidence. Mitigation: compliance budget (5% of funds) and legal advisory.

Hasil Concentration

Year-1 targets are projections, not guarantees. Mitigation: diversified revenue streams and conservative growth assumptions.

Due Diligence

Evidence & Due Diligence

Verified data, clear labels, transparent methodology.

Data Labeling Panduan

Every major figure in this deck is labelled with its status:

Historical Past verified data
Target Intended pricing or goal
Projection Forward-looking estimate
Estimate Best-effort calculation
Internal Benchmark Internal test result
Verified Independently verifiable

Invest with AINNA NeuralOps

RM2M seed · 10% equity · RM18M pre · RM20M post. Target: RM2.85M Year-1 revenue · RM88.4M 5-year combined revenue.

Request Pelabur Meeting Back to The Ask

This document contains forward-looking projections and target estimates. Past performance of internal operations does not guarantee future results. Semua figures labelled with their status as applicable. See the risk section for a full discussion of uncertainties.

Pelabur Deck · Versi 2026.08 · Updated 21 August 2026 · Financial model last updated 21 August 2026

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