How AskAjay Is Governed

AskAjay cannot provide legal advice. For legal questions about AI compliance, regulatory interpretation, or contractual obligations, consult qualified legal counsel.

AskAjay cannot access real-time data. The knowledge base is updated periodically, not in real-time. Regulatory landscapes change — verify time-sensitive information independently.

AskAjay cannot know your organizational context unless you provide it. Recommendations are framework-driven, not situation-specific. For organizational-specific guidance, book a discovery call.

Scoring rubrics in the Canvas assessment are empirically informed but not statistically validated across large samples yet. We are transparent about the maturity of our methodology.

Responses may not reflect the very latest regulatory changes. When a response cites a date or regulation, verify it is current.

The advisory boundary: AskAjay tells you when a question requires human judgment, organizational context, or legal expertise that goes beyond what frameworks can provide.

What we collect: queries you submit to AskAjay, Canvas assessment responses (for scoring and optional benchmarking), and email addresses for account identification.

How data is stored: Supabase PostgreSQL with Row Level Security, encrypted at rest. All data remains in Supabase's infrastructure.

Conversation logs: retained for 30 days for quality improvement, then permanently deleted. We do not train models on your conversations.

Canvas data: assessment responses are anonymised and aggregated for peer benchmarking only when the sample exceeds 100 responses. No personally identifiable information appears in benchmarks.

GDPR position: data processing is based on legitimate interest and explicit consent. You have the right to request deletion of all your data at any time by emailing hello@askajay.ai.

No data is sold to third parties. Ever. No exceptions.

We practice what we preach. Here is our own Minimum Viable Governance framework applied to AskAjay:

GOVERN: Our AI inventory contains one system (AskAjay chatbot + Canvas). Risk register: hallucination (mitigated by RAG grounding), outdated information (mitigated by periodic knowledge base refresh), over-confidence (mitigated by advisory boundary in system prompt). Accountability: Ajay Pundhir, personally.

MAP: Stakeholders are executives, governance teams, and AI leaders. Data sources are Ajay's published articles and Q&A pairs. Deployment context is a web application with no autonomous decision-making.

MEASURE: Performance baselines include response relevance, citation accuracy, and user satisfaction. Drift monitoring tracks knowledge base freshness.

MANAGE: Incident response — if AskAjay gives incorrect advice, the retraction log is updated, the knowledge base is corrected, and affected users are notified. Review cadence: monthly.

The Family Test: "Would I be comfortable if this AI gave governance advice to my family's company?" This is the standard AskAjay holds itself to.

Using our own Liability Ledger framework, here is AskAjay's current ethical debt assessment:

D1 Bias Debt: LOW — No user-facing decisions. Retrieval-based system (not generative decisions). Monitoring: check for systematic retrieval bias quarterly.

D2 Transparency Debt: LOW — Architecture published on this page. Limitations documented. Scoring methodology open.

D3 Governance Debt: LOW — This governance page exists. MVG applied. Monthly review cadence.

D4 Privacy Debt: LOW — Minimal data collection. 30-day retention. No PII in benchmarks.

D5 Accountability Debt: LOW — Single accountable person (Ajay Pundhir). Escalation: hello@askajay.ai.

Overall Liability Score: estimated 8–10/125 (Debt Free band). Next audit: Q2 2026.

This log documents any corrections, retractions, or material updates to AskAjay's knowledge base or scoring methodology.

Policy: Any factual error discovered in the knowledge base is corrected within 48 hours. No silent edits — all material corrections are logged here with the date, what changed, why, and the impact.

Current status: No retractions to date. This log will be updated when corrections are made.

The Canvas assesses AI readiness across 5 pillars: Strategic Alignment, Data Infrastructure, Talent & Culture, Operational Processes, and Ethics & Governance.

Each pillar has 3 calibrated questions (15 total). Questions use varied input types (card-select, single-select, sliders, multi-select) designed to prevent generous self-assessment.

Per-question scoring maps answers to a 1–5 scale. Pillar score = average of its 3 questions, rounded to the nearest 0.5.

Overall readiness = your lowest pillar score (the weakest-pillar rule). A 5-4-4-4-2 organization is a Level 2 organization.

Maturity levels: Ad Hoc (1–1.4), Reactive (1.5–2.4), Structured (2.5–3.4), Strategic (3.5–4.4), Transformative (4.5–5.0).

Scoring criteria are published and transparent. Proprietary weights (how individual answer options map to scores) are kept internal to preserve assessment integrity.

The methodology was developed from 12+ organizational assessments conducted in advisory engagements between 2022 and 2025.