AI & Custom LLM Integration

We integrate AI where it removes real manual effort inside a system we've already built, rather than as a bolted-on chat widget. In our webinar distribution pipeline, AI identifies the clips and quotes worth publishing from a full recording. In our e-commerce ERP work, AI surfaces which product updates and listings actually need attention across dozens of SKUs. The goal is always a smaller, well-defined task the model can do reliably, wired into a workflow a team already depends on.

What's included

• AI-assisted content extraction (clip and quote identification from long-form video)
• AI-assisted product and catalog recommendations
• Custom LLM integration into existing products and workflows
• Retrieval and summarization over proprietary data
• Model evaluation and reliability checks before a feature ships
• Cost and latency-aware AI feature design

How we approach this

1. 1

   Find the narrow job, not the broad vision

   We look for one specific, well-defined task inside an existing workflow, not a general-purpose AI feature bolted onto the side of a product.

2. 2

   Prototype against real data early

   We test the model against actual production-like inputs from week one, since demo data hides the failure modes that matter in practice.

3. 3

   Build an evaluation set before shipping

   We create a labeled set of real examples that has to keep passing as we change prompts or retrieval, so regressions get caught before a customer finds them.

4. 4

   Design for cost, latency, and graceful failure

   Which model handles which query, when to fall back to a human, and what happens when the model isn’t confident are explicit product decisions, not afterthoughts.

Common questions

See it in practice