We solve hard problems.

Claude Sonnet delivered strong relevance, but responses were taking 4 to 5 seconds in production. That was too slow for the search experience and expensive to operate at scale.

We built an evaluation set from the client’s search traffic, trained a smaller model for that domain, compared relevance against Claude Sonnet, and designed a serving and fallback path for production.

• Relevance comparable to Claude Sonnet on the client’s evaluation set
• Latency reduced from 4 to 5 seconds to about 500 milliseconds
• Search made 8 to 10 times faster
• Approximately $250,000 in annual infrastructure savings

Unstructured inputs had to produce consistent routing and predictions, while uncertain cases still needed human judgment.

We defined the labels and decisions, built datasets and evaluations for the job, trained candidate models, calibrated confidence, and set thresholds for human review.

• More than 300 million items processed
• Approximately 30 milliseconds per inference
• Manual review reduced by more than 80%
• Approximately 50% revenue growth from ecommerce workflows using fine tuned language models

Teams were handling repetitive customer requests across disconnected tools. This created delays, inconsistent responses, and unnecessary manual work.

We built AI agents that connected existing systems, understood business context, and automated customer and commerce workflows.

• Customer and commerce workflows automated across existing systems
• Less manual work across commerce operations
• Faster and more consistent execution
• Approximately 30% increase in revenue through intelligent automation