Building Enterprise-Grade AI You Can Actually Trust
The world of Artificial Intelligence is crowded with hype. How do you move beyond the buzzwords to build AI systems that are reliable, auditable, and deliver measurable value?
This is a challenge we take seriously, and it’s a perspective that was recently highlighted when our team was featured in the enterprise technology journal, diginomica. The article, “Time to get real about RAG and enterprise-grade AI,” explored what it takes to make AI work in the real world, moving past the initial excitement to tackle the hard problems of accuracy and trust.
Our approach, as shared with diginomica, begins with a principle that many companies overlook: a respect for user skepticism. We believe that trust in AI cannot be assumed; it must be earned through transparent design and consistent results. Acknowledging that technologies like Retrieval-Augmented Generation (RAG) are powerful, but not perfect, is the only honest starting point. As our own Greg Tull stated in the article, “RAG isn’t magic… Our job isn’t just to implement RAG; it’s to engineer reliable, task-specific results”.
Engineering that reliability takes hard work, plus a multi-layered strategy. We start with superior data management, because a RAG system can only be as accurate as the source material it relies on. From there, we build a framework designed for precision and accountability, incorporating three key components:
- Chunk Level Precision. We make sure the AI is drawing from the most relevant information by intelligently labeling and grouping datasets before they are presented to the model.
- Dialogue Memory (“Recall”). To provide accurate, context-aware answers, our system considers the entire conversation history before generating a new response.
- Strict RAG-Only Mode. To prevent inaccuracies, we can constrain the AI to only use information from the verified documents provided, forcing it to say “I don’t know” rather than hallucinate an answer.
Naturally, the most important component is human oversight. User training is a must, and our systems are built to provide citations for every answer, allowing you to hold the AI’s work accountable. The goal isn’t to replace human intelligence, but to augment it, helping your team get to the right answer faster.
If you’re ready to move from AI curiosity to enterprise-grade capability, let’s talk about what a realistic and reliable AI strategy looks like for your business. Discuss Your AI Strategy.
Industry Insight: Social Commerce is Blending Shopping with Entertainment
It’s impossible to ignore the power of social media. Recent data shows that 82% of shoppers report that viral trends directly influence their buying decisions. Success in this growing channel requires a new approach that blends entertainment with commerce. To win, brands are partnering with creators for authentic, relatable content and hosting live shopping events that create a sense of urgency and community. By making content instantly shoppable with tools such as integrated “buy” buttons, ecommerce leaders are removing the friction and turning social engagement into a simple path to purchase.
Partner Highlight
This month, we’re highlighting our partner, Amasty, a top developer of extensions for Adobe Commerce and Magento. Their robust modules are designed to solve common ecommerce challenges and add powerful new functionality to the platform. We recently used Amasty’s Out of Stock Notification extension for a client to resolve a frustrating issue where customers were receiving duplicate alerts. The Amasty solution provided a reliable fix, improving the customer experience and showcasing how the right partner can solve complex problems efficiently.