Ai auditing services
AI Audit for Startup Companies | Best Website Audits
Is AI making a difference for your business?
AI is cool. But is it making a measurable impact on your business?
Our AI audits can tell you what’s working, what’s not working, what’s worth eliminating today, what’s worth building fresh or sharpening what you already have? These AI audits are necessary from time to time to check if your AI is adapting to rapid changes in market behavior, new operational challenges and unknowns, without hallucinating.
Even if you built an AI solution that was initially effective, if left unchecked, over time it could slip into bias, create compliance issues, or stop performing as expected. Can you trust your AI to stay accurate, compliant and unbiased? Codewave’s AI auditing services help you keep your AI systems reliable.
We follow a systematic process starting with model evaluation, for fairness and robustness. We analyze model behavior using Explainable AI (XAI) to identify decision logic and ensure transparency in high-stakes environments. Fairlearn evaluates fairness metrics to mitigate algorithmic biases across demographic groups.
TensorFlow Model Analysis monitors model predictions over time, ensuring consistent performance with real-time feedback. AWS SageMaker Clarify detects and flags drift in input data and outputs for reliability in dynamic datasets. Our agile approach ensures businesses stay ahead of risks while meeting industry standards.
Our audits reduce error rates and ensure 99.8% compliance with GDPR–your AI is ethical, accurate, and ready for whatever comes next.
With Codewave, you get AI you can fully trust.
99.98%
Compliance adherence rate
Instant
Model drift detection
40%
Improvement in decision accuracy
3x
Faster issue identification
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AI Auditing with Codewave: Continuously Improve
We check for algorithm transparency, data integrity, and operational efficiency– keep your AI systems sharp and relevant, as your business goes through changes continuously.
Every AI decision is transparent and traceable for regulatory compliance checks and maintaining user trust. We audit decision-making algorithms using Explainable AI (XAI) frameworks, which clarify AI decision processes.
SHAP (SHapley Additive explanations) assigns responsibility to individual data points, while LIME (Local Interpretable Model-agnostic Explanations) clarifies black-box model behavior in real-time.
For example, a financial institution using AI for credit approvals can face legal scrutiny over biased decisions, if left unchecked. An accountability assessment uncovers biases and ensures compliance, with reduced risks and improved customer trust.
Ethical compliance in AI involves aligning AI models with established ethical standards to ensure fairness, transparency, and accountability. We utilize advanced AI fairness tools, such as Fairlearn, to identify and mitigate algorithm bias. Aequitas is used to evaluate and address demographic disparities in model predictions. This ensures the AI system operates in line with ethical guidelines and regulations.
For example, a healthcare provider using AI-driven diagnostics may encounter biases if the AI model’s predictions vary across demographics. Ethical evaluation helps detect and correct these biases in real-time– ensuring fair outcomes and regulatory adherence.
Your AI models must align with strict industry regulations to prevent penalties. We use IBM Watson OpenScale for AI fairness and transparency. It verifies that all model outcomes meet the legal standards of GDPR, HIPAA, and financial regulations. Compliance is embedded directly into the model’s workflow to guarantee it stays aligned with regulatory guidelines.
For example, a healthcare provider using AI to diagnose patients must comply with HIPAA. The regulatory audit ensures that every model decision complies with patient data privacy laws and tracks any potential risks to mitigate violations before they occur.
Model interpretability ensures that AI decisions are understandable and transparent for accountability. We use LIME to generate localized explanations for individual predictions, ensuring stakeholders understand decision rationales. Shapley Values help us measure each feature’s contribution to a prediction, ensuring a fair assessment of feature importance. These methods increase transparency, especially in complex, black-box models, and ensure compliance with industry standards like FRTB and SR11-7.
For example, Imagine an AI model evaluates loan eligibility in credit scoring. Interpretability analysis helps financial institutions to explain customers how their credit scores are calculated and why they qualify or get denied. This builds trust with customers.
Poor quality or insufficient data leads to flawed AI results. Our data quality assessment identifies and rectifies inconsistencies like outliers, format discrepancies, and missing fields & values. This ensures clean, reliable datasets for AI models. DataRobot automates data preprocessing, detects anomalies, and provides real-time quality metrics. Talend handles data integration, cleaning, and validation to ensure compliance with industry standards.
AI systems require strong defenses against evolving cyber threats. Security vulnerability testing scans for potential risks in code and infrastructure like SQL injection, hardcoded credentials, or misconfigurations. We use SonarQube for static code analysis, and to detect vulnerabilities like SQL injection and cross-site scripting (XSS). Nessus scans networks to identify misconfigurations and weaknesses in cloud environments.
For example, an e-commerce platform using AI for fraud detection ensures secure APIs and data pipelines by identifying vulnerabilities. Regular assessments help safeguard customer information and transaction data.
AI systems require continuous monitoring for optimal performance across all processes. Our Operational performance reviews focus on metrics–response times, resource utilization, and throughput. New Relic measures latency and transaction throughput across the entire system. Datadog ensures optimized resource allocation, providing real-time monitoring to prevent potential downtime. These insights help maintain system efficiency, scaling operations as needed.
For example, a supply chain AI model analyzes inventory levels and shipping routes. Performance reviews ensure the system adapts in real-time to unexpected demand, preventing delays and optimizing delivery timelines.
AI solutions need to drive measurable user engagement and delight. We measure your AI’s smartness through user behavior tracking and interaction data – page views, session duration, scroll depth, and interactions. Google Analytics captures key metrics such as click-through rates and time spent on AI-driven content. Hotjar’s heatmaps reveal user attention areas, optimizing interfaces for better interaction flow. This helps prioritize AI features that resonate most with users.
For instance, a retail platform uses AI to recommend products to users. Impact analysis measures engagement, allowing for fine-tuning recommendations that increase purchase conversions and enhance the shopping experience.
Continuous monitoring ensures your AI systems adapt swiftly to changes and maintain high performance standards. We use Prometheus for real-time performance tracking, detecting deviations from expected model behavior. Grafana dashboards visualize key metrics, providing insights into model stability and reliability. This proactive approach avoids performance degradation over time.
For example, imagine a fintech firm continuously monitors AI-driven fraud detection–the system detects emerging threats promptly, maintains high accuracy and reduces false positives.
Solutions for Smarter AI Audits
Your AI deserves the best audits.
We check data quality, enhance transparency, and monitor results in real time. Expect an increase in model accuracy and reduced biases across systems.
How We Conduct AI Audits: A Transparent Approach
We take a hands-on, step-by-step approach to AI audits– everything is checked and verified– reduce errors by up to 95% and stay on top of compliance.
| Initial Assessment and Scope Definition | First, we get to know your AI systems inside and out. We break down the model architecture, data flow, and algorithms you’re using. This helps us pinpoint any risks and areas that slow things down. TensorFlow Model Analysis gives us a clear picture of your model’s behavior from the start. This sets the foundation for a focused and efficient audit. |
| Data Integrity Check | Next, we dive deep into your data to ensure it’s solid and reliable. DataRobots quickly checks for inconsistencies, gaps, or biases that could affect your AI model’s performance. This ensures your AI operates on a strong, reliable foundation, minimizing risks tied to flawed data. With clean, unbiased data, your model delivers more accurate and trustworthy results. |
| Model Evaluation and Risk Analysis | Here’s where we test your AI models to their limits. Stress tests reveal hidden biases and accuracy issues that standard checks may miss. Monte Carlo simulations run thousands of randomized scenarios to evaluate model stability and reliability. We pinpoint vulnerabilities that could lead to flawed decisions by simulating edge cases. This early detection helps you strengthen weak areas before deployment. |
| Compliance and Ethical Standards Review | This is where we ensure your AI models follow the rules. EU’s AI Act and ISO/IEC 27001 frameworks are used to check compliance gaps. Whether it’s data privacy, transparency, or algorithmic fairness, we review everything to keep your models legally sound and ethically responsible. You get a system that’s fully prepared to meet evolving regulations. |
| Security Vulnerability Assessment | Securing your AI models isn’t optional—it’s essential. In this step, we actively search for vulnerabilities that could expose your system to attacks. Burp Suite scans for weak points in data pipelines, model deployment, and API endpoints. This way, you get a resilient system against breaches and unauthorized access, ensuring data integrity and user trust. |
| Performance and Transparency Review | Your AI’s efficiency and decision-making process take the spotlight in this step. We use IBM Watson OpenScale to monitor key metrics such as latency and accuracy. We also check how clear your model’s decisions are, ensuring stakeholders can easily understand why your AI makes specific calls. This boosts trust and keeps everything transparent. |
Ready to slash errors and boost efficiency?
Inside Codewave's AI Audit ToolKit
We’ve seen error rates drop by 30% thanks to these audits. And the best part? save over 40% on operating costs–our toolkit gives your AI a serious upgrade!
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| Bias & Fairness Assessment |
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| Model Performance Evaluation |
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| Security & Vulnerability Testing |
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| Transparency & Explainability |
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Who We Serve
We’re all about big ideas and pushing boundaries.
With a fresh approach to innovation, we work with over 400+ businesses worldwide – from startups to governments. We’re here to transform the way AI works for your business.
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We transform companies!
Codewave is an award-winning company that transforms businesses by generating ideas, building products, and accelerating growth.
What to expect
What to expect working with us.
Coffee break? Let's talk about how AI auditing can future-proof your AI!
Frequently asked questions
AI auditing is the process of reviewing AI systems for accuracy, fairness, and compliance. It identifies performance gaps, biases, and security risks. The goal is to ensure AI models operate ethically and effectively. Regular audits help improve system reliability.
AI audits ensure models are free from biases and errors. They help businesses stay compliant with legal regulations. Audits also enhance transparency and accountability. This builds trust in AI-driven decisions across industries.
AI audits improve the accuracy and performance of AI models. They help reduce errors and minimize risks. Audits also ensure systems meet ethical and legal standards. This increases stakeholder confidence and boosts business outcomes.
AI systems should be audited whenever there are updates or new model deployments. Regular audits help maintain performance and security. They ensure models stay compliant with changing regulations. Routine checks keep AI systems reliable over time.
Codewave takes a comprehensive approach, analyzing model performance, biases, compliance, and even conducting a website audit AI to ensure end-to-end optimization. We use top tools and frameworks to identify vulnerabilities. Our team ensures your AI system is secure and delivers measurable improvements.
Codewave serves a wide range of industries, including healthcare, finance, retail, and more. Our AI audits help optimize model performance specific to each sector. We ensure compliance with industry regulations. Each audit is tailored to meet industry-specific needs.
Codewave uses powerful tools like IBM Watson OpenScale, TensorFlow Model Analysis, and Burp Suite. These tools help assess model behavior and detect biases. We also use DataRobot for performance testing and optimization. Our tech stack ensures comprehensive audits.
We use IBM Watson OpenScale to monitor AI decisions in real-time. This gives full visibility into model behavior and decision-making. It helps ensure AI systems are accountable. Our approach builds trust and transparency for all stakeholders.
Yes, we specialize in identifying and mitigating biases. Our audits examine training data, algorithms, and outcomes for fairness. We make sure your AI models are free from unfair discrimination. This helps ensure equity in AI-driven results.
Codewave ensures your AI models comply with regulations like GDPR and ISO standards. We assess legal risks and compliance gaps. Our team ensures your systems stay up-to-date with changing laws. This helps avoid legal issues and maintains trust.
Codewave combines cutting-edge tech with a human-centered approach. We go beyond generic audits to offer tailored, actionable insights. Our audits focus on measurable improvements and results. We help businesses get the most out of their AI systems.
Expect improved accuracy, reduced errors, and better performance. Clients see up to a 30% reduction in error rates. AI audits also lead to a 20% savings in operational costs. Our audits deliver real, measurable results that drive business growth.
