Enterprise AI
Knowledge bases, internal assistants, agent workflows, document automation, and business analysis tools.
Xingqi Technology helps companies move from scattered AI experiments to working systems: scoped use cases, clean knowledge sources, agent workflows, system integration, acceptance tests, and ongoing review.
Most AI projects fail when they stay outside daily work. We design around source data, access control, review steps, handoff rules, and the metrics your team can actually track.
Knowledge bases, internal assistants, agent workflows, document automation, and business analysis tools.
SKU content, campaign scripts, customer service answers, review analysis, and operating dashboards.
Localization workflows, multilingual FAQ, platform rules, compliance knowledge, and global support.
Evidence-based product status checks using images, codes, batches, channels, prices, and logistics records.
The goal is not to ship a demo. The goal is to make the first use case reliable enough for real teams to use and improve.
Map tasks, roles, inputs, exceptions, and the cost of current manual work.
Review documents, systems, permissions, and source quality before building.
Define RAG, agents, tools, review steps, integrations, and acceptance tests.
Use real samples to test accuracy, control, handoff, and user experience.
Connect the workflow, configure logs, access control, and fallback handling.
Track usage, errors, handoff rate, response time, and business outcomes.

Turn manuals, proposals, service tickets, and training notes into a source-backed assistant for sales and support.

Use SKU data, campaign plans, and support records to produce consistent listing content and customer replies.

Build terminology, local content rules, platform templates, and multilingual support knowledge for global teams.
Share your industry, team size, systems, and the manual work that slows you down.