Results as a Service
Before you build, know what's worth building.
This is the demand map for the entire Mycelium. It answers a business owner's first hard question: which parts of my data footprint should become leverage?
Three registers cover every job a venture could need — from auth to tokenomics, from legal tech to construction payments, from hot wallets to multisig. Features that score high enough earn a PRD and become shared capabilities. Everything else gets partnered or skipped.
Use this page from back to front, then forward again:
- Map the operator's data footprint — invoices, jobs, contracts, conversations, assets, sensors, compliance records.
- Read the horizontal register to see which universal capabilities that footprint implies.
- Read the vertical register to choose the market position where those capabilities matter most.
- Read Platform Instruments to see what is already specified, built, tested, or commissioned.
- Pull Business Levers to steer narrative, pricing, product, distribution, and capital.
- Prove the first workflow as an AI-Native Edge Twin before claiming transformation.
- Use AI Organisations to decide which primitives to build first.
The loop is not "build a super-app." The loop is data → prediction → instrument → lever → action → new data.
The Three Registers
| Register | What It Covers | Features | Key Question |
|---|---|---|---|
| Horizontal SaaS | 177 features across 24 categories — auth, billing, data, AI, voice, blockchain, IoT, geospatial, booking, field ops, UI | 177 | Where do we have unfair advantage? |
| Vertical SaaS | Industry opportunities scored by lucrativity, effort, and market size — PropTech expanded to 6 sub-verticals | 6 verticals | Which vertical do we enter first? |
| On-Chain | Wallet safety, DePIN device registration, property tokenization, carbon credits, attestation | All chains | How do we make the next action safe and obvious? |
| AI Organisations | Edge twins, agent passports, workflow learning loops, tacit knowledge capture | All operators | How does the organisation learn faster than it can be copied? |
Scoring Methodology
Every horizontal feature is scored on four dimensions (1-10 each):
| Dimension | What It Measures |
|---|---|
| Current | How much capability exists today |
| Fit | How well it fits the platform architecture |
| Value | How much customers care |
| Edge | How differentiated we are vs alternatives |
Priority = (Current + Fit + Value + Edge) / 4 x Edge
Edge is the multiplier. High-value commodities (payments, auth) score high on Value but low on Edge — partner for these. High-edge capabilities (multi-agent orchestration, smart contracts) compound — own these.
Top 10 by ROI
The highest-scoring features across the entire superset, ordered by priority score:
| Rank | Category | JTBD | Priority | Status | Mycelium PRD |
|---|---|---|---|---|---|
| 1 | AI Workflow | Multi-Agent Orchestration | 94.5 | CORE | Workflow Engine |
| 2 | Blockchain | Smart Contracts | 76 | CORE | Sui Wallet Safety |
| 3 | Blockchain | Tokenomics | 76 | ACTIVE | Sui Wallet Safety |
| 4 | Collaboration | Knowledge Mgmt | 76 | STRONG | Agent Platform |
| 5 | AI Workflow | Process Automation | 67.5 | OPPORTUNITY | Intelligence Functions |
| 6 | AI Workflow | Document Intelligence | 67 | STRONG | Intelligence Functions |
| 7 | Developer Tools | CI/CD Automation | 67 | STRONG | — |
| 8 | Emerging | API Development | 67 | STRONG | Data Interface |
| 9 | Security | Audit Logging | 60 | STRONG | Commissioning |
| 10 | Marketing | Customer Support | 60 | STRONG | Intelligence Functions |
9 of the top 10 already have a Mycelium PRD. The one that doesn't — CI/CD Automation — is developer tooling that compounds through the engineering repo, not here.
Data Footprint as Actuation
The registers above answer "what software do I need?" That is the wrong question to ask first.
Every operator already generates a footprint — invoices, jobs, fleet, sensors, signatures, comms. Most feel it as a tax that pays no rent. AI agents now read structured data faster than any human consumer ever did, and the cost of firing one fact across many consumers has collapsed. The window for a small operator's footprint to serve as many consumers as a large operator's is open — and it closes the day the aggregator becomes the moat.
| Face | Verb | Governs | Pattern |
|---|---|---|---|
| Arbitrate | Decide | Consent, retention, surfacing, who-may-see | The operator authorises what the data permits |
| Arbitrage | Distribute | One data event triggers N actions for N consumers | Insurer, lender, agent, audit, partner — each pays when a trigger fires |
The right first question is "what is the footprint permitted to do, and where else does the same fact pay?" The horizontal and vertical registers above answer the second half. The on-chain register — attestation, tokenisation, DePIN device registration — makes one fact fire many consequences cheaply and verifiably.
The full play lives at Data Footprint — governance (arbitrate) on one side, distribution (arbitrage) on the other.
When the footprint is mapped, do not boil the ocean. Choose one workflow and run it as an AI-Native Edge Twin: fork the data, govern the agents, run in parallel, and only move the core process after the instruments show proof.
The build sequence is captured in AI Organisations: footprint, passport, runtime, instrumentation, learning, tacit capture, scale.
Critical Gaps
Features that score high on Value but have no Mycelium PRD yet:
| Gap | Value Score | Blocks | Next Step |
|---|---|---|---|
| Embedded Payments | 10 | All verticals — 20-40% revenue uplift | Write PRD (P0) |
| Identity/Auth | 10 | Every venture needs auth | Evaluate: build or partner |
| IoT Device Telemetry | 9 | PropTech (Tector, Polar Night, Birdsview) | DePIN Infrastructure planned |
| Resource Booking Engine | 9 | PropTech workspace (Spacent), field services | Gap — no PRD |
| Real-Time (WebSockets) | High | Live dashboards, agent comms | Covered partially by Agent Platform |
| Compliance Framework | High | Healthcare, financial, PropTech sustainability | Extend Commissioning |
| Geospatial Analytics | 8 | Smart cities (Green Urban Sights), property portfolio | Gap — no PRD |
Resource Allocation
| Tier | Effort | Rule | Examples |
|---|---|---|---|
| Core (own it) | 80% | Build capabilities where Edge > 7 | Multi-agent orchestration, smart contracts, knowledge mgmt |
| Strategic (invest selectively) | 15% | Build when it unlocks a specific vertical | Compliance, APIs, DeFi, decentralized ID |
| Opportunistic (partner or skip) | 5% | Build only when a customer pays upfront | Communication, shipping, native mobile |
The Loop
DATA FOOTPRINT (what the operator already generates)
→ SUPERSETS (score demand and edge)
→ MYCELIUM PRDs (spec highest-ROI capabilities)
→ MUSHROOM CAPS (prove in one venture)
→ COMMISSION (verify against spec)
→ SCALE (promote to platform primitive)
The operator's data footprint is the raw material. The supersets predict where that footprint can become leverage. The Mycelium PRDs turn prediction into buildable capability. The mushroom caps are the proving ground. Nothing scales until it's commissioned.
Front to Back
The front-end story must point back to evidence. The back-end catalog must point forward to action.
- Business Levers decide what to pull next. The next click is Platform Instruments.
- Platform Instruments read capability maturity. The next click is Horizontal RaaS.
- Horizontal RaaS names the universal jobs. The next click is Vertical RaaS.
- Vertical RaaS chooses the market position. The next click is Data Footprint.
- Data Footprint governs and arbitrages the data asset. The next click is AI-Native Edge Twin.
- AI-Native Edge Twin proves one workflow safely. The next click returns to Business Levers.
- AI Organisations names the primitives to build first when the loop is ready to become product.
External References
- JTBD Superset Workbook — Live scoring spreadsheet
- SaaS Stack Map — Visual architecture
Context
- Phygital Mycelium — Where high-scoring features become PRDs
- Mushroom Caps — The ventures that compose from these capabilities
- Everything App — The wallet-to-platform trajectory
- AI Organisations — The build sequence for edge twins and agentic workflow primitives
- Jobs to Be Done — Demand validation framework
- Standards — Where proven patterns graduate to
Questions
Which RaaS function category — data management, workflow automation, or AI decision support — creates the most leverage when delivered as a service rather than built custom per deployment?
- At what function count does a RaaS catalog become comprehensive enough to assemble most business applications from pre-built components?
- How does the RaaS model change the build-versus-buy decision for a startup — and which functions should always be built custom regardless of what's available?
- Which RaaS function is most likely to be commoditized by foundation model capabilities in the next 18 months?