Website text export · 6 September 2026 Cleopas : A$15M Use of Funds A$15M Use of Funds · Working capital plan Money in. Product strength out. Cleopas is not raising A$15 million to build a chatbot. The capital buys research depth, independent challenge, engineering speed and thousands of hours of paid real-user learning. One cheque should be enough to build quickly, learn honestly and preserve independence without making the company depend on another fundraising round during its most important years. Read the business planSee the numbers 01 · Why this page exists The capital plan should explain the business in dollars. Cleopas is building a new way to explore life's biggest questions about faith and existence. It combines deeply governed research with AI so a person can privately explore a much wider set of evidence, arguments and perspectives than any single human could reasonably hold in their head, through a conversation that moves at their pace. The A$15 million ask is deliberately easy to scrutinise. If the cheque does not make the research better, the product safer, the team faster or the user learning deeper, it should not be in the budget. The working assumption is a single capitalisation event. The goal is therefore not to minimise the cheque. The goal is to fund the mission well enough that Cleopas does not have to slow down, narrow the research programme or depend on another fundraising round while the product is still proving itself. 02 · The capital logic A$9.05M to build and learn fast. A$5.95M to protect the mission. A$9.05M Indicative first 24 months Engineering, research, scholars, paid users, compute, governance, distribution and operating contingency. A$5.95M Protected strategic reserve Years three to five, sovereignty, specialist escalation, additional cohorts and downside protection. Important: A$15 million raised does not mean A$15 million immediately spent. The reserve is not a target. Capital should be released against evidence, milestones and real user learning. 03 · What the first 24 months buy Front-load the expensive learning. The first two years intentionally spend more than the old operating plan because speed and evidence quality are strategic advantages. Research and engineering run in parallel while real users enter the product early. Engineering + productA$3.05M Internal research operationsA$1.55M Paid alpha + beta user programmeA$1.50M External scholars + specialist reviewA$0.80M Compute, model experimentation + securityA$0.65M Operations + contingencyA$0.65M Distribution, partnerships + brandA$0.50M Legal, governance, IP + privacyA$0.35M Workstream24 monthsWhat it should create Engineering + productA$3.05M4 to 6 engineers early, governed product, evidence graph, retrieval, evaluations, research tooling, security foundations Internal research operationsA$1.55MResearch team, claim decomposition, source mapping, tagging, provenance, quality control and external-review preparation External scholars + specialist reviewA$0.80M150 to 250 independent review assignments across the first 50 major questions, plus premium specialist reserve Paid alpha + beta usersA$1.50MStaged cohorts building toward about 1,000 intensive participants and roughly 10,000 hours of real product use Compute + securityA$0.65MInference, embeddings, model bake-offs, evaluation runs, observability, secure environments and sovereign-hosting experiments Legal + governance + IPA$0.35MLicensing, participant consent, scholar agreements, IP assignment, privacy, entity and governance work Distribution + partnerships + brandA$0.50MControlled partner cohorts, launch preparation and measured channel testing Operations + contingencyA$0.65MOperating overhead and room for a research-heavy international AI project to encounter the unexpected TotalA$9.05MFront-loaded build and learning programme 04 · Engineering More engineers early because time to market matters. Cleopas should not spend a year producing research while a tiny engineering team slowly builds around it. The plan assumes 4 to 6 engineers early, with product and design support, so the Evidence Landscape, conversational system, evaluations and research tooling can develop at the same time. MonthsPrivate alpha A narrow governed product should be in real hands quickly. 3 to 6Closed beta Users start changing the product and research roadmap well before year one is over. 4 to 6Engineers early Evidence system, product, evals, data tooling, security and infrastructure can move in parallel. 05 · Research The research team is one of the company's two production engines. Internal researchers do the repeatable work: assemble sources, split questions into claims, map evidence families, tag provenance, identify competing explanations and prepare material for external attack. Expensive scholars should spend their time challenging difficult judgments, not doing work the internal team can do. The long-term scaling test is not how many topics Cleopas can list. It is whether human research hours per governed topic fall while traceability, external defensibility and overclaim performance stay stable or improve. 06 · External challenge Fifty major questions. Hundreds of independent review assignments. The first 50 major questions should be unusually expensive and unusually well challenged. A simple planning assumption is US$1,000 per review, with 3 to 5 independent reviews per question. That creates 150 to 250 review assignments before premium specialists are added. 50major questions Highly governed early topic set. 3 to 5reviews each No single scholar becomes the authority. 150 to 250review assignments Independent attack before product confidence. ~70 to 100scholars over time Different disciplines, countries and worldviews. The A$0.80M budget is deliberately larger than the simple per-review calculation. Some globally recognised specialists may cost materially more. Some questions will require several disciplines. Others will need methodological or scientific review rather than historical review. The budget buys the ability to use the right person rather than the cheapest person. 07 · Purpose-blind validation Confidentiality and anti-contamination can be the same design choice. One of the strongest features of the research model is that external work can be siloed. A scholar can receive a claim, source set and review task without being told the wider product thesis, the desired conclusion or what another reviewer has already said. Scholar research Reviewers work independently. They can disagree. Their disagreement is preserved. They do not need to know the intended product or be exposed to the conclusions of the other reviewers. User research Participants receive proper consent information about the product test, compensation, recording and data use, but they do not need to be primed with the product's preferred hypotheses or the conclusions of earlier cohorts before giving their own reactions. Why it matters: the strongest answer to a cherry-picking accusation is not a better argument. It is a process in which many reviewers were independently selected across different countries, disciplines and worldviews, then asked to attack narrow tasks without being coached toward a shared conclusion. This is purpose-blind independent research and product validation, not a formal clinical trial. Any withholding of hypothesis or product context should be designed with appropriate consent, privacy and research-governance advice. 08 · Paid user research Pay 1,000 people enough to stay long enough to find the flaws. The first 1,000 beta participants are not vanity registrations. The working protocol asks for roughly 8 to 10 hours of use over one to two weeks, across several question families, followed by structured feedback and interview work. 1,000paid participants 8 to 10huse per person ~10,000hreal product use ~US$1kworking honorarium We want to know what made them trust Cleopas, what made them distrust it, where it felt biased, where it became too technical, where it overclaimed, what changed their thinking and what made them want to leave. That feedback then becomes product changes, research priorities, evaluation cases and conversation-training material. Cohorts should be staged. A practical sequence could be 50, 150, 300 and 500. Each cohort should receive a materially better version than the cohort before it. 09 · The learning loop The money creates a compounding system, not a static library. Research team builds the claim landscape Independent scholars attack it Engineering makes it conversational Paid users expose failures and demand The next research queue gets better This loop is the real capital efficiency story. Research creates better software. Software creates better user conversations. User conversations reveal which research matters. Over time AI should make the research operation faster while human governance remains responsible for what becomes trusted knowledge. 10 · The protected A$5.95M Raise it once. Spend it only when the evidence says to. The remaining A$5.95 million is deliberately not tied to permanent first-24-month headcount. It protects years three to five and creates the option to move faster when evidence justifies it. Possible uses Slower supporter revenue, premium specialists, additional beta cohorts, sovereignty infrastructure, security events, high-demand research branches or proven distribution channels. Governance Milestone release, board oversight, budget variance reporting and explicit gates before reserve capital becomes operating spend. 11 · What A$15M should visibly create The cheque should be inspectable in the product and the IP. 50deeply governed headline questions Each decomposed into the many smaller propositions underneath it. 150 to 250+external review assignments With premium experts where the question deserves them. ~1,000intensive paid users Approximately 10,000 hours of real use plus qualitative research. 1repeatable Evidence Landscape system Research, governance, evaluation and AI tooling that can expand beyond the first topic set. The best use-of-funds page is one where an investor can point to every major line item and see exactly how it makes Cleopas harder to fool, harder to bias and faster to improve. 12 · Downside protection If the consumer product fails, the cheque should still leave something unusually valuable behind. The capital does not primarily disappear into ephemeral model inference. It creates structured research, atomic claim maps, provenance, source relationships, scholar disagreement records, review protocols, evaluation sets, behavioural training material and a repeatable governance method. That does not mean the research has a guaranteed A$15 million resale value. It does mean a mission-led funder can be left with a substantial reusable intellectual asset rather than a failed interface and a pile of model invoices. For a church network, seminary, educational organisation, publisher or other Christian institution, a deeply structured body of independently challenged research could be reused across education, training, digital products, apologetics critique, curriculum, pastoral resources and future AI systems. Much of the research methodology is also portable to other fields where evidence is contested. 13 · One sentence A$15 million is being raised to make the evidence and the learning unusually hard to attack. The capital thesis A$15 million is not being raised to build a chatbot. It is being raised to buy the research depth, independent challenge, engineering speed and real-user learning required to make a new kind of evidence-governed conversation trustworthy enough for life's biggest questions. Downloads Take the capital plan with you. Word documentPlain text CLEOPAS.AI · Life's deepest questions. Evidence, context and room to question.