[Investor Teaser] Epistemic Auditor AI: Decolonial Governance for African Agritech

1     The Problem: Generative Extraction in AI-Driven Agriculture

African agriculture is at a crossroads: AI promises productivity gains amid climate-related crises and food insecurity, with the continent's agtech market projected to reach $1B by 2030 (up from $600M in 2022 investments). Yet, mainstream LLMs perpetuate "generative extraction"—translating agrarian life into computable forms optimized for external markets while erasing indigenous knowledges. In Malawi and Kenya, where 80% of food comes from smallholders relying on local epistemologies (e.g., Chichewa/Tumbuka/Swahili soil wisdom), generic AI advice amplifies epistemic injustice: it overwrites relational practices with high-modernist hierarchies, leading to "thin integration" (Mann, 2018)—inclusion on terms that extract data without redistributing power or value.

Real risks: Encrypted bias renders local knowledge "inaudible" in model pipelines, exacerbating gender gaps (women hold 70% of SSA ag labor but <30% digital access) and trust barriers (smallholders wary of data colonialism). Without governance, AI scales plantation logics—efficiency over equity—trapping 500M+ SSA farmers in agronomic dependency. AU's 2024 Continental AI Strategy mandates ethical, inclusive tools, but compliance is performative; funders like IDRC/AI4D demand decolonial proofs amid rising "responsible AI" grants (CAD$10M+ allocated 2025-27).

2     Our Solution: Epistemic Auditor AI

Epistemic Auditor is a governed LLM layer that audits and enforces decolonial integrity in agtech pipelines. Built on retrieval-augmented generation (RAG) with refusal boundaries, it detects encrypted bias—where indigenous knowledge is present but structurally silenced—and prioritizes epistemic justice:

• Core Features: Community-defined corpus governance (provenance tracking, consent-based retrieval); encrypted-bias audits (pipeline variations to isolate loss points); "infrastructures of refusal" (e.g., model says "I don't know" or defers to local experts); multilingual support (English, Chichewa, Swahili) for actionable, culturally resonant advice.

• Value Prop: Plugs into platforms like Apollo Agriculture or Kitovu, turning extractive AI into equitable tools. For farmers: Advice that respects local epistemologies (e.g., integrating Tumbuka crop rotations). For platforms: Compliance with AU ethics, reduced harms, and premium pricing (e.g., 20% uplift in smallholder retention via trust-building).

• Business Model: SaaS audit-as-a-service ($500-200K/month per client, tiered by scale); hybrid grants (e.g., AI4D pilots) to revenue. Target: Agtech startups, NGOs, donors—initial focus Malawi/Kenya (10M+ smallholders).

Proof-of-Concept: Piloted via Bayreuth Fellowship (2026-27 outputs: governed agrarian corpus, annotation schema, Decolonial Curriculum Rewriter prototype). Early traction: Can integrates with Malawi advisory pilot, surfacing X% more indigenous insights while flagging X% biased outputs in tests.

3     Market Opportunity & Competitive Edge

Africa's AI-ag market: $200M+ in 2026 (CAGR 23% from $43M in 2024), driven by donor ecosystems (IDRC, AUDA-NEPAD) and investments (e.g., Ghana's $100M AI hub). Total addressable: $1B+ by 2030, with ethical AI niche underserved—competitors like ThriveAgric or Aerobotics optimize yields but ignore epistemic erasure, risking regulatory backlash.

Our Edge: Not another "precision" tool, but a decolonial governor that makes existing platforms accountable. Substantiated by Mbembe's Black Reason: We dismantle racialized epistemic projects, ensuring AI builds sovereignty, not dependency. Revenue Potential: 50 clients Year 1 ($300K ARR); scale to 200 by Year 3 ($2.4M) via partnerships (e.g., FAO/UNDP networks).

4     Team & Traction

Founder/CEO: Dumisani Z. Moyo, PhD

• Economic Geographer (U. Glasgow, 2025: "Cultivating Geographies" thesis on decolonial agrarian policy via Mbembe/Scott critiques).

• FAO Policy Specialist (2024-26).

• WFP/UNDP/USAID/INGO experience; publications in Frontiers in Sociology, Routledge (e.g., datafication in Malawi), etc.

• Technical: LLM fine-tuning (Oxford LLMs 2024), Python/R, Chichewa/Tumbuka fluency for grounded design.

• Networks: Bayreuth University (computer science, political economy), Germany; Kenya and Malawi collaborators (computer science, political economy).

Advisors/Partners: Experts in critical economic geography, computational social science, CS expertise for pipeline auditability, Kenya agrarian political economy). DDSc (founded 2021): AI news intelligence platform with X+ users, proving execution.

Traction:

• Bayreuth PoC: Auditable stack ready Q2 2027; encrypted-bias diagnostics validated in Malawi workshops.

• Pipeline: LOIs from X agtechs; AI4D grant application (CAD$1M) in prep.

• Metrics: Prototype reduces epistemic loss by X% in audits; gender-inclusive eval rubric ensures intersectional fairness.

5     Financials & Ask

• Projections (Conservative, SSA-focused): Year 1: $300K revenue (grants 60%, SaaS 40%); Break-even Year 2; $5M ARR by Year 5 (10% market share in ethical AI-ag). CAPEX low ($150K for compute/team via grants).

Seeking: $500K seed (equity/convertible) for MVP build, pilots, team (1 dev, 1 community lead). Use: 40% tech, 30% field ops, 30% biz dev. Exit: Acquisition by … (AI-ag ethics push).