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 operations. This seed round is intended to convert internal technical validation into external commercial scale.
AI builds. Systems run. Knowledge stays local. Value 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.
One screen for bankers and VCs the model in a single view. All figures are TARGET projections unless labelled otherwise.
Recurring MRR RM148,668 → ARR RM1,784,016 · Setup RM294,000 · Services RM774,000 · Total Year-1 RM2,852,016. Target
Traditional AI creates a paradox: the more SMEs adopt it, the more it costs and the more control they lose.
Macro tailwinds in enterprise AI connect directly to AINNA's operator-tested execution opportunity.
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.
Inference cost, governance and data control are becoming primary selection criteria. Enterprises increasingly want private/local deployment with predictable economics and auditability.
AINNA's architecture was built and validated inside its own complex commerce operations. This puts the platform ahead of the typical vendor that must learn workflow reality at customer cost.
A three-layer platform serving different deployment needs. Detached AI infrastructure built from live operators and real workflows. The operating model is the Efficiency Flywheel (Segmentation → Smart Routing → Distillation → Detached Systems → Private Infrastructure). Current production layers are live for suitable workloads; larger-scale items are Phase 2 / funding-dependent. See the flywheel →
Core subscription for AI orchestration, model management and workflow automation, with a VPN backbone for secure on-premise deployment.
API add-on enabling integration with existing systems, custom workflows and third-party applications. Usage-based AI credits.
Industry-specific applications built on NeuralOps, starting with financial-services compliance for Malaysia.
Three-tier subscription with transparent add-ons. All prices are TARGET.
RM100
per month · 1–2 users
RM299
per month · up to 5 users
RM1,000
per month · up to 20 users
| Add-on | SME | Business | Enterprise |
|---|---|---|---|
| API add-on | RM50/mo | RM150/mo | RM500/mo |
| Setup fee | RM300 | RM1,000 | RM5,000+ |
| AI credits (10K) | RM30 | ||
| AI credits (50K) | RM120 | ||
| AI credits (100K) | RM200 | ||
All prices are TARGET. Custom development is NOT included in any plan and is billed separately. Setup fees and usage charges apply as listed.
Platform + API + Vertical SaaS + Services diversified, recurring-first model.
| Stream | MRR (RM) | ARR (RM) |
|---|---|---|
| Platform subscriptions | 100,880 | 1,210,560 |
| API add-ons | 21,200 | 254,400 |
| Usage overage (10%) | 10,088 | 121,056 |
| Dedicated deployments | 6,000 | 72,000 |
| Bank Recompiler (110 users) | 5,500 | 66,000 |
| Street Account (50 users) | 5,000 | 60,000 |
| Total Recurring | 148,668 | 1,784,016 |
| Category | Monthly (RM) | Annual (RM) |
|---|---|---|
| Monthly service income | 64,500 | 774,000 |
| Setup fees (one-time) | 294,000 | |
| Recurring revenue | 148,668 | 1,784,016 |
| Total Year-1 Revenue | 213,168 | 2,852,016 |
All figures are TARGET projections. Actual results depend on customer acquisition, pricing validation, and deployment execution.
500 NeuralOps customers in Year 1. Target
RM100/month · 1–2 users
MRR contribution: RM35,000
RM299/month · up to 5 users
MRR contribution: RM35,880
RM1,000/month · up to 20 users
MRR contribution: RM30,000
Total Platform MRR: RM100,880 Target
Total Platform ARR: RM1,210,560 Target
500 → 8,000 NeuralOps customers · RM88.4M 5-year cumulative combined revenue target. This is a management scenario, not guaranteed growth.
| Metric | Year 1 | Year 2 | Year 3 | Year 4 | Year 5 |
|---|---|---|---|---|---|
| NeuralOps Customers | 500 | 1,000 | 2,000 | 4,000 | 8,000 |
| Recurring Revenue (RM) | 1,784,016 | 3,568,032 | 7,136,064 | 14,272,128 | 28,544,256 |
| Setup fees (RM) | 294,000 | 588,000 | 1,176,000 | 2,352,000 | 4,704,000 |
| Services (RM) | 774,000 | 1,548,000 | 3,096,000 | 6,192,000 | 12,384,000 |
| Total Revenue (RM) | 2,852,016 | 5,704,032 | 11,408,064 | 22,816,128 | 45,632,256 |
Customer growth assumes 100% YoY increase. Vertical SaaS scales linearly with customers. Services and setup fees scale proportionally. Projection
Alternative customer-growth paths. These are directional scenarios, not forecasts of guaranteed outcomes.
| Scenario | Year-5 Customers | Year-5 ARR | Assumptions |
|---|---|---|---|
| Conservative | 2,000 | ~RM13.8M | Slower conversion; ~400 new customers/year |
| Base | 8,000 | ~RM55.3M | Management scenario, ~100% YoY growth |
| Upside | 15,000 | ~RM103.7M | High conversion + channel leverage |
VPS + GPU H200 RM4.98M over 5 years. Estimate
| Year | Customers | VPS Cost | GPU Cost | Total Infra | Cost/User |
|---|---|---|---|---|---|
| Year 1 | 500 | 50,000 | 264,000 | 314,000 | 628 |
| Year 2 | 1,000 | 100,000 | 264,000 | 364,000 | 364 |
| Year 3 | 2,000 | 200,000 | 528,000 | 728,000 | 364 |
| Year 4 | 4,000 | 400,000 | 792,000 | 1,192,000 | 298 |
| Year 5 | 8,000 | 800,000 | 1,584,000 | 2,384,000 | 298 |
| Total | 1,550,000 | 3,432,000 | 4,982,000 |
VPS: RM100/user/year. GPU H200 (4× H200 141GB) server lease: ~RM22,000/month. Each server serves ~1,500 customers for LLM inference. Initial GPU servers funded from RM500K infrastructure allocation. Estimate
Based on target pricing final unit economics require validated commercial data.
RM202
Monthly blended (Platform + API)
Estimate
RM8.33
Monthly infra cost per user
Estimate
To Validate
Inference, acceleration, peak load
Estimate
To Validate
ACV − VPS − GPU − support − direct
Estimate
| Metric | Value | Status |
|---|---|---|
| Blended ARPU (Platform + API) | RM202/month | Estimate |
| VPS cost per user | RM8.33/month | Estimate |
| GPU cost per user | To Be Validated | Estimate |
| Total infra cost per user | VPS + GPU + support | Estimate |
| Gross Margin | To Be Validated | Estimate |
| CAC | To Be Validated | Estimate |
| LTV | To Be Validated | Estimate |
Final unit economics will be calculated using confirmed pricing, VPS provisioning, GPU inference, support overhead, and retention data.
Revenue per customer − VPS − allocated GPU inference − support − payment fees − external-API fallback − storage/backup = contribution margin. Management will measure each line during the round; none of these are yet commercially proven.
Internal Retail Case Study verified historical data, clearly separated from external AI revenue.
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 monthly orders.
Important: 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.
Smart routing demonstrated a 87% token reduction vs naive full-LLM routing in internal benchmarks an internal operational result, not a universal industry claim.
Based on AINNA's internal operational workload. Results may vary across models, infrastructure and use cases.
Transparent separation of internal validation, external pilots, and external revenue. Nothing is overstated.
AINNA's NeuralOps platform runs real AINNA operating deployments (14 production systems) inside the company's own complex commerce operations. This is the strongest validation currently available.
Selected external pilots and proof-of-concept deployments are intended as the next commercial milestone. Confirmed paying external NeuralOps customers are not currently reported.
None confirmed at the date of this deck. External NeuralOps SaaS revenue is not claimed. The seed round is intended to convert internal validation into external commercial scale.
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.
Bottom-up market sizing based on addressable customer count × realistic annual ARPU. No GDP or national-economic-conribution shortcuts.
1.2M
MSMEs in Malaysia
120K
Digital-ready segment (10% of TAM) Estimate
8,000
Year-5 target Target
TAM = eligible MSME count × blended annual ARPU = 1,200,000 × (RM202 × 12) ≈ RM2.9B theoretical annual revenue TAM.
ARPU = RM202/month blended (management estimate across SME RM100 / Business 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. Source: SME 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. External 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.
A structural differentiation built on data sovereignty, pricing model and operator credibility.
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.
Built from years of actual commerce operations, not slide-deck use cases. The platform solves problems AINNA experienced first-hand.
Segmentation → Smart Routing → Specialised Parsers → Detached Systems → Private Infrastructure. Each layer reduces unnecessary cost and keeps execution predictable.
259 published scenarios built from operational problems, reusable across customers as deployment accelerators. An accumulating, defensible library.
Lessons from real workflows inform routing and parser design, reducing unnecessary frontier-model calls and lowering cost per outcome.
Local-first routing avoids unnecessary per-token costs while retaining an optional controlled external fallback where required.
Private/local deployment with governed control over data location, access, routing policy, model choice and auditability. Optional fallback does not weaken sovereign control.
The accumulated workflow library and operator-derived operational learning are the hardest parts to copy quickly. General-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 operations.
How the 500-customer Year-1 target is intended to be reached. Acquisition logic is a GTM assumption, not an achievement. Target
In-house sales team targeting Malaysian SMEs through events, referrals and digital outreach focused on retail, logistics and financial services.
Technology consultants, system integrators and industry associations as reseller and referral partners under a revenue-share model.
Self-service onboarding for the SME tier, free demos and proof-of-concept deployments that demonstrate value before conversion.
The channels below are illustrative acquisition assumptions, not verified conversion data. CAC / conversion will be measured during the round.
| Channel | Target Leads | Conversion | Expected Customers |
|---|---|---|---|
| Direct outbound | 1,500 | 8% | 120 |
| Channel partners | 1,000 | 10% | 100 |
| Associations / vertical | 800 | 10% | 80 |
| Pilot / POC conversion | 150 | 40% | 60 |
| Product-led / self-service | 3,000 | 3% | 90 |
| Strategic enterprise | 50 | 100% | 50 |
| Total Year-1 | 6,500 | — | 500 |
Figures are management GTM assumptions pending validation. Actual conversion depends on market response, pricing validation and execution.
Three-phase strategy from shared VPS to dedicated data center. Target
Year 1 · 500 customers · RM2M funding
Year 3 · 2,000 customers · Self-funded
Year 5 · 8,000 customers · Management target
Customer growth assumes a management scenario of ~100% YoY growth. This is a target scenario, not a guarantee.
RM2M for 10% RM18M pre-money, RM20M post-money.
ASK
RM2,000,000
10% equity
PRE-MONEY
RM18,000,000
Before this round
POST-MONEY
RM20,000,000
After this round
| Category | Allocation | RM Amount | Purpose |
|---|---|---|---|
| Engineering | 30% | RM600,000 | Product development, platform hardening, model optimisation |
| Infrastructure | 25% | RM500,000 | GPU servers, vLLM cluster, VPN backbone, VPS provisioning |
| Go-to-Market | 20% | RM400,000 | Sales team, marketing, channel development, pilots |
| Onboarding & Support | 10% | RM200,000 | Customer onboarding, documentation, support team |
| Compliance & Legal | 5% | RM100,000 | Data-protection alignment, legal structure, IP protection |
| Runway Reserve | 10% | RM200,000 | Operating runway buffer, contingency |
| Total | 100% | RM2,000,000 |
Detailed cap table & financial model available on investor request.
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.
RM15M+ cumulative lifetime retail sales of the operating company since 2019.
NeuralOps: segmentation, smart routing, specialised parsers, detached systems, private infrastructure.
259 published scenarios across 33 verticals; 14 production systems.
7-model local LLM orchestra and private-infrastructure deployment capability.
Operator-built platform validated inside AINNA's own complex commerce operations.
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.
Human operator-led team. Proprietary AI development infrastructure (Agent TC) is presented separately as a technology asset below.
Co-Founder & CEO
Drives vision, partnerships and business development. Multi-platform ecommerce operator managing 80K+ SKUs.
Degree in Business (Digital Business) — focus on Digital Transformation, E-commerce & Innovation Strategy (2023–Present)
Founded 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.
Co-Founder & Director of Customer Strategy
Strategic planning, operations and company growth. Oversees cross-border trade corridors Dumai–Melaka and Medan–Port Klang.
Business Management & Customer Strategy — Strategic Leadership & Sustainable Business (Professional Development)
Co-Founder & Technical Director
30 years IT & Engineering. Architect of the Detached System, 7-model LLM orchestra, and the broader scenario/workflow library.
Finance & Accounting
Bachelor of Education (Accountancy) with hands-on experience in administrative work, asset management, and teaching. Applies accounting knowledge to support AINNA's financial operations.
Bachelor of Education (Accountancy) with Honours — Sultan Idris Education University (UPSI), CGPA 3.5
Matriculation Program: Accounting — Melaka Matriculation College, CGPA 4.00 (Dean's List)
Co-Founder & Head of Logistics
Indonesia operations, vendor relations and logistics. Manages Dumai and Medan trade corridor operations.
Bachelor's Degree in Microelectronic Engineering — Universiti Malaysia Perlis (UniMAP), focus on Automation, Control Systems & Logistics Technology (2022–Present)
Co-Founder & Head of R&D
Malaysia operations, warehousing and logistics. Leads R&D on sensor-based systems — optical sensors, RFID triangulation.
Operations & Business Development
Connects live operations with business development across warehouse, restaurant and e-commerce units. Supports AINNA's commercial delivery and customer-facing operations.
Bachelor of Technopreneurship & Technology Management (In Progress) — focus on Technology Management, Innovation, Sales & Business Development.
Professional Certificate TRIZ · BTEC (Course Related), KESSUMA 2015 & 2016 — Innovation & Problem Solving.
E-Commerce Operations
Operates storefronts, orders and catalogue workflows across marketplaces. Built and merged 10 online stores into AINNA, generating RM1M in sales across ~25,000 SKUs.
Industrial Machining — technical qualification in industrial machining, foundation in precision engineering and technical problem-solving.
Proprietary AI Development Infrastructure · Internal Agent
Agent TC is AINNA's internal AI-assisted development and operational agent. 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.
Internal AI-assisted development and operations agent supporting engineering velocity. Specific model identity, context length and benchmark rankings are not claimed pending verifiable evidence.
Based in Melaka, Malaysia · Full team bios & references available in data room. Agent TC is an internal technology asset, distinct from the human founding team.
Transparent assessment of key risks and planned mitigations. Estimate
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.
Target prices (RM100/RM299/RM1,000) need market validation. Mitigation: phased rollout, early-adopter pricing, continuous feedback, willingness to adjust tiers.
VPS cost estimate may vary with provider pricing and utilisation. Mitigation: modular design, multi-provider strategy, infra cost monitoring as a KPI.
Hybrid routing uses external API fallback not zero API dependency. Mitigation: transparent disclosure, continuous optimisation to minimise external API calls.
Established AI providers and new entrants. Mitigation: Malaysian SME niche focus, data sovereignty advantage, operator-built credibility.
Data-protection and financial-services regulations. Architecture 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.
Year-1 targets are projections, not guarantees. Mitigation: diversified revenue streams and conservative growth assumptions.
Verified data, clear labels, transparent methodology.
Each page validates a layer of the investment thesis moat, technology, unit economics, and growth path.
Detached-system library across 33 industry verticals reusable workflow IP.
View evidence → Tech DDSmart routing across 7 local models local-first with controlled fallback.
View evidence → Unit econModelled unit-economics study; 87% is an internal benchmark with stated assumptions.
View evidence → Growth6-stage forward roadmap from operator base to broader platform ambition.
View evidence →Every major figure in this deck is labelled with its status:
RM2M seed · 10% equity · RM18M pre · RM20M post. Target: RM2.85M Year-1 revenue · RM88.4M 5-year combined revenue.
This document contains forward-looking projections and target estimates. Past performance of internal operations does not guarantee future results. All figures labelled with their status as applicable. See the risk section for a full discussion of uncertainties.
Investor Deck · Version 2026.08 · Updated 21 August 2026 · Financial model last updated 21 August 2026