Decidr Ai Industries Ltd · ASX:DAI
Decidr Ai Industries — Investor Presentation (September 2026)
43 slides from Decidr Ai Industries's investor presentation, Sep 18, 2026
Slide 1: Built the platform. Now running the business. FY26 in Review and Outlook
rsonal use only Buil the plat orm. Now running the business. FY in Review and Outlook Decidr AI Industries · ASX: DAI · September 2026 decidrindustries.ai 2 6 t f
Slide 2: Important notice
Decidr Ai Industries 2 Impor ant notice The forward-looking statements included in these materials involve No representation, warranty or assurance (express or implied) is given or rsonal use only subjective judgement and analysis and are subject to significant made in relation to any forward-lookin
Slide 3: What Decidr is
What Decidr is 3 An ASX-listed company building an operating system that runs business workflows on AI, under the customer's own rules. rsonal use only What we do Vision Key facts Every organisation will run on Listing ASX: DAI AI. Decidr’s aim is to be the DecidrOS captures how a business works and
Slide 4: FY26 Year in review
FY · Year in review 4 Annualised exit-rate revenue (AER) of A$13.6M at 30 June 2026 rsonal use only A$13.6M A$13.6m AER · A$M Annualised exit-rate revenue at 30 june 2026 A$8.06m A$4m 7.5× the prior corresponding quar er and A$2.5m 1.7× March 2026. A$1.8m Six consecutive quar ers of growth. Jun 25 S
Slide 5: FY financial and operating summary
FY at a glance 5 FY financial and operating summary Preliminary and unaudited, from the Appendix 4E lodged 31 August 2026. rsonal use only A$4.73M Statutory revenue 12.1 × Par ner and direct revenue growth A$23.4M Cash at 30 june 2026 A$3.19M in FY vs A$0.26M in FY Up 99% on FY (A$2.4M) From A$7.8M
Slide 6: Cash and financial discipline
Cash and financial discipline 6 Cash position and cost program Cash A$23.4M at 30 June 2026. A$5M annualised cost reduction program initiated in Q FY . rsonal use only FY cash movement · A$m · preliminary 4E cash flow +37.8 23.4 7.8 −19.5 −2.6 A$5M Annualised cost reductions targeted Jun 2025 Net fi
Slide 7: Market context
rsonal use only 01 Market context
Slide 8: The market in FY26: Enterprise AI adoption
The market in FY 8 Enterprise AI adoption in FY Adoption is broad; operational deployment at scale remains limited. The constraint is knowledge of how work is done, held by people rather than systems. rsonal use only 88% 2% 64% of businesses were using AI in of organisations have operationalised AI
Slide 9: Where DAI fits: Decidr compared with AI assistants
Where DAI fits 9 Decidr compared with AI assistants Assistants suppor an individual. Decidr runs business workflows end to end. rsonal use only Ai assistants Sovereign operational ai One chat, one answer Every team, end to end Knows the internet, not your business Buil on the business’s own knowledg
Slide 10: How the platform works: capture, coordinate, compound
How it works 10 How the plat orm works: capture, coordinate, compound Each step, with a customer resul for the two steps in commercial use. rsonal use only 01 Capture 02 Coordinate 03 Compound Sugarwork and Rumi record how people DecidrOS breaks workflows into small, The ontology and Agentic Graph l
Slide 11: Business & financial performance
rsonal use only 02 Business & financial per ormance f
Slide 12: Revenue channels and stage of development
The strategy 12 Revenue channels and stage of development Share of AER at 30 June 2026 by channel. rsonal use only 01 Seeding 02 Commercialising 03 Emerging 04 Growth DecidrOS (BetaOS) Embedded par ners Commercial resellers Sugarwork + Rumi Early adopters and foundation Sof ware companies that sell
Slide 13: Every deal adds a node. Some deals add a network
Go-to-market 13 Every deal adds a node. Some deals add a network Par ners attach thousands of businesses at once. Enterprises attach one at a time. Foundation customers prove the schema that both rely on. rsonal use only Foundation customers Decidr Enterprise customers Sugarwork Embedded par ners Su
Slide 14: Customer deployment timeline: an aged care provider
Customer case study 14 Customer deployment timeline: an aged care provider Sugarwork Pilot to a two-year agreement in six months. An Australian aged care and retirement living provider; customer not named. rsonal use only Jan 2026 30 Mar 2026 Apr to Jun 2026 Within 12 weeks 30 Jun 2026 Sep 2026 Firs
Slide 15: Distribution channels
Distribution 15 Distribution channels AWS, Google Cloud, sovereign compute in Australia, and the US commercial base. rsonal use only Decidr Decidr Sugarwork Sugarwork Sovereign AI US landing and Amazon Google Cloud and Graph network Web Services sovereign compute Oct 2025 Decidr U.S. Inc established
Slide 16: Product delivery and roadmap
Technology progress 16 Product delivery, Q FY Released in the June 2026 release cycle, and the current roadmap. rsonal use only ~180 13 4 BYOK updates in the June 2026 new integrations added messaging sur aces via run on your own model release cycle Agent Relay and infrastructure Shipped in FY Roadm
Slide 17: How the Decidr platform is built
The plat orm 17 How the Decidr plat orm is buil Seven layers. Each one is a capability the layer above relies on. The three highlighted layers are the ones currently in focused production. rsonal use only Runs On Decidr, Sugarwork and Rumi. Screens are views over the same model, Products Sold four w
Slide 18: Harnesses vs Schema: what they are and why they matter
The plat orm 18 Harnesses vs Schema: what they are and why they matter The model is rarely what decides whether AI does real work. The sof ware around it is. rsonal use only AI model Harness Working AI Answers a question. Remembers The sof ware around the model. Work that gets finished, can be nothi
Slide 19: Ahead of the harnesses
The plat orm 19 Ahead of the harnesses What Decidr already has, what the planned agent layer adds, and how that stacks against the five coding-agent harnesses assessed: Claude Code, OpenCode, DeepSeek, OpenChamber and Oto-Dock. The harness ecosystem is converging on ideas Decidr made structural deci
Slide 20: How we get there is the whole question
The problem 20 How we get there is the whole question rsonal use only Knowledge work today
Slide 21: Work runs down a production line, not through a meeting room
The breakthrough 21 Work runs down a production line, not through a meeting room The production line is deterministic. Build the rails once, then route each task to the right executor rsonal use only
Slide 22: Decidr turns knowledge into structured workflows
The breakthrough 22 Decidr turns knowledge into structured workflows that can be executed, improved and reused rsonal use only Via Decidr, work is harnessed in manufacturing-like, deterministic production lines that can be reused, are auditable, quality controlled and inputs can be tested and swappe
Slide 23: The same graph of work customized across industries
The breakthrough 23 The same graph of work customized for similar functions across industries The vocabulary changes between industries. The structure doesn’t. Task standards buil once hold everywhere rsonal use only t
Slide 24: Graphs make markets
The breakthrough 24 Graphs make markets Search, social and payments each became a market when someone named the objects. Work has no such graph yet. rsonal use only 1,2,3,4 : eMarketer, Statista, McKinsey Global Payments Report, MIT Project Iceberg
Slide 25: Outlook
rsonal use only 03 Outlook
Slide 26: Commitments and outcomes
Accountability 26 Commitments and outcomes, FY to CY Commitments made in ASX announcements and webinars, their due dates and status. Open items run to CY . rsonal use only Category Commitments Benefit Go1, ELMO and eBev to contribute revenue; Conver commercial traction into recurring revenue Commerc
Slide 27: Update since June 2026
What has changed 27 Update since 30 June 2026 rsonal use only September quar er September quar er Product dates Q Cash Receipts up vs Q Cost base reducing DecidrOS sel -service to include business and consumer organisations The quar erly trajectory is consistent with the The Group has under aken a d
Slide 28: Outlook for the December and March quarters
Outlook 28 Outlook for the December and March quar ers Expected milestones and dates. Dates are calendar quar ers; working dates are marked. rsonal use only 01 02 03 Sugarwork and par ners Decidr and the Agentic Graph Cash and people • Sugarwork revenue will continue to grow at • Foundation customer
Slide 29: Questions
rsonal use only Questions Decidr AI Industries Limited · ASX: DAI decidrindustries.ai
Slide 30: Appendix. Customer case studies
rsonal use only Appendix. Customer case studies Mercha, Scentia, a subsea cable company, Appen, Lcubed, Supergoop.
Slide 31: Customer case study: Mercha, proposal generation
Success story 31 Customer case study: Mercha, proposal generation Decidr buil a proposal-generation system for Mercha, Australia’s leading promotional merchandise plat orm. rsonal use only Before Decidr, proposals required hours Today, Decidr automatically qualifies leads, assembles of manual work a
Slide 32: Mercha proposal workflow: task time before and after Decidr
Mercha proposal workflow 32 Mercha proposal workflow: task time before and af er Decidr rsonal use only Data prep Analysis Execution Task Read Research Find Identify Select Calc Calc Generate Draf Assemble Send Track Time Required (Human Sales Mgr) 5m 20m 10m 10m 20m 20m 15m 25m 15m 10m 5m 5m Time R
Slide 33: Mercha proposal workflow: product screens
Mercha proposal workflow 33 Mercha proposal workflow: product screens rsonal use only Leads are enriched and Pricing, mock-ups, and proposal Teams review, adjust, and send qualified automatically. assets are generated and assembled. proposals in minutes.
Slide 34: Mercha: outcomes beyond the initial workflow
Success story 34 Mercha: outcomes beyond the initial workflow The Proposal Generator gave Mercha something even more valuable than speed: structure. Memory, context, and repeatable workflows were wired into the business for the first time and can extend across the rest of the company. rsonal use onl
Slide 35: Mercha: customer statement
Q 35 Mercha: customer statement Mercha didn’t wait for the per ect plan. They chose their highest value star ing point, the proposal generator. This operating layer can now extend across other decision-heavy processes without impacting headcount. rsonal use only “I you believe AI is going to drastic
Slide 36: Customer case study: Scentia, AI learning tools
Success story 36 Customer case study: Scentia, AI learning tools Decidr buil three AI learning tools inside the learning plat orm of Scentia, an Australian education and training group. rsonal use only Scentia delivers career-focused learning across 200+ future skills courses and a por folio of Toda
Slide 37: Scentia: what changes for students and staff
Success story 37 Scentia: what changes for students and staf The same operating layer changes how students ask, how staf spend their time, and how courses are produced. rsonal use only Students ask more, and earlier Academic staf move to higher-value work Public Q&A and passive waiting for marks bec
Slide 38: Scentia: customer statement
Success story 38 Scentia: customer statement Scentia wanted AI inside the learning journey rather than bol ed on beside it, across mul iple education brands and a single learning plat orm. rsonal use only “We wanted a par ner we could build with WHY SCENTIA PARTNERED WITH DECIDR for the long term. D
Slide 39: Subsea cable company: $3.8M of duplicated effort identified
CASE STUDY Ef iciency 39 SUBSEA CABLE COMPANY SUBSEA FIBRE OPTIC CABLE SYSTEMS Subsea cable company: $3.8M of duplicated ef or identified rsonal use only ~900 employees, 8 cable ships, contracts The challenge of $300M–$1.5B. Af er a restructure, role 39 interviews across 26 people in three overlappi
Slide 40: Appen: knowledge transfer during a restructure
CASE STUDY Continuity 40 APPEN TECHNOLOGY · ASX:APX · 1,000+ EMPLOYEES Appen: knowledge transfer during a restructure rsonal use only Appen restructured, moving engineering The chal len ge functions across geographies. Over 100 Exper –learner pairs ran 250+ structured sessions over 6 weeks, captured
Slide 41: Lcubed: succession planning for a state agency
CASE STUDY Assurance 41 LCUBED CONSULTING · STATE GOVERNMENT AGENCY Lcubed: succession planning for a state agency rsonal use only A state agency faced a retirement clif : nearly The chal len ge 100 senior leaders eligible to exit within 18–24 100% of at-risk staf interviewed at depth, sur acing pro
Slide 42: Supergoop: commercialisation process documented
CASE STUDY Assurance 42 SUPERGOOP CONSUMER PACKAGED GOODS · SKINCARE Supergoop: commercialisation process documented rsonal use only A fast-growing CPG company outran its own The chal len ge documentation. Teams tracked their own tasks Seven stakeholder interviews over 2.5 weeks became RACI char s,
Slide 43: MD File for AI Review
FOR AI ANALYSIS 43 MD File for AI Review The below MD file is intended to provide clear instructions for AI systems to interpret the data contained in this presentation. This is a ‘presentation harness’ that is intended to reduce misinterpretation by AI, reduce hallucinations and ensure that informa
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