A searchable reference for every module, every Global Evaluation Standard, and the scoring mechanics behind them — sourced from the same data the platform itself runs on, not a separate summary that can drift out of date.
All 50 SVIE OS modules across the 9 phases.
Data Quality & Evidence Reliability
Before we evaluate your venture, we need to understand the quality of information you're working with. Every evaluation is only as reliable as the evidence behind it. A venture built on assumptions is being steered blind — this module identifies how much of what you know is proven versus believed.
Is the available evidence reliable enough to support a credible evaluation?
The reliability of every evaluation depends on the reliability of the evidence behind it. This module forces a reality check: the difference between optimism ("my friends said they would buy it") and evidence ("50 verified M-Pesa pre-orders"). No venture can be evaluated more reliably than the quality of its own data.
Is the problem real, significant, and commercially meaningful?
The single most common reason ventures fail is that they solve a problem no one is urgently paying to fix. This module strips away solution bias and forces you to prove the pain is real — not just acknowledged, but actively felt and acted upon. In African markets especially, what people say they want and what they actually spend money on are often very different.
Have real, identifiable customers confirmed they will pay?
In many African markets, the user is not the buyer. A school student uses EdTech, but the parent or institution pays. An informal trader uses your logistics app, but you invoice their SACCO. Misidentifying who controls the budget leads to building products people love but no one purchases. This module maps the full customer ecosystem and checks for real evidence of willingness to pay — not just interest.
The SAM Chain — problem reach, paying capacity, distribution, conversion
A market size claim is not a single fact — it is a chain of four distinct claims, each requiring independent evidence. This module scores the weakest evidenced link in that chain, not your projected TAM/SAM/SOM figure, and prevents market assumptions from travelling through the rest of the evaluation disguised as validated facts.
Are current market conditions favorable, or is the venture too early or too late?
Being too early can be as fatal as being too late. A venture can have the right solution, but if the regulatory framework or infrastructure isn't ready, it burns capital educating a market that isn't ready to be educated.
Can the venture establish and defend a sustainable competitive position?
In emerging markets the biggest competitor is often the status quo — jua kali artisans, manual ledgers, WhatsApp groups, cash — not other funded startups. Ignoring these informal alternatives leads to flawed value propositions and mispriced products.
Is the value proposition compelling, differentiated, and clearly understood?
A great product fails if the customer cannot understand its value in under 30 seconds. Value communication must account for language, literacy, and channel — and the value must outweigh the total cost of ownership, including hidden costs like data bundles.
Has the venture achieved measurable, sustainable product-market fit?
PMF is the inflection point where the market pulls the product out of the startup. Before PMF, focus is learning; after, it's scaling. Misidentifying PMF leads to premature scaling and rapid cash burn.
Has the team accurately identified its critical knowledge gaps?
The greatest risk to a venture is not what it doesn't know, but what it doesn't know that it doesn't know. Founders often project formal-sector assumptions onto informal economies — this module forces you to confront blind spots before you burn capital on them.
Does the team have the functional skills to execute the next stage?
Strategy creates direction; capability determines whether execution is possible. This module evaluates what the team can actually DO, not what they know — and identifies single points of failure where a critical function relies on one person with no backup.
Do you have the stability and accountability to execute?
More ventures fail from founder fracture than from bad markets. In African ecosystems, co-founders often split equity based on friendship rather than contribution, and undisclosed personal financial pressures — what some call the "black tax" — can cause desperate decision-making. For solo founders, the question shifts: without a partner to challenge you, what external accountability mechanisms ensure you don't operate in an echo chamber?
Can the founders transition from operators to institutional leaders?
What got you here won't get you there. Scaling requires transitioning from informal hustle to institutional leadership — implementing governance, managing a board, delegating authority. Founders who resist oversight become the bottleneck to enterprise value.
Can the broader organization execute reliably and scale its human capital?
A great team with a mediocre idea will out-execute a mediocre team with a great idea. This module looks beyond the founders at the organizational engine — retention, culture, and whether institutional knowledge survives someone leaving.
Is there a clear, defensible path to capturing economic value?
A validated problem and a capable founder only create an investable venture when value can be converted into a sustainable economic engine. In African markets, many ventures fail not because their products are bad but because they copy Western business models without adapting to local payment realities — monthly SaaS subscriptions fail where customers think in daily or weekly cash flow. This module evaluates the commercial logic of how your venture makes money.
Is the revenue model aligned with customer behavior and capable of scaling predictably?
Revenue models in Africa must accommodate micro-transactions, mobile money withdrawal fees, and seasonal cash flows. A model that works in the West can fail locally if it ignores the friction of moving money from a customer's wallet to your bank account.
Does pricing capture value while remaining attractive to the target segment?
Pricing in emerging markets means navigating purchasing power parity, the "kadogo" (sachet) economy, and high price sensitivity. Founders often underprice to "gain market share" without realising they're attracting low-LTV customers while burning cash.
Does each incremental unit of the business contribute positive economic value?
In markets with high last-mile delivery costs, fragmented addressing, and mobile money fees, unit economics can turn negative at scale if not rigorously modeled. "Growing revenue" with broken unit economics is a fast track to insolvency.
Can the venture acquire customers predictably and efficiently at scale?
Digital ads work for urban millennials but fail for rural farmers or informal traders. Acquisition in Africa often relies on trust-based networks — community leaders, radio, WhatsApp groups, physical agent networks — not just a Facebook pixel.
Does the venture retain customers and expand revenue from existing accounts?
In low-trust environments, churn is often driven by one bad experience or a slightly cheaper competitor. Retention requires building real operational trust and becoming part of the customer's daily workflow.
Can the business grow revenue without a proportionate increase in cost or complexity?
Many ventures in emerging markets are actually profitable consultancies or agencies disguised as tech companies. True scalability requires decoupling revenue growth from headcount growth through technology, automation, or standardized agent networks.
Does the solution directly, effectively, and elegantly solve the validated customer problem?
In markets with low digital literacy or intermittent infrastructure, a "perfect" smartphone app is useless if it meets the user in the wrong place. The solution must meet the user where they are — USSD, SMS, WhatsApp, or a physical kiosk. Feature creep is a massive risk; simplicity and reliability trump advanced functionality.
Does the venture employ a disciplined, efficient, and adaptable product development process?
Many African tech ventures suffer from "agency syndrome" — building custom, unmaintainable code for every client instead of a scalable, standardized product. This module checks whether the team can avoid crippling technical debt while iterating rapidly on real feedback.
Is the product's technology architecture secure, scalable, and appropriate for current and future needs?
Technology choices must account for local data residency laws (e.g. Kenya's Data Protection Act), cloud infrastructure costs, and integration with legacy systems like core banking or government APIs. Over-engineering is fatal for a Concept-stage venture; under-engineering security is existential for a FinTech venture.
Does the venture possess or have a credible path to IP that provides a sustainable advantage and freedom to operate?
In many African jurisdictions, software patents are difficult to enforce. IP defensibility often relies instead on trade secrets, copyright, data moats, network effects, and rigorous IP assignment agreements. A common fatal flaw: founders never having freelance developers or early co-founders sign over IP to the company.
Does the venture consistently deliver a high-quality, reliable product that meets or exceeds expectations?
In markets where alternatives are scarce, users may tolerate poor quality initially, but trust breaks easily and permanently. A failed payment or a crash on a low-end device can mean a customer never returns. Quality must be tested against real local conditions — intermittent 3G, power outages, device fragmentation.
Does the venture possess a systematic, sustainable approach to product innovation and continuous improvement?
Markets in emerging economies leapfrog technologies rapidly — moving straight to mobile money, skipping credit cards entirely. Ventures need a systematic way to sense market shifts, test new features, and kill underperforming ones before they drain resources.
Does the venture have the operational infrastructure and process discipline to execute consistently?
In the early days, everything is ad-hoc — run via WhatsApp groups and mental notes. This module forces you to document workflows before they become bottlenecks. It separates ventures that have built a repeatable machine from those entirely dependent on founders' personal memory and manual intervention.
Can the venture reliably source what it needs to deliver its value proposition at the required quality and cost?
In emerging markets, supply chains are often fragmented, reliant on imports subject to customs delays, or dependent on informal vendors with variable quality. For SaaS, the "supply chain" is cloud infrastructure and local APIs (e.g. Safaricom Daraja) — a failure here halts the business just the same.
Can the venture produce or deliver its solution at the required volume, quality, and speed to meet demand?
Scaling in Africa often breaks down at the "last mile" — unstructured addressing systems, traffic congestion, reliance on informal logistics like boda-boda riders. This module checks whether the venture can actually deliver what it sells without the customer experience collapsing under growth.
Does the venture have a systematic approach to preventing defects and continuously improving delivery quality?
In low-trust environments, churn is often driven by a single bad experience — and word travels through community networks. Quality cannot be an afterthought; it must be built into the operational system, not just inspected for at the end.
Is the venture operating in full compliance with applicable laws and prepared for future regulatory scrutiny?
Many founders operate in "regulatory gray areas" hoping to fly under the radar until they scale — and then become targets for enforcement by bodies like the Central Bank, the Communications Authority, or the Data Protection Commissioner. A brilliant product built on regulatory non-compliance is a liability, not a venture.
Does the venture have a proactive approach to identifying operational risks and a tested continuity plan?
Emerging markets are prone to sudden operational shocks — political shifts, currency controls, internet blackouts, subsea cable cuts, power grid failures. This module checks whether the venture has a tested plan for business continuity, not just hope that nothing goes wrong.
Is the venture financially stable, and is its financial reporting reliable enough to support decisions?
Many early-stage ventures mix personal and business finances, or rely on unaudited spreadsheets that don't reflect reality. Reconciling mobile money (M-Pesa Paybill/Till) transactions with formal bank feeds is a common blind spot. This module establishes whether the financial data can actually be trusted.
Are the venture's funding requirements realistic, justified, and aligned with market standards for its stage?
Founders often ask for arbitrary amounts based on "what they think investors will give." Capital in the African VC landscape is expensive and patient. This module forces alignment between capital deployment and value creation — enough runway to reach the next milestone, without excessive dilution.
Does the venture have a clear, manageable cash flow trajectory and an optimized cash conversion cycle?
Profit is an accounting concept; cash is reality. In B2B markets, long payment cycles can kill a profitable venture. Paying for cloud services in USD while earning in local currency creates real FX exposure that erodes purchasing power over time.
Does the venture have a credible, mathematically sound path to sustainable profitability?
"Growth at all costs" is a flawed strategy where capital is expensive and purchasing power is low. This module checks whether there is a credible path to profitability that doesn't depend on perpetual subsidies or unrealistic margin expansion.
Is the venture structurally ready for institutional capital, and does it have a realistic path to a liquidity event?
The African exit landscape is maturing but constrained — most exits happen via regional M&A, secondary sales, or dividend recaps rather than IPOs. A messy cap table or missing IP assignments from early freelance developers is a classic deal-killer in technical due diligence.
Is the venture adequately protected against financial shocks, fraud, and macroeconomic volatility?
Emerging-market ventures face currency devaluation, sudden tax policy changes, high inflation, and customer concentration risk. Internal controls in early-stage ventures are often weak, creating real risk of founder misappropriation or accidental fraud.
Is the venture's execution consistently aligned with its long-term strategic vision, or is it drifting?
In emerging markets, macroeconomic shocks — sudden currency depreciation, fuel price hikes, changes in Finance Acts — are common. This module forces a distinction between strategic agility (adapting tactics while preserving the core mission) and strategic drift (abandoning the plan to chase every short-term survival tactic).
Does the venture have a credible, capital-efficient, and scalable strategy for sustainable growth?
Expanding across African borders — e.g. leveraging AfCFTA to move from Kenya into Uganda or Nigeria — introduces severe regulatory, cultural, logistical, and FX complexity. Copy-pasting a local model into a new geography rarely works, and "growth at all costs" is a flawed strategy where capital is expensive and patient.
Do the venture's partnerships create genuine strategic advantage, or introduce unacceptable dependency risk?
African ventures often rely heavily on third parties for critical infrastructure — mobile money APIs, distribution networks, regulatory navigation. Partnerships are essential, but over-reliance creates fatal single points of failure if a key telco changes API pricing or a distributor shifts focus.
Is the venture's competitive moat durable, and are its operations sustainably positioned for long-term shifts?
In Africa, ESG is tied directly to operational survival — climate shocks hit agricultural supply chains and infrastructure, and regulatory shifts like the Kenya Data Protection Act can shut down non-compliant ventures overnight. Communities and governments increasingly demand shared value, not just extraction.
Does the venture have a credible pathway to liquidity, and is it deploying capital efficiently for stakeholder returns?
The African exit landscape is maturing but constrained — IPOs on regional bourses are rare for early-stage tech. Most exits happen via regional M&A, secondary sales, or dividend recaps. Evaluators must check the venture's capital structure aligns with these realistic exit multiples, not Silicon Valley IPO fantasies.
Viewed holistically, does the cumulative evidence support the venture's readiness for its proposed next step?
A great product with toxic founder dynamics is still a failure. A massive market with broken unit economics is a value-destroying machine. This module forces a step back from individual module scores to assess the venture as one interconnected system — where isolated Yellow Flags across phases can combine into a systemic Red Flag at the enterprise level.
Are all prerequisite Decision Gates satisfied, and are there any unresolved Fatal Flaw Conditions?
Evaluators frequently fall in love with a venture's potential and subconsciously downgrade the severity of a critical risk — ignoring a messy cap table because the product is brilliant, for example. This module is the final governance checkpoint that strips away optimism and enforces the framework's non-negotiable standards.
What is the final, actionable recommendation for this venture?
This is the culmination of the entire evaluation process — translating many hours of rigorous, evidence-based evaluation into a concise, defensible, actionable decision that stakeholders can rely on, insulated from charisma bias and intuition.
What are the critical, time-bound actions the venture must take to de-risk the business and mature?
Founders and investors need to know exactly what to do on Monday morning. This module converts abstract evaluation scores into concrete, assigned tasks that directly increase enterprise value and de-risk the business.
How and when will this venture be re-evaluated to ensure new risks are identified promptly?
Ventures evolve rapidly. A venture that scores a "Go" today may face a fatal flaw tomorrow due to a founder departure, a regulatory ban, or the sudden loss of a key customer. This module hands the evaluation over to ongoing portfolio management or governance, so it remains a living tool rather than a static report.
Building a benchmark database from the SVIE OS evaluation pool
Individual evaluations answer: Is this venture viable? Portfolio intelligence answers: Which types of ventures consistently score well, and where do cohort-wide blind spots cluster? This module transforms the evaluation pool into a proprietary intelligence asset — one that grows more accurate with every evaluation completed. No external comparable data is required or used. The benchmark is the SVIE OS evaluation pool itself.
All 19 GES principles, verbatim from SVIE OS v1.1.0.
Evaluations shall be conducted independently of personal bias or predetermined outcomes. The evaluator's role is to follow the evidence, not to validate a preferred conclusion. Where conflicts of interest exist, they must be disclosed and managed before the evaluation begins.
Every significant conclusion shall be supported by objective evidence proportionate to the decision's significance. Assertions, intuition, and reputation alone do not constitute evidence. Conclusions must be traceable to specific evidence items classified within the 5-Level Evidence Hierarchy (GES-12).
Every conclusion, score, and Decision Gate outcome shall be traceable to supporting evidence and documented assumptions. An evaluation that cannot be reconstructed from its records is not a defensible evaluation. Traceability is the foundation of audit readiness.
Material uncertainties, assumptions, and limitations shall be openly documented. Gaps in evidence, areas of elevated risk, and unresolved questions must be recorded in the relevant register rather than omitted or minimised. Transparency protects both the evaluator and the venture.
Equivalent evidence shall produce equivalent conclusions when evaluated using the same methodology. Evaluators must apply the same standards across modules, ventures, and cohorts. Where context requires different treatment, the contextual adjustment must be explicitly documented.
No significant business domain should be omitted without documented justification. Skipping a module or phase requires a formal scope note explaining why the omission is appropriate given the venture's stage, sector, or evaluation purpose. Omission without justification is a governance failure.
Evaluation depth shall be proportionate to venture complexity, maturity, and risk profile. A Concept-stage venture should not be evaluated to the same depth as a Series A candidate. The MVE Track (GES-14) provides the minimum viable evaluation path for rapid screening contexts.
Material assumptions shall be validated before becoming the basis of significant decisions. Where validation is not yet possible, the assumption must be logged in the Assumption Register and the associated Decision Gate must reflect the outstanding uncertainty.
Every evaluation shall maintain disciplined governance, version control, and audit trails. Documents must be versioned, decisions must be dated, and all evaluators must be identified. Governance is not bureaucracy — it is the mechanism that makes an evaluation defensible.
Each completed evaluation shall generate organisational learning to strengthen future evaluations. Post-evaluation reviews, calibration sessions (GES-16), and cohort intelligence (Module 8.6) are the primary mechanisms through which the framework improves over time.
Modules are not weighted equally. SVIEOS applies Dynamic Stage Weighting to prevent over-evaluating irrelevant domains:
Concept/Discovery: 60% Phase 1 (Market), 30% Phase 2 (Founder), 10% Phase 3 (Business Model). MVP/Pilot: 30% Phase 1, 30% Phase 2, 40% Phases 3 & 4 (Product/Business Model). Early Revenue/Growth: 20% Phase 2, 40% Phase 5 (Operations), 40% Phase 6 (Financials).
Rule: Penalising a Concept-stage venture for lacking Phase 5 operational maturity is a violation of this standard.
To resolve conflicting evidence and ensure empirical rigour, all supporting evidence is classified into five levels. When modules disagree, the conclusion supported by the higher-level evidence prevails.
Level 5 (Highest): Independently verified empirical data (e.g., audited financials, third-party technical audits). Level 4: Primary organisational records (e.g., CRM data, signed contracts, cap table, bank statements, verified M-Pesa/API transaction logs). Level 3: Structured expert judgment & internal documentation (e.g., SOPs, management accounts, formalised meeting minutes). Level 2: Management assertions & preliminary evidence (e.g., founder interviews, unverified TAM projections, pitch decks). Level 1 (Lowest): Opinions, anecdotes, unsubstantiated claims, and "intuition."
Triangulate: Seek a Level 4 or 5 source to break the tie.
Hierarchy Rule: If Module A claims success based on Level 2 evidence, and Module B claims failure based on Level 4 evidence, Module B prevails.
Pause Trigger: If conflicting evidence exists at the same hierarchy level, the evaluation triggers a Pause at the Decision Gate until targeted validation resolves the discrepancy.
SVIE OS provides four named evaluation tracks. Each track is defined entirely by SVIE OS module numbers, with no external framework dependency. Evaluators select the appropriate track based on the decision context before the evaluation begins. The selected track must be recorded in Module 0.1.
Track 1 — MVE Track (Minimum Viable Evaluation) Modules: 0.1 (Scope), 1.1 (Problem Validation), 1.2 (Customer Validation), 2.3 (Founder Dynamics), 3.1 (Business Model). Time: ~20–40 minutes per venture. Use case: initial screening where a binary viability signal is required with minimal time investment. Output: Go / No-Go / Deep-Dive signal with a one-paragraph narrative per module — no phase score, no Investment Recommendation.
Track 2 — Screening Track Modules: Phase 0 (0.1–0.2), Phase 1 (1.1–1.7), Module 2.3. Time: ~2–3 hours. Use case: first-round shortlisting for accelerators, studios, and grant programmes. Output: Phase 0/1 summaries, Module 2.3 outcome, three-flag summary, formal shortlist recommendation.
Track 3 — Readiness Track Modules: Phase 0 (0.1–0.2), Phase 1 (1.1–1.7), Phase 2 (2.1–2.5), Phase 3 (3.1–3.7), Phase 6 (6.1–6.6). Time: ~1–2 days. Use case: investment readiness, pre-seed evaluation, grant applications. Phase 5 (Operations) and Phase 7 (Strategy) are excluded.
Track 4 — Full Track Modules: all nine phases (0.1 through 8.6). Time: ~5–10 working days. Use case: institutional due diligence, growth-stage and Series A+ evaluation, portfolio governance. The only track that produces a complete Investment Recommendation (Module 8.3).
Scores (1–5) are anchored to observable behaviours, not arbitrary feelings:
5 (Excellent): Evidence is Level 4/5, process is institutionalised, zero critical gaps. 3 (Adequate): Evidence is Level 3, process exists but is founder-dependent, manageable gaps with a clear mitigation plan. 1 (Deficient): Evidence is Level 1/2, process is absent, critical gaps pose a Fatal Flaw risk.
To ensure different evaluators score consistently:
Blind Scoring: Two evaluators independently score a sample module. Variance Check: If scores differ by >1.0 point, a calibration meeting is required. Consensus: Evaluators must align on the Evidence Level applied before agreeing on the final score.
Standard venture evaluation criteria assume a broadly comparable operating environment across startups. This assumption fails materially when a venture operates in a sector where regulatory requirements, mandatory professional credentials, multi-authority approvals, or specialist infrastructure prerequisites are central to commercial viability rather than peripheral to it.
Sector Complexity Classification — every venture is assigned to one of four classes, determined by the regulatory, professional, and operational prerequisites of its primary industry. Where a venture spans multiple sectors, the higher classification applies.
Class A — Standard: no mandatory pre-operational licensing, no regulated professional requirements, no multi-authority approval. Examples: consumer technology, B2B SaaS, e-commerce, marketplace platforms, media, non-hazardous logistics, agritech.
Class B — Regulated: single-authority oversight, defined licensing pathway, moderate compliance requirements achievable within a normal runway. Examples: fintech and mobile money, SACCOs, microfinance, insurance, food service, ride-hailing.
Class C — Complex Regulated: concurrent multi-authority oversight, mandatory professional licensing, and/or material infrastructure or safety standards. Examples: healthcare facilities, private education, energy generation and distribution, construction, aviation.
Class D — Specialist: full Class C burden plus a founding-team requirement for non-transferable scientific, engineering, or clinical expertise. Examples: biotech and pharma, medical devices, nuclear/radiological, geothermal, mining and extractives at scale.
Consequence Rule — for Class C/D ventures: (1) Confidence Cap — aggregate confidence capped at Moderate until Sector Readiness is verified. (2) Fatal Flaw Trigger — 2+ unaddressed Class C requirements (no plan/timeline/engagement), or a single unaddressed Class D specialist requirement, triggers a Fatal Flaw that overrides the aggregate score. (3) Early Stage Exception — at Ideation/Pre-Prototype stage, the Fatal Flaw is suspended in favour of a carried-forward Sector Readiness Note, unless the founder has misrepresented their regulatory status (which forfeits the exception permanently).
SVIE OS uses a staged weighted aggregation model to convert module scores into phase scores, and phase scores into a venture-level Total Evaluation Score. The weighting is stage-dependent: at early stages, market and founder evidence dominates; at growth stages, financial and operational evidence dominates.
Step 1 — Module Score: unweighted average of domain scores, rounded to one decimal. Step 2 — Phase Score: average of completed module scores within the phase. Evidence Gap modules (GES-08) are excluded, not scored as zero. Step 3 — Stage-Weighted Venture Score (TES): phase scores combined using stage-dependent weights into a single 1.0–5.0 figure.
Total Evaluation Score Bands: 4.5–5.0 — Strong Go. Level 4/5 evidence throughout, no Fatal Flaw. 3.5–4.4 — Go. Predominantly Level 3/4, no unresolved Fatal Flaw. 2.5–3.4 — Conditional Go. Mixed Level 2/3, flags require resolution. 1.5–2.4 — Pause. Predominantly Level 1/2, material gaps. 1.0–1.4 — No-Go. Insufficient/contradictory evidence or an unresolved Fatal Flaw.
A Fatal Flaw Condition produces a mandatory No-Go regardless of the TES.
Market validation evidence is only meaningful relative to the ecosystem role of the person generating it. Before any market signal is weighted as evidence, the evaluator confirms the ecosystem role of the signal source and that the signal tier matches the claim being made.
Ecosystem Role Map — every venture has a User (interacts with the product), a Payer (transfers economic value), and a Beneficiary (derives the primary outcome benefit), who may be the same person or distinct parties. This matters most in B2B2C, donor-funded, employer-sponsored, and multi-sided models.
Signal Tier Classification: Tier 1 — Interest Signals: sign-ups, clicks, survey responses. Confirm awareness, not demand. Tier 2 — Intent Signals: LOIs, pilot agreements, waitlists. Indicate directional demand, not price tolerance. Tier 3 — Transaction Signals: actual payment, renewals, binding contracts. The only tier that constitutes direct evidence of willingness to pay.
A Tier 1 signal from a User cannot be cited as evidence of Payer demand. Where a founder cites User signals as Payer-demand evidence without a structural link between the roles, the evaluator flags Signal Misattribution and downgrades the Evidence Level — reduced by at least one level, or to Level 1 if no Tier 2/3 Payer signal exists at all.
GES-14 — the four named tracks and what each is for.
Solo founders and early teams (fewer than 5 members, no institutional funding)
Run the five core modules and receive a scored report, confidence rating, and prioritised action plan. Governance standards are calibrated for early-stage realities under GES-14 Minimum Viable Governance. This is the entry point for most first-time users.
Venture studios, accelerators, and grant-making programmes evaluating multiple ventures for selection
A 13-module evaluation covering founder, market, business model, and early financial signal — deeper than the MVE, targeted rather than exhaustive. Ventures reaching Conditional Go or better proceed to full evaluation. Module 8.6 (Portfolio Intelligence Framework) activates once three or more ventures have completed evaluation in the same cohort.
Founders preparing for investor meetings, grant applications, or strategic partnerships
A comprehensive, self-directed diagnostic covering all nine phases. No minimum evidence threshold — the output is a frank readiness assessment identifying your strongest evidence, your gaps, and what you need before approaching investors. Multi-session; save and resume at any point.
Investors and grant-makers using SVIE OS as their primary due diligence framework
A targeted 29-module set spanning founder, market, business model, product, and financial evaluation, plus the Integrated Decision Framework. Phase 6 (Financial Evaluation Sprint) and Phase 8 (Integrated Decision Framework) must reach minimum Level 3 evidence for a Go recommendation. Does not include Phase 5 (Operations), Phase 7 (Strategy), or Modules 8.4–8.6.
GES-12 — how every claim is graded, 1 through 5.
Audited financials, third-party due diligence, independent market research, regulatory approvals, signed contracts with major partners.
Pilot revenue data, signed LOIs, cohort analytics from real users, documented customer interviews with specific named sources.
Unpaid pilot users, documented customer discovery interviews (5+), observable market behaviour, cited industry reports from credible sources.
Surveys, analogous market data, competitor analysis, informal conversations that are not documented or systematically conducted.
Personal belief, anecdote, intuition, or "I think people would pay for this." This is the starting point — not the evidence.
Every module ends with one of four structured outcomes.
The evidence meets or exceeds the threshold for this module. The venture is ready to move to the next gate. Strengths are documented for the investor profile.
The core foundation is sound but specific gaps must be addressed within a defined timeframe. The venture can progress while resolving the conditions.
Critical assumptions are unvalidated or evidence quality is too low to proceed with confidence. Continuing without resolution wastes resources and misleads investors.
A fatal flaw has been identified — a condition that cannot be resolved within the current venture structure. This is not failure; it is intelligence that prevents greater loss.
GES-17 — determines what regulatory burden a venture's sector carries.
No mandatory pre-operational licensing, no regulated professional requirements, no multi-authority approval processes. Standard GES criteria apply unmodified.
e.g. Consumer technology, B2B SaaS, e-commerce, marketplace platforms, media and content, non-hazardous logistics, agricultural technology
Primary oversight by a single authority, defined licensing pathway, moderate compliance requirements achievable within a normal pre-launch runway.
e.g. Fintech and mobile money (CBK), SACCOs (SASRA), microfinance institutions, insurance (IRA), food service, ride-hailing and mobility platforms
Concurrent oversight by multiple regulatory authorities, mandatory professional licensing, and/or material physical infrastructure or safety standards that must be satisfied before any operations commence.
e.g. Healthcare facilities, private education, energy generation and distribution, construction and real estate development, aviation
Full Class C burden plus a requirement that the founding team possesses or has contracted specific scientific, engineering, or clinical expertise that is not transferable from adjacent domains.
e.g. Biotechnology and pharmaceuticals, medical devices, nuclear and radiological applications, geothermal infrastructure, mining and extractives at scale
Market size is scored as a chain of four dependent domains — the weakest link, not an average.
1: Global/continental report, percentage applied with no local source. 2: National/regional secondary source (KNBS, World Bank, AfDB, industry association), bottom-up but not segment-specific. 3: Segment-specific secondary source that directly counts the population with the problem. 4: Primary research (interviews/surveys) supplementing secondary data, confirming the problem in the counted population. 5: Third-party validation — a comparable company's customer base, a commissioned market study, or verifiable traction data implying the count.
1: Claim that the price is affordable with no data. 2: Segment-level revenue/income data used to infer affordability, no direct customer confirmation. 3: Proxy data — a comparable product sold at a similar price in this segment, with adoption evidence. 4: Direct pricing conversations with 10+ named potential customers, documented willingness to pay at or near the stated price. 5: Pilot pricing test with real transactions — customers have paid, even a trial fee or deposit, at or near the stated price.
1: Channel named but no evidence it works or has capacity; can't explain how they'd find the first 100 customers. 2: Distribution logic is coherent and capacity estimated, but untested — no customers acquired, no channel confirmed. 3: Channel tested in a limited way — cold outreach with response-rate data, platform account with early traffic, or partner conversations initiated (not signed). 4: Channel operational with evidence — at least one customer acquired via the stated channel, or a signed distribution agreement with a partner who has the target audience. 5: Multiple customers acquired through the stated channel; capacity demonstrated, not projected.
1: Conversion rate is an assumption with no basis stated. 2: Industry benchmark cited with source referenced. 3: Comparable company's conversion rate cited with evidence the comparable is genuinely analogous. 4: Own pilot data — founder has run a sales/trial process with an observed conversion rate from at least 5 prospects. 5: Sustained traction — multiple sales cycles, stable conversion rate drawn from at least 20 prospects across different cohorts or time periods.