For Every Unique Requirement,
We Engineer the Path to a Solution.

SEM images present a difficult classification problem: defect classes are unevenly represented, labeled examples may be scarce, and scratches or particles can vary substantially in appearance. A conventional classifier may also force unfamiliar anomalies into known categories.

We implemented separate stages for anomaly localization, region analysis, defect-specific classification and unknown-defect flagging. Geometric features support scratch analysis, while different visual characteristics are used for foreign particles. Each stage used a method suited to the target defect instead of relying on one general-purpose classifier.

This architecture addresses limited training data and the need to flag anomalies outside the defined taxonomy. It gives inspection teams a clearer basis for reviewing known defects and unfamiliar anomalies.

Searching long-form video is difficult when visuals, speech, subtitles and sound provide complementary evidence. Program titles and flat tags rarely identify a particular remark or event, while fixed-duration cuts can split a meaningful scene or conversation.

We implemented a multimodal pipeline that combines video and audio information, segments footage around changes in scenes and topics, and generates descriptions, keywords and summaries for each segment. Relationships among people, organizations, places and events are structured for content-aware retrieval.

The implemented search returned relevant scenes rather than only matching media files. Organizing content around scenes and conversations helps teams locate relevant footage and evaluate it for reuse.

Used auto parts rarely have a single reference price. Value depends on vehicle compatibility, item condition, market demand, inventory age and marketplace fees. Competitor prices and sales velocity also change, making manual pricing rules difficult to maintain across many listings.

We implemented an item-level pricing workflow that combines product, inventory and market signals, evaluates candidate prices against margin and turnover, and applies channel-specific fee logic. Listing conditions, sales performance and synchronized stock data provide inputs for subsequent pricing reviews.

The implementation connected recommendations with observed sales and inventory outcomes. Rather than treating pricing as a one-time prediction, the approach structures a continuous cycle of market monitoring, reassessment and coordinated pricing decisions across channels.

Support calls contain symptoms, diagnostic steps, assumptions, exceptions and outcomes scattered across conversational turns. Repetition, digressions and ambiguous customer language make transcripts difficult to convert into reliable operational knowledge. Summarizing an entire call is not enough.

We implemented a staged process for speaker-aware transcription, cross-turn evidence extraction and KCS-style knowledge object creation. An ontology or knowledge graph links problems, conditions, possible causes, actions and results, while the analysis distinguishes tentative hypotheses from established facts and verified procedures.

New knowledge candidates are checked against existing material for duplication and relationships before enrichment. The focus is guidance that support teams can assess and reuse, with links to the evidence behind it.

Audience reactions during live broadcasts are fast-moving and expressed in short, colloquial Japanese posts. Unexpected scenes or remarks can trigger topics that predefined keywords miss, while overall mention counts conceal the timing and direction of changes in sentiment.

We implemented a time-aware analysis pipeline to collect and timestamp relevant posts, interpret Japanese-language sentiment in context, discover emerging topics and summarize how conversations shift. Results are structured for production, programming and marketing workflows rather than isolated dashboard statistics.

The approach combines language analysis with the timing of audience reactions. The pipeline surfaced interpretable audience signals as they evolved, while leaving editorial and commercial decisions to the responsible teams.

Security teams receive vulnerability advisories and threat reports from many sources, but severity rankings alone do not show which findings affect their own systems. Infrastructure context may also be confidential, limiting what can be sent to external AI services.

We implemented a pipeline for feed normalization, environment-aware relevance assessment and sensitivity-based AI routing. Approved external services process permitted information, while sensitive system context can be handled through a controlled LLM environment. Relevant findings are summarized and routed to existing communication tools.

The implementation connected external intelligence with the organization's systems through controlled processing paths. AI supports prioritization and explanation; security specialists remain responsible for validating findings and deciding how to respond.