Case Studies & Industries

A specialized research and consulting firm with roughly 60 employees was producing strong work, but revenue per employee had been declining steadily. Analysts were spending too much time finding and processing information — the synthesis and narrative work that clients paid for was getting compressed. Operations had grown to support workflows that were manual by default rather than by necessity.

Aegion conducted a five-day on-site assessment, then restructured the firm's data infrastructure, deployed LLM pipelines for research intake and draft generation, automated expense classification and invoice processing, and templatized proposal generation. Eight weeks in, research turnaround was cut roughly in half, average project length compressed by a third, client satisfaction scores rose 20%, and two open roles were absorbed by the systems we built. Revenue per consultant rose an estimated 20%.

An educational technology company building personalized test preparation for competitive examinations in India had invested in AI-powered question generation. Their approach — a single complex prompt covering over a dozen question formats — produced output that was wrong roughly one in four times. The subject matter experts lost trust and reverted to manual production at $5 per question. The AI investment was effectively abandoned.

Aegion replaced the monolithic prompt architecture with dedicated prompts per question template, then developed an ensemble verification method: every AI-generated question is independently answered by multiple LLMs, and only full consensus passes to the review queue. Error rates dropped from 27% to under 5%. Cost per student-ready question fell to less than $0.50 — a greater than 90% reduction. The company launched a product that its previous cost structure could not support.