DataAcuity — Full Internal Proposition

Confidential: Internal strategy, redistribution mechanics, pricing philosophy

Classification: Internal — contains % splits, entity detail, strategic framing Links: AppInfo/_shared/CONFIDENTIAL-internal-narrative.md — corporate structure Links: AppInfo/DataAcuity/DataAcuity_Data_Flow.md — technical pipeline Links: AppInfo/DataAcuity/DataAcuity_External_Pitch.md — public-safe version Memory: .claude-memory/banking-compliance-rules.md, .claude-memory/aether-global-compliance.md

The Core Thesis

DataAcuity is the monetisation layer on top of real, already-existing, consented data generated by 17 live production apps across South Africa.

It is not a data startup waiting to acquire users. The users are already here. The data is already flowing. DataAcuity is the pipeline that turns it into a business product — and routes the proceeds back to the people who generated it.

This is wealth transfer.

Not charity. Not corporate social responsibility. Structural, contractual, permanent redistribution: corporations who want to understand the poor end up paying the poor to be understood.

Every research report purchased. Every API call made. Every dashboard subscription paid. A portion flows back — via Qi, via community trusts, via social pledge — to the 17 apps' users who made that insight possible.

Why DataAcuity's Data Is Unlike Any Other

1. The invisible billion made visible

Most data providers have excellent coverage of the wired, banked, formally-employed world. They have almost nothing on the 4 billion people who live outside that world.

DataAcuity's data comes from exactly those people:

• Unbanked users opening SDPKT wallets
• People without formal employment searching The Job Center for learnerships
• Township residents tagging locations on TagMe — offline, via Aether mesh
• Content creators on SleptOn with no formal business registration
• Families on Takemehome.co.za navigating transport systems that Uber doesn't serve

This is not modelled, inferred, or extrapolated data. It is ground truth from the people themselves.

2. Consented at source — legally clean by design

Every data point in DataAcuity was generated by a user who:

1. Chose to consent (per-app, granular, revocable)
2. Was told what the data is used for
3. Was paid for consenting (Qi earned at point of consent)

In a GDPR, POPIA, NDPR, LGPD world, consented data is worth more than scraped data because it carries no regulatory risk for the buyer.

DataAcuity sells pre-cleared insights. The buyer does not need to worry about whether the underlying data is compliant — we carry that burden by design.

3. Multi-domain longitudinal life journey data

No other data provider can show you a single cohort's journey across:

• Financial behaviour (SDPKT wallet transactions)
• Employment seeking and outcomes (The Job Center)
• Social connection patterns (txtMe! metadata)
• Creative/economic output (SleptOn creator activity)
• Physical-world presence and movement (TagMe + Takemehome.co.za)
• Safety experiences (Panik incident metadata)
• Legal/identity engagement (TrustSeal)
• Consumption behaviour (KiffStore)
• Savings and investment trajectory (ShhMoney)
• Cross-app journey sequencing (Bruh! engagement patterns)

This is the full arc of economic life. Visible, consented, real.

4. Offline data via Aether by Circle mesh

TagMe and Panik (and partially SDPKT) work via Aether mesh when there is no internet. This means DataAcuity captures activity in:

• Areas with no mobile data coverage
• Times when a user's data has run out
• Rural and peri-urban zones invisible to any web-based analytics

The mesh is also a map of infrastructure failure: where Aether nodes are dense = where people go offline = where network investment is needed. Telecoms will pay for this insight.

5. Real-world social graph (not a web crawl)

TagMe's tags are created by humans who are physically present at a location. They are:

• Structured (category, sentiment, timestamp, location zone)
• Verified (linked to a TGN identity via TrustSeal/SDPKT)
• Compensated (user earns Qi for tagging)
• Offline-capable (mesh-submitted if no internet)

This is fundamentally different from web-crawled review data (which requires the location to have a web presence) or social media data (which is self-selected, biased toward the vocal and wired).

TagMe's data is what actually happens on the ground — not what businesses choose to advertise about themselves.

The Redistribution Mechanism

This is the architecture of wealth transfer. Every DataAcuity sale triggers this flow:

Business pays DataAcuity invoice (e.g., R50,000 for industry report)
        │
        ▼
1% gross revenue → Social pledge (R500 pre-committed before anything else)
        │
        ▼
Operating costs (server, analytics, team) deducted
        │
        ▼
5% net revenue → Qi Pool
(distributed to users whose consented events contributed to the report/API call)
        │
        ▼
Remaining net profit splits:
        ├── 65% → Founder (Bengu Group)
        └── 35% → Community arms:
                  ├── Bhengu Stichting (NL) → European member cashback
                  ├── Bengu Community Trust (SA) → South African member cashback
                  └── Bhengu San Zaidan (JP) → Japanese member cashback

In plain language

A FMCG company pays R50,000 for a report on township consumer spending.

• R500 immediately goes to a social pledge fund (schools, clinics, community projects)
• From the net revenue: Qi is distributed to every SDPKT and KiffStore user whose consented transaction events contributed to that report
• From the net profit: 35% goes into community trust accounts that distribute to ecosystem members annually

The FMCG company bought market research. In doing so, they inadvertently funded:

• Direct micropayments to township consumers (via Qi)
• Community trust distributions
• Social infrastructure

This is the playbook we do not publish. The businesses don't need to know the mechanism for it to work. They just need to know the data is good. The redistribution happens automatically via the corporate structure.

The Three Product Surfaces (Internal Detail)

Path B — Industry Reports

What it is: Periodic sector intelligence reports. Sold to industries, researchers, policy bodies. No single client dependency — we publish, they buy.

Format:

• PDF report + interactive data dashboard (where applicable)
• Published quarterly (major) or monthly (pulse reports)
• Example titles:
  • "South African Township Consumer Spend Report — Q1 2026"
  • "Labour Market Demand by Province: Learnerships vs Graduate Roles, 2026"
  • "Mobility Patterns in Sub-Saharan Africa: Where Transport Is Failing, 2026"
  • "Financial Inclusion Trajectory: From First Wallet to First Investment"
  • "The Aether Network Coverage Gap Report: Where South Africa Goes Offline"

Pricing philosophy (internal):

• Entry-level reports: R5,000–R15,000 (accessible to NGOs, universities, small businesses)
• Sector deep-dives: R25,000–R75,000 (mid-market corporate)
• Custom commissioned reports: R100,000+ (enterprise, government)
• No client-exclusive data — the same underlying dataset feeds all buyers (protecting user privacy by ensuring no single buyer can correlate across datasets)

Why B is the right start:

• No single client dependency = independent
• Scalable — one report sells to many buyers
• PR-ready — a published report establishes DataAcuity's credibility in the market

Path C — Embedded Pro (API)

What it is: Subscription API access to aggregated, anonymised signals from the TGN ecosystem. Enterprise clients embed DataAcuity data into their own products, dashboards, or models.

Endpoints (to be formally defined — see DataAcuity_Data_Flow.md):

• /cohorts/{segment} — demographic cohort signals
• /mobility/{zone} — movement patterns by location zone
• /spend/{category} — spend patterns by product category and economic tier
• /labour/{province} — job market demand signals
• /sentiment/{tag-category} — TagMe sentiment by category and geography
• /offline-zones — Aether mesh density map (infrastructure gap signals)

Pricing philosophy (internal):

• Starter: R2,500/month (limited calls, 3 endpoint categories)
• Growth: R8,000/month (higher volume, all endpoints)
• Enterprise: R25,000+/month (SLA, dedicated support, custom cohorts)

Who buys Pro:

• Fintech building alternative credit scoring
• Insurance companies modeling risk in underserved markets
• Retailers doing location intelligence for store placement
• Telcos identifying network investment opportunities
• Property developers tracking population movement

Path D — Wolverine Observability SaaS

What it is: The Wolverine self-healing event pipeline (WolverineAPI:5014) — which powers DataAcuity's own reliability — sold as a standalone SaaS product for engineering teams.

Why this is valuable:

• Built on real production load (17 apps, 36 APIs, millions of events)
• Self-healing: auto-detects anomalies, retries, routes around failures
• AI-powered: anomaly detection trained on TGN's own incident history
• Not another observability dashboard — it acts, not just alerts

Target buyers (different from B and C):

• Engineering teams at mid-market companies (50–500 developers)
• Who can't afford Datadog Enterprise but need more than basic logging
• In Africa and emerging markets (where infrastructure is unreliable = self-healing is essential)

Pricing philosophy (internal):

• Per-event ingestion + storage model (like Datadog/Splunk but 60% cheaper)
• Aligned to emerging market pricing realities

Why D is strategic: It gives DataAcuity a product that is completely independent of TGN user data. If regulatory changes restrict data monetisation, Wolverine SaaS continues. It also builds engineering credibility in the market — "they built the tools that run 17 apps."

Target Buyers and Their Specific Pain Points

1. FMCG companies (Unilever, Tiger Brands, Nestlé SA, Pioneer Foods)

Pain: They spend millions on formal market research (surveys, focus groups) to understand township and peri-urban consumers — but the research is slow, expensive, and often biased by the formal methodology.

DataAcuity provides:

• Real-time KiffStore + SDPKT spend patterns by product category, location zone, economic tier
• "First purchase after income" patterns (what people buy when they first get money)
• Brand vs unbranded preference signals (from KiffStore category data)
• Price sensitivity bands by Karma tier (economic trajectory)

The pitch:

"We know what 400,000 township consumers bought last Tuesday — categorised, anonymised, and compliant. Your quarterly focus group is already out of date."

2. Financial services (micro-lenders, insurers, fintech, alternative credit)

Pain: Inability to assess credit risk for the unbanked and under-banked. Traditional credit scores require formal employment + bank account + credit history. 35 million South Africans don't have this.

DataAcuity provides:

• SDPKT wallet behaviour patterns (transaction frequency, regularity, amounts — anonymised)
• ShhMoney savings trajectory (goal-setting behaviour, deposit consistency)
• Karma tier as economic trajectory signal (rising tier = improving financial behaviour)
• Job Center employment signals (applied, received, accepted — outcome metadata)
• Cross-app financial engagement (someone using SDPKT + ShhMoney + KiffStore together signals different behaviour than SDPKT-only)

The pitch:

"An alternative credit signal for the 35 million South Africans your models can't see. Built from real behaviour, not inferred from census data."

WARNING (internal): Do NOT sell raw financial data or individual-level signals. Sell only aggregated cohort behaviour. FICA + POPIA strictly enforced.

3. Property and real estate developers

Pain: Where are people moving? Which areas are gentrifying? Current sources: estate agent anecdote, Lightstone data (lagged), census (5-year delay).

DataAcuity provides:

• Takemehome mobility patterns (origin/destination pairs by zone, time-of-day)
• TagMe physical presence density (how many people tag a zone = footfall proxy)
• Job Center location demand (where people are searching for work)
• Aether mesh density change over time (growing mesh = growing population activity in zone)

The pitch:

"Real-time population movement data at neighbourhood level. We know where people are going before the estate agents do."

4. Transport and logistics (Uber, Bolt, taxi associations, bus operators)

Pain: Demand forecasting in areas where they have no historical data. How do you plan a route where you've never operated?

DataAcuity provides:

• Takemehome ride demand patterns (where, when, how far, price sensitivity)
• Aether offline zone map (where data is unavailable = where Uber/Bolt can't operate either = where Takemehome serves)
• TagMe route tagging (people tag the paths they use — informal routes, taxi ranks, etc.)

The pitch:

"We have demand data for routes you've never covered — from the people already traveling them. And we know exactly where your app stops working — because our mesh keeps going."

5. Government and NGOs / Development Finance Institutions

Pain: Evidence-based policy requires ground truth from communities. Most government data is self-reported, lagged, or from formal institutions that don't reach the most marginalised.

DataAcuity provides:

• Panik incident patterns (safety hotspots by zone, time of day — anonymised, aggregated)
• Job Center skills gap analysis (what roles are sought vs available by province)
• TrustSeal verification demand (where document verification infrastructure is failing)
• SleptOn informal economy signals (where the informal creative/knowledge economy is active)
• Aether mesh coverage = map of where internet infrastructure is failing

The pitch:

"Real-world evidence from the communities your policy is meant to serve — not from the institutions that were supposed to serve them."

Special consideration: DFIs (IFC, DEG, Norfund, etc.) may co-invest in DataAcuity as a development-aligned data infrastructure play. The redistribution mechanism (35% community + 5% Qi + 1% social pledge) is a compelling development finance thesis.

6. Telecommunications companies (MTN, Vodacom, Cell C, Telkom)

Pain: Where to invest in infrastructure? Where is the network failing?

DataAcuity provides:

• Aether mesh density map = inverse of network coverage (dense mesh = people going offline)
• TagMe offline event concentration (where people are active with no internet)
• SDPKT failed transaction geography (where payments fail = where network fails)
• txtMe! Aether vs IP routing ratio by zone (high Aether use = poor network zone)

The pitch:

"A real-time map of where your network fails — built from the people experiencing it. Not from your tower telemetry. From your actual customers."

7. Research institutions, universities, think tanks

Pain: Longitudinal datasets on economic mobility in Africa are rare, small, or outdated.

DataAcuity provides:

• Multi-year cross-app journey data: first wallet → first job → first savings → first investment
• Economic mobility trajectory by cohort (Karma tier progression over time)
• Language + locale dynamics in economic participation
• Gender × economic activity signals (opt-in demographic metadata)

The pitch:

"The most comprehensive longitudinal dataset on economic mobility in Sub-Saharan Africa. Consented. Compensated. Continuously updated."

Pricing: Academic licensing at 20% of commercial rate. Why: academic papers citing DataAcuity = credibility + distribution in the market that makes enterprise sales easier.

The Ethical Firewall

DataAcuity will be attractive to buyers who want to exploit the data differently. This section defines what we will NOT sell — ever.

The firewall is the product. Buyers pay more for DataAcuity precisely because it is pre-cleared, compliant, and comes with a documented ethical framework. Buyers who want dirty data have other places to go. We are not one of them.

The Wealth Transfer in Numbers (Illustrative)

Assume DataAcuity reaches R5M annual revenue in Year 3 (conservative).

This is not a rounding error. This is the largest structured data-derived community redistribution programme in South African history — and it grows automatically with revenue.

Internal Summary: Why DataAcuity Matters to the Mission

1. It funds the Qi Pool — which pays users for their data in real time
2. It funds the community trusts — which distribute annually to members
3. It funds the social pledge — which pre-commits to community infrastructure
4. It generates the revenue that sustains the 17 apps (cross-subsidy)
5. It makes the ecosystem sustainable WITHOUT advertising — users are not the product for advertisers; they are the producers of a data product they own and get paid for

The difference between DataAcuity and surveillance capitalism:

• Surveillance capitalism: take data → sell it → profit → nothing back to users
• DataAcuity: take consented data → sell insights → profit → structured portion back to users

"Yes, we sell your data. But we protect your identity first. And we pay you for it." That is the honest, public-safe version of what DataAcuity does. The full version — the wealth transfer — is what makes it historic.