What is an AI due diligence audit?

An AI due diligence audit is an independent assessment of a company's AI and technology that tells investors, acquirers or founders whether the AI is real, defensible, cost-efficient, secure and compliant, and what it will cost to fix any issues found.

Why did Quikdu need an AI due diligence audit?

AI powers key parts of Quikdu's business, including SEO content generation, customer engagement and marketing. A readiness audit let Quikdu understand its AI value, costs and risks clearly, and fix issues before any investor or partner review.

How do you tell whether a company's AI is proprietary or a wrapper?

We trace every AI call in the code and workflows to see which models are used, what custom data, prompts and orchestration sit around them, and whether a competitor could replicate the capability quickly. We then test the AI independently against the claims made about it.

Why does AI cost per order matter?

AI model usage is a variable cost that grows with every customer interaction. If it is not measured per order or per user, it can quietly erode margins as the business scales. Investors increasingly ask for this number.

What is AI vendor lock-in risk?

It is the risk that a business depends so heavily on one AI model provider that a price increase, policy change or outage at that provider disrupts the business. A model-agnostic architecture with fallback models reduces this risk.

How was AI-generated health content reviewed?

A sample of AI-generated Ayurveda content was checked for factual accuracy, validity of cited research references and the risk of misleading health claims, and the publishing workflow was strengthened with guardrails and human review steps.

Which compliance areas does the audit cover?

The audit covers data privacy under India's DPDP Act, consent for using customer data in AI workflows, terms for data shared with AI providers, open-source and model licences, and sector-specific content rules where relevant.

What does the final due diligence report include?

The report includes an overall verdict, a nine-area Red, Amber, Green scorecard, an AI reality check, a risk register with severity and deal impact, unit economics and cost outlook, and a prioritised remediation roadmap.

Can founders use this audit before raising investment?

Yes. A vendor-side readiness audit uses the same framework investors apply, so founders can find and fix red flags in advance, answer technical questions confidently and protect their valuation.

How can ELITIST SOFTWARE SOLUTIONS LLP help with AI due diligence?

ELITIST SOFTWARE SOLUTIONS LLP offers fixed-scope, fully remote AI and technology due diligence audits for investors, acquirers, lenders and founders, from a 5-day Red Flag Scan to a full buy-side audit, followed by optional remediation and AI cost reduction services.

Case Study

AI & Technology Due Diligence Audit

An independent-style AI and technology due diligence readiness audit of Quikdu's Ayurveda ecommerce platform, assessing AI authenticity, AI cost and unit economics, architecture, code quality, security, data compliance, IP and team risk to make the business investor-ready.

AI Authenticity

Traced every AI call across web, app, WhatsApp and n8n workflows to separate real AI value from commodity API usage.

AI Cost Analysis

Calculated AI cost per output and per order, with a 3x and 10x scale model and optimisation plan.

Smart API System

Vendor Risk

Assessed dependency on AI model providers and designed a model-agnostic fallback approach.

CLIENT

Quikdu Solutions Private Limited

Industry

HealthTech • Ecommerce • SaaS

Delivered Services

AI Due DiligenceTechnology Due DiligenceAI Authenticity AssessmentAI Cost & Unit Economics ReviewArchitecture & Scalability ReviewCode Quality AssessmentSecurity & Compliance ReviewIP & Licence ReviewAI Vendor Lock-in AssessmentRemediation Roadmap

Challenge

Quikdu had grown into a multi-channel Ayurveda ecommerce business with a website, a mobile app, WhatsApp-based customer support and AI-driven workflows, including an n8n pipeline that generates SEO product blogs with PubMed Central references. As AI became central to content, marketing and customer engagement, leadership needed clear answers to the questions any investor or acquirer would ask: Which AI capabilities are genuinely proprietary and which depend on third-party models? What does AI cost per order and per customer, and how will that scale? Is AI-generated health content accurate and compliant? Is the platform secure, maintainable and free of IP risk? The goal was a vendor-side due diligence readiness audit that would surface every red flag before an external reviewer did, and turn the findings into a prioritised fix plan.

Solution

60-Point Due Diligence Framework

Applied ELITIST's standard 60-point checklist across nine areas: architecture, code quality, AI authenticity, infrastructure and unit economics, security, data and compliance, intellectual property, team and process, and product roadmap. Every check was scored 0 to 3 and rolled up into a Red, Amber, Green scorecard.

AI Authenticity Assessment

Traced every AI call across the website, app, WhatsApp flows and n8n workflows to map exactly which capabilities rely on third-party model APIs, which use custom prompts and orchestration, and where proprietary value is created through Quikdu's own product data, workflows and customer context.

AI Content Quality & Health-Claim Review

Tested a sample of AI-generated Ayurveda blog content for factual accuracy, citation validity against PubMed Central references, and risk of misleading health claims under Indian advertising rules such as the Drugs and Magic Remedies (Objectionable Advertisements) Act. Recommended human review gates and claim guardrails in the publishing workflow.

AI Cost & Unit Economics Analysis

Analysed AI and cloud spend against content output, orders and active customers to calculate cost per blog, per conversation and per order, then modelled the cost at 3x and 10x scale and identified savings from model routing, caching and lower-cost models.

AI Vendor Dependency & Lock-in Review

Assessed what would happen if a primary model provider raised prices or changed terms, and recommended a model-agnostic abstraction layer and fallback models so core workflows can switch providers without a rebuild.

Architecture & Scalability Review

Reviewed the Node.js, React Native and MongoDB architecture, third-party integrations and automation workflows to identify single points of failure, scaling limits, backup and disaster recovery gaps, and dependencies that would concern an investor.

Code Quality & Technical Debt Assessment

Evaluated repository structure, test coverage, CI/CD maturity, outdated dependencies and documentation, and estimated technical debt so that it could be planned and disclosed rather than discovered.

Security & AI-Specific Risk Review

Built on the platform's existing Secure SDLC and DevSecOps foundation to check secrets management, access control and dependency vulnerabilities, and added AI-specific tests for prompt injection in customer-facing flows and leakage of customer data to external model providers.

Data Privacy & Compliance Review

Mapped personal data across the website, app, WhatsApp and marketing tools, reviewed consent and lawful basis for using customer data in AI workflows, and assessed readiness against India's Digital Personal Data Protection (DPDP) Act.

IP, Licence & Team Risk Review

Checked code ownership and contractor IP assignments, open-source and model licences for commercial use, and key-person dependency in critical systems and automations, with recommendations to document and de-risk them.

Outcome

Investor-Ready Technology Profile

Quikdu gained a clear, evidence-based picture of its technology and AI capabilities, presented in the same scorecard and risk register format an investor's due diligence team would use.

Clear AI Value Story

Separated genuinely proprietary value, such as product data, curated references and automated content and support workflows, from commodity model usage, giving leadership an honest and defensible way to describe its AI.

Known AI Cost per Order

Established AI cost per blog, per conversation and per order, with a scale model and optimisation plan, so AI spend can grow in line with revenue rather than ahead of it.

Reduced AI Vendor Risk

Defined a model-agnostic approach and fallback options so that a price change or outage at a single AI provider would not disrupt content, marketing or customer support.

Safer AI-Generated Health Content

Introduced citation checks, claim guardrails and human review steps for AI-generated Ayurveda content, lowering regulatory and reputational risk while keeping the speed of automated SEO publishing.

Compliance Readiness

Mapped customer data flows and identified the steps needed for DPDP Act readiness, including consent, data minimisation and terms for data shared with AI providers.

Prioritised Remediation Roadmap

Converted every Red and Amber finding into a plan grouped by urgency, with effort estimates, so issues are fixed before an external audit rather than negotiated against the valuation.