Inspeq AI

Enterprise Platform for Responsible and Trustworthy AI Operations

Contact for Pricing

Description

Inspeq AI provides an enterprise platform dedicated to implementing Responsible AI (RAI) operations within business applications. It focuses on managing AI risks and ensuring governance, which are crucial for maximizing the return on investment and potential of agentic and generative AI across various business functions. The platform aids enterprises in harnessing AI safely and sustainably throughout the entire AI lifecycle, from design and development to deployment.

By utilizing Inspeq AI's solutions, organizations can achieve significant improvements in AI system performance, cost efficiency, and compliance. The platform helps detect misinformation and bias, validate model accuracy, measure fairness and transparency, and generate necessary audit reports for regulatory requirements like the EU AI Act. It supports integration with various LLMs and platforms, offers deployment flexibility including on-premise options, and aims to prevent reputational damage associated with irresponsible AI use.

Key Features

Pre-production Testing: Conducts robust testing before AI system deployment.
Post Go-Live Monitoring: Provides continuous monitoring of AI systems after launch.
Responsible AI Governance: Establishes frameworks for managing AI risks and ensuring compliance.
Misinformation & Bias Detection: Identifies and mitigates harmful content and biases in AI models.
Accuracy Validation: Validates the accuracy of inputs, outputs, and models.
Fairness & Transparency Measurement: Assesses AI systems against fairness and transparency metrics.
Regulatory Audit & Reporting: Generates reports to meet compliance requirements (e.g., EU AI Act).
Broad Integration: Integrates with platforms like Microsoft Co-Pilot Studio, Amazon Bedrock, Agentforce, and various LLMs.
Model Agnosticism: Supports open-source, private models, and agentic workflows.
On-Premise Deployment: Offers on-premise solutions for enhanced data security and privacy.
Cost-Effective Evaluation: Uses deterministic metrics and runs on CPU, avoiding costly LLM-as-a-judge methods and GPU requirements for on-premise.

Use Cases

Operationalising Responsible AI (RAI) in enterprise applications.
Managing AI risk and governance for generative and agentic AI.
Improving LLM performance and reliability.
Reducing AI hallucinations and inaccuracies.
Lowering AI development and operational costs.
Ensuring compliance with AI regulations (EU AI Act, SB-942, UK AI Principles).
Securing AI in Banking processes (e.g., Chargeback, Loans, Fraud Detection).
Securing AI in Insurance processes (e.g., Policy Renewals, Underwriting).
Securing AI in Telecoms processes (e.g., Traffic Management, Billing Systems).
Securing AI in Consumer Goods processes (e.g., Pricing, Promotions).
Enhancing chatbot accuracy and mitigating bias.

Frequently Asked Questions

How does Inspeq AI help businesses scale their AI systems?

Inspeq AI provides Responsible AI Operations (RAI Ops) tools to help businesses scale responsibly by monitoring AI performance, ensuring compliance, and managing risks throughout the AI development lifecycle.

What sets Inspeq AI apart from other AI governance platforms?

Key differentiators include operationalising RAI at the business process level, avoiding LLM-as-a-judge for cost-effectiveness and accuracy, CPU-based operation for on-premise versions, an enterprise-first focus with on-premise options, and support for agentic workflows.

Is Inspeq AI’s platform customizable for different industries?

Yes, the platform is designed to be flexible and customizable to meet the unique needs of various industries, including Banking, Insurance, Telecommunications, and Consumer Goods.

How does Inspeq AI ensure data security and privacy?

Inspeq AI employs state-of-the-art encryption, rigorous quality assurance, automated logging, regular audits, adheres to stringent data protection regulations, offers transparent privacy policies, and provides on-premise deployment options to keep data within the customer's system.