Case Studies
A look at the kinds of AI problems we solve and how we approach them. Client details are anonymized to respect confidentiality; these illustrate our typical scope, method, and outcomes.
B2B SaaS
Challenge & approach: A growing SaaS company was overwhelmed by repetitive support tickets. We designed and deployed a retrieval-augmented assistant grounded in their documentation and ticket history, with human-in-the-loop review and continuous evaluation.
Outcome: Faster first responses, deflection of routine tickets, and happier support agents focused on complex cases.
Logistics & supply chain
Challenge & approach: A logistics operator needed more accurate demand and capacity forecasts. We built the data pipelines, trained and validated forecasting models, and shipped a monitored production service with automated retraining.
Outcome: More reliable forecasts feeding planning, with a transparent pipeline the in-house team can own and extend.
Fintech
Challenge & approach: A fintech team spent hours manually extracting data from documents. We built an ML pipeline combining OCR and LLM extraction with validation and audit trails, integrated into their existing workflow.
Outcome: Dramatically reduced manual effort with traceable, reviewable automated extraction.
Let's talk about your data and goals — we'll outline how we'd approach it.
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