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Turning AI into an Asset—Not a Gamble

The hit or miss nature of LLMs make it difficult to budget projects around them and the additional burden on proofreaders and stakeholders leads to phantom ROI, where costs rise but measurable benefits remain unclear.

Let's Discuss Your AI Strategy

Discovery: Opportunities & Data

Before investing in AI tools, we map out areas where AI has a high probability of success and determine what data is required to support it

Where can AI provide error-checking without replacing human judgment adding sneaky mistakes?

Evaluate & structure existing data
to see what's involved in aggregating and indexing it.

Assess inefficiencies in validating your content and assets during production

Review security and compliance concerns Ensure your IT department, MSP, and any other stakeholders in those systems are included.

Index Data & Optimize Lossy Processes

Implement search first by connecting your databases, unstructured files, and ERP to a search engine or database with vector search capabilities useful to an LLM and standard full text search for everyone else.

Immediate Gains Finding what we need quicker improves productivity and starts the project on a positive note.

AI Readiness is an Asset. Curating a balanced dataset for future needs is an asset. Retroactively sifting through data and balancing the set is costly and difficult to budget for.

Optimize Processes If you're losing data you need. This could be as simple as saving in another file format. Retroactively scavenging for data leads to  unbalanced datasets favoring what's easy to find over what's necessary.

AI-Assisted Quality Assurance & Validation

Rather than trusting AI to generate content, this phase focuses on using AI to check human-created work and later on some generative content. This method reduces risk, ensuring AI supports rather than replaces human judgment. Every error it finds offers immediate value.

Deploy small, focused AI models for error detection, proofreading, and brand consistency checking.

Track where AI assistance improves efficiency vs. where it misses mistakes.

Train teams on AI strengths, weaknesses, and data biases so they can refine how AI interacts with company data.

Continue testing Retrieval-Augmented Generation (RAG) approaches and comparing base model performance using your data.

AI Maturity: Efficiency Gains & Oppportunity

By this phase, your organization will have a deeper understanding of AI, its strengths, and its limitations. More importantly, you’ll have a structured dataset and a team trained to work with AI effectively.

Reassess goals for AI, how the technology changed since the discovery stage and your experiences.

Explore options like training text embeddings, LoRAs or even fine tuning in addition to RAG.

Identify and formalize one-off automation tools created by power users to ensure they’re scalable and secure.

Experimental scripts can quickly become mission-critical without warning. Work with IT and/or a consultant to help with code reviews.

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Loremma Dolor

Chief Technology Officer, Notion