Xinan Intelligence Technology · 2026-10-05

Enterprise AI application brief

Connect models to real work: knowledge search, business assistants, multimodal apps and auditable automation.

Original AI-generated concept scene; not a real customer, device or project result.

Requirements and data

Define users, tasks, sensitivity and existing interfaces. Inventory authorized documents, access rules and retention. Establish a baseline with authorized real tasks, not demo questions alone.

System architecture

Separate UI, identity, retrieval, model gateway and tools. Cite retrieval sources and restrict tool permissions. Keep approvals and logs independent of model output; test injection and data leakage from untrusted documents.

Deliverables

Agree source or usage rights, deployment configuration, API docs, ingestion rules, test reports and runbooks. Document model providers, costs, offline limits, data flows and exit migration.

What we can develop

Enterprise RAG: ingestion, role-based access, source citations and retrieval evaluation.
Business agents: tools, approvals, human collaboration, exceptions and audits.
Multimodal applications: text, voice and image interfaces, content checks and model routing.
Integration with authorized ERP, CRM, ticketing and local-model interfaces.
Intelligent documents: OCR, classification, extraction and review.
Voice support, meeting notes and search using authorized media.
Assisted analytics and workflows for industry, logistics and public services.
Model gateways, API platforms, observability and private-cloud integration.

From requirements to handover

Requirements and authorization: define the problem, owners, data and interface rights.
Plan and baseline: agree scope, risks, budget, deliverables and acceptance.
Prototype and pilot: validate critical flows and recovery in controlled environments.
Integration and acceptance: review test evidence, not demonstrations alone.
Handover and maintenance: deliver docs, training, access, backups and iteration plans.

Scenario and solution studies

Concept studies illustrate design and acceptance, not completed customer projects.

Manufacturing support assistant

Project context: Manuals, tickets and fault records are fragmented.

Solution approach: Retrieve by product and permissions, draft cited replies and escalate uncertain answers.

Acceptance focus: Check citations, abstention, customer isolation and approved ticket updates.

Procurement review workflow

Project context: Many attachments require consistent risk review and approval.

Solution approach: Extract clauses against rules; draft recommendations without signing or legal conclusions.

Acceptance focus: Use authorized contracts to assess misses, false positives, versions and approval logs.

Hospital administrative scheduling

Project context: Administrators manage roster rules and staff changes, not diagnosis.

Solution approach: Draft rosters from authorized staffing data, check hours and conflicts, require owner approval.

Acceptance focus: Test rule coverage, conflict explanations, access isolation and edit logs.

Logistics exceptions and ticket orchestration

Project context: Fragmented events require detection of delays and missing information.

Solution approach: Use authorized events and rules/models to draft exception tickets without unauthorized shipment changes.

Acceptance focus: Check deduplication, false alarms, handling time and takeover.

Data and acceptance measures

Measures guide project testing. Except for cited industry definitions, they are not achieved Xinan results or performance promises.

Answer quality: score fixed task categories, not one aggregate accuracy claim.
End-to-end latency: report P50/P95 with model, load, network and cache conditions.
Cost per task: include inference, retrieval, infrastructure and human review.
Citation precision = reviewed correct citations / sampled citations; report uncited answers separately.
Escalation rate = escalated tasks / eligible tasks; separate refusals and system failures.
Data inventory: documents, rights coverage, update frequency, roles, concurrency and retention.

Sources and industry references

Third-party material is technical reference, not a partnership or endorsement. Checked: 2026-10-05.

NIST AI RMF / Generative AI Profile

Organize AI validation around risk identification, measurement and management; not a certification.

Microsoft GraphRAG

Public reference: open-source graph retrieval; evaluate locally rather than copying performance claims.

Before we start, tell us

Workflow, users, interfaces, data rights, deployment location and budget.

Delivery and usage boundaries

Models can err. Professional review is required for medical, legal, financial and critical decisions; scope, IP and service levels are contractual.