Ensure Generative AI reliability
Bid farewell to inaccuracies in AI outputs – empower your Generative AI/LLMs (Chat-GPT-like models) to provide guidance for your teams based on up-to-date, contextualized, and reliable information.
AI isn’t perfect
The race to implement Generative AI (GenAI) put executives under immense pressure to deliver immediate and tangible results. Feeling the heat, most companies are looking for quick wins, but over 40 percent of AI initiatives are stalling in the scaling phase.
To create long-term results, executives need to factor in the limitations of AI and create a solid foundation for their AI initiatives, ensuring accuracy and reliability.
of business users report inaccuracy as a concern (#1 GenAI-related risk)
The cost of training a large language model such as GPT-3
Even AI struggles with deteriorated data
Many employees have already embraced GenAI at work, but models like ChatGPT lack insight into a company’s unique knowledge. Even if the AI system is based on internal content, relying on uncurated historical information leaves it out of touch with current realities (e.g. new market needs, strategies, regulations…) and creates issues with reliability and accuracy.
To adopt a dynamic approach and take your AI game to the next level, it is essential to ensure six dimensions of data quality: accuracy, completeness, consistency, timeliness, integrity, and validity. This means addressing the root cause of the issue and maintaining a clutter-free know-how base.
Methods such as prompt engineering, fine-tuning, and reinforcement learning claim to enhance outputs but are often slow, unstable, and costly. Even popular methods such as retrieval augmented generation (RAG) require accurate and reliable input for success.
Why qibri for AI reliability?
The quality of your LLM outputs can only be as good as the quality of your data, regardless of how advanced the technology behind it is. We believe that when managed well, know-how content (e.g. guidelines, SOPs, best practices, and how-tos) can serve as guardrails for data accuracy and GenAI initiatives, rather than another obstacle. qibri can be combined with advanced techniques like RAG, ensuring precision, data reliability, and transparency while minimizing implementation cost and time.
* While the methodologies mentioned above can be combined to improve results, in this table individual application for a company-specific setting is compared.
Empower LLMs for precise guidance
qibri is a quality-assured and dynamic platform for know-how management focusing on application knowledge(e.g. guidelines, SOPs, best practices, and templates.) It serves as a robust data source for LLMs, providing reliable information and facilitating sustainable updates.
Extract and structure content
We start by taking inventory of all know-how content, assess and structure them to create a single source of truth for employees and LLMs.
- Each document reviewed for validity and timeliness
- Duplicated and outdated content removed
- 30-50% of redundant content pieces removed
Set up a structured and validated platform
Once we have decluttered your content and extracted essential know-how, we migrate to qibri, a structured and controlled platform designed for managing and continuously improving know-how.
- A single space for each guideline/best practice
- Clear accountability with a single owner per space
- Decentralized and self-organized system
Provide context for LLMs and teams
We work closely with your key stakeholders to understand your organization’s unique needs and create a multidimensional structure to better contextualize existing know-how.
- Each space and document is labeled with metadata
- Personalized home screen and contextualized results for every employee
- Embedded in your system landscape
Sustain collaboration and regular updates
We offer back-office functionality to ensure your content is always up-to-date and provide a space for frictionless collaboration to improve know-how content.
- Version control for each document
- Dynamic alignment and approval processes
- Ongoing clean-up & harmonization
Data governance
Break free from the black box AI approach. Gain precise control over the data feeding into your LLMs, ensuring transparency.
Cost-efficiency
Minimize the financial burden associated with training and optimizing LLMs by solving AI reliability issue at the source.
Enhanced productivity
Identify synergies, harmonize processes, and standardize information flow across your organization.
