Transform your business with GenAI-infused apps.
Protect it with Quality Engineering.
With Generative AI becoming more accessible, organizations are quickly adopting the technology to deliver new services, conquer new markets and optimize business processes. But applications infused with GenAI carry unique challenges and risks. Before you deploy, you need specialized Quality Engineering from a team of experts — like ours.
We’ll turn tricky GenAI integrations into trustworthy experiences.
Large Language Models (LLMs), text-to-text applications and text-to-image apps are a complex amalgamation of multiple layers, which must all work together. Our rigorous testing and validation strategies will assure seamless integrations with your existing systems and mitigate risks:
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Reduce toxicity and hallucinations (false or fabricated data).
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Increase accuracy.
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Protect data privacy in training data.
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Remove regional cultural bias before global launches.
Proaktiver Markenschutz und Qualitätskontrolle, die nie aufhört.
We’ll optimize your business processes by assuring data sources, LLMs and seamless user interfaces. Our deep tech and domain expertise enables alignment with industry standards and your business goals.
Enhance data quality through a comprehensive strategy that applies stringent criteria to examine data from source through distribution checks and outlier review.
Our “red teaming” approach tricks the GenAI model so we can proactively identify toxicity, cultural bias and stereotypes.
Cut down release cycles with automated regression testing focused on end user experience, model performance and benchmarking.
Avoid cultural bias, hallucinations and toxicity anywhere you launch with continuous LLM validation in multiple languages, powered in real-time by Qualitest’s dynamic fact database.
Shift quality mindset left, right and center.
Deliver on the promise of GenAI with our comprehensive quality solutions. We’ll partner with you to build a customized Quality Engineering strategy for your GenAI-infused apps that leverages data quality assurance, model validation and customer experience testing.
Identify data quality issues early in the software development lifecycle (SDLC) by establishing standards from the start. Confirm data quality during transfer and transformation by leveraging our comprehensive data quality framework.
We’ll test your text-to-text or text-to-image applications for coverage, hallucinations, cultural bias, toxicity and accuracy of output, using prompts generated from various data sets and automating the output quality validation for time and cost savings.
We’ll compare your model output for metrics such as summarization, answer relevancy, hallucination, toxicity, bias and faithfulness, and we’ll implement continuous automation to benchmark KPIs against previous releases or industry models.
Applying an adversarial lens to your AI system, we simulate probes and attacks with prompt engineering specialists and a prompt database, identifying harmful or objectionable output.
We’ll test your GenAI-infused applications for global languages and regional context, ensuring your translations are regionally accurate and free from cultural bias.
EINE ERFOLGSGESCHICHTE
Continuous Automation of Data Quality Validations Cuts Effort by 35% for Leading US News Agency
01-Herausforderungen
The Client wanted to migrate to a digital asset management (DAM) system that was not based on meta data and enhance media search capabilities using AI-powered search. This new implementation opened up a new challenge, as it enabled users to search with Natural Language Processing (NLP) and the search results were not restricted to meta data tagging.
02-Lösungen
Qualitest proposed a solution to analyze all search strings in production, leverage the search criteria and create new prompts to test the new AI engine. The production data was further expanded with localized search priorities and parameters. The solution also included automation of the data pipeline in the cloud and E2E integration testing.
03-Ergebnisse
- 100% on-time completion of the program. Validation of the E2E cloud implementation of the application. 35% effort saved on Data Quality validations through continuous automation.
Womit können wir Ihnen sonst noch helfen?
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