How Data Privacy Accelerates Innovation
At Keywords Studios, we discovered something counterintuitive about AI adoption: security controls don't limit innovation - they enable it. Our journey to this insight began with a typical enterprise challenge.

Throughout 2024, our employees were experimenting with public AI tools like ChatGPT, but very cautiously and in line with our internal AI and security policies. Following our security protocols around data, they self-censored, limiting themselves to safe but low-value tasks. Real productivity gains remained out of reach.
This aligns with Gartner's analysis, which shows that by 2026, 70% of organizations adopting conversational AI will require responsible AI practices and techniques, up from less than 10% in 2023. Our experience demonstrates why: robust security controls don't just protect data - they unlock adoption.
Initially, we explored enterprise offerings from major providers, but through summer 2024, these programs were still maturing - lacking trial options and requiring minimum commitments of 500 seats. We faced a catch-22: we needed to prove business value to justify the enterprise investment, but we couldn't test real business use cases on unsecured free versions.
Our solution was to create a secure trial environment using AWS Bedrock (AWS) as our backend and LibreChat as our front end. This approach balanced security and flexibility while offloading key Machine Learning Operations (MLOps) responsibilities to AWS. Our teams could safely test AI internally for the first time - brainstorming business plans, analyzing proprietary code, exploring large data sets, summarizing meeting notes, and much more.
The impact was immediate. As trust in the system grew, innovation flourished. We now receive access requests from teams across the organization daily, all eager to explore how secure LLM access could improve job satisfaction and productivity.
Implementation Strategy
Step one was providing secure access to state-of-the-art foundation models through a chat interface, offering Llama 3.2, Claude 3.5 Sonnet, Mistral Large, and other leading models during the pilot. Despite broader adoption and experimentation, per-employee token usage remained lower than projected. We discovered the main cost driver wasn't AI usage but system maintenance. For an organization the size of Keywords, with maintenance costs spread over thousands of users, we can deliver secure chatbot services for less than $10 per user per month.
Step two focused on building naive RAG systems to leverage data and reduce hallucinations. While much of this work uses AWS, Keywords Studios is a multi-cloud organization, optimizing for each team and studio’s security needs.
Looking ahead, we're exploring advanced RAG architectures to improve answer quality, with GraphRAG as our next focus. We've also begun investigating LLM fine-tuning for specific domain use cases.
Rather than seeking a single solution, we're building expertise across platforms to confidently deploy the right tool for every job. Your choices will depend on your size and needs, but whether you choose Enterprise grade SaaS, or build something yourself, deploying a secure chatbot will unlock innovation that the free services just can't.