Real Client Challenges,
and How We Actually Solved Them.

In telecom technical support, customers may describe equipment or network problems only in vague terms. Operators must ask diagnostic questions and follow equipment-specific procedures, while unusual cases often depend on knowledge held by a small group of experienced staff.

Technica AI developed a process-aware system combining standard operating procedures (SOPs), conversation context, technical documents, and knowledge from past calls. Routine tasks can be automated within approved limits, while complex situations are handed to operators. Experienced agents’ troubleshooting and exception-handling practices are organized for review and reuse.

After approximately a year of joint development and evaluation, the project estimated a potential 40–45% reduction in overall contact center workload. This is a projected benefit, not a measured production outcome. The approach provides a foundation for scaling support through structured knowledge and controlled automation.

A major electronics manufacturer handles more than 10,000 components. Requests for replacements for competitors’ products accounted for over 13% of inquiries, but evaluating alternatives required manual research across catalogs and datasheets. Slow responses could weaken an important sales opportunity.

Technica AI structured product specifications into comparable fields and built a system to shortlist potential replacements from competitor part numbers or technical requirements. Matching attributes and differences appear together, allowing specialists to verify application fit instead of starting each search from scratch.

A proof of concept covered four categories and approximately 1,000 products. In tested knowledge-creation tasks, preparation time fell from roughly 90 minutes to 10–20 minutes. These findings show improved workflow efficiency in the PoC; they do not establish a measured increase in sales conversion.

An auto recycler purchasing more than 20,000 used vehicles annually must decide whether each vehicle is best resold, dismantled for parts, or recycled as material. The right purchase price changes with vehicle condition, parts demand, and market prices, making timely, consistent appraisals difficult.

Technica AI developed a market-informed appraisal system that estimates value across these options and considers processing costs and profit criteria. It tracks daily market information for approximately 200 vehicle models and 140 types of parts, supporting both purchase decisions and downstream parts planning.

Following deployment, the client reported approximately 30% growth in parts production without purchasing more vehicles. The reported increase was in production volume; profit improvement was not quantified. The system also provided a shared basis for coordinating purchasing, dismantling, and sales around expected value.

A teacher-placement agency needed to identify suitable candidates from more than 50,000 registrations. Records included missing values, outdated preferences, inconsistent terminology, and free-text descriptions. Conventional filters could not fully account for experience and fit, leaving selection heavily dependent on individual coordinators.

Technica AI made existing candidate information easier to compare without replacing the agency’s operating system. Shared criteria distinguished mandatory qualifications from experience, working conditions and preferences, giving coordinators a consistent basis for preparing and reviewing shortlists.

The project reported accurate evaluations in approximately 90% of test cases after separating information extraction from rule-based scoring. The metric and sample size were not specified; this is not a measured placement-success rate. The approach makes existing records more useful and provides more consistent shortlists.

A semiconductor equipment manufacturer needed to inspect arrays of microscopic holes for blockages, missing features, diameter, and position, alongside surface irregularities. Visual assessments varied between inspectors, and completely blocked holes could be difficult to detect when no visible opening remained.

Technica AI developed visual inspection outputs showing the location and type of candidate hole anomalies, alongside heat maps of relative surface patterns. These gave inspectors shared evidence for discussing quality. The heat maps do not measure actual three-dimensional surface height or replace calibrated metrology.

In a proof of concept using scanning electron microscope (SEM) images, the team demonstrated identification and visualization of several hole anomalies with only 12 training images available. These findings establish initial feasibility, not production-level inspection accuracy. Further validation with representative images and tolerances is needed before deployment.

A major broadcaster held an archive of tens of thousands of programs spanning decades, but older footage often had only titles and broadcast dates. Finding specific scenes therefore depended on staff memory and manual viewing, limiting discovery and reuse.

Technica AI evaluated video, audio, and text analysis to generate keywords, scene descriptions, summaries, and representative images. A proof of concept covered five programs broadcast from 1959 to 1996 across several genres, examining whether content-level metadata could support searches beyond existing catalog fields.

Keyword accuracy in the selected tests ranged from 75.6% to 96.6%; a separate documentary evaluation matched 400 of 429 keywords, or 93.24%. Results varied across metadata tasks and are not archive-wide performance figures. The findings support further evaluation of content discovery and potential licensing workflows.