此頁面尚未提供您所使用語言的版本。 經審校的譯文正在準備中，您目前瀏覽的是英文版本。
01 · 商業問題
Most enterprises are no longer asking whether to use AI. They are asking why the pilots they funded have not changed how work gets done. Proofs of concept live in isolated sandboxes, built on exported data and judged by enthusiasm rather than agreed criteria. When a pilot succeeds, there is rarely a path to production: no integration with systems of record, no owner for model behavior, and no agreement on who approves what the system is allowed to do.
Meanwhile, the risk surface grows. Employees adopt public AI tools on their own, sensitive documents leave controlled environments, and business units license overlapping platforms with incompatible security models. Leadership is left with fragmented spend, uneven quality and no consolidated view of where AI is actually creating value. The organization needs a deliberate operating model for AI: shared infrastructure, clear governance, and a portfolio of use cases engineered to production standards rather than demonstrated once and abandoned.
02 · 建議方案
iQsolutions engineers an enterprise AI foundation and then delivers use cases on top of it, one production release at a time. The foundation provides governed model access, retrieval over approved knowledge sources, evaluation tooling and observability, so each new application inherits security and quality controls instead of reinventing them. Use cases are selected for business impact and data readiness, not novelty.
Each application is embedded in the systems people already use, such as CRM, service desk, document management and ERP, and designed with explicit human checkpoints where judgment or accountability matters. The result is an AI capability the organization owns, understands and can extend, rather than a collection of disconnected experiments that depend on a single vendor.
03 · 系統元件
一條從 AI 實驗邁向正式系統的結構化路徑：以企業資料為基礎，融入實際工作流程，並從首次發布起即納入治理。
- 01
Model Gateway
A single governed entry point to commercial and open-weight models. It enforces authentication, routing, rate limits, redaction policies and cost attribution, so teams can change providers without rewriting applications or bypassing controls.
- 02
Enterprise Knowledge Layer
Retrieval pipelines that index approved documents, tickets, policies and records with their access permissions intact. Answers cite their sources, and content stays current through incremental ingestion rather than one-time exports.
- 03
Agent and Workflow Runtime
An orchestration layer where assistants and agents call business tools through defined, auditable interfaces. Actions that change records or commit the organization are routed through configurable human approval steps.
- 04
Evaluation and Observability
Test suites, reference datasets and production tracing that measure accuracy, groundedness, latency and cost for every release. Regressions surface before users see them, and behavior can be explained after the fact.
- 05
Governance Console
A shared register of models, use cases, owners, data sources and risk classifications. It records approvals and policy decisions, giving leadership, security and compliance teams a consistent basis for oversight.
04 · 工程方法
工程方法
- 01
Portfolio before platform
We start by mapping candidate use cases against value, feasibility and risk, then build only the platform capabilities the first releases require. Infrastructure grows in step with demand rather than ahead of it.
- 02
Evaluation as a contract
Every use case gets agreed acceptance criteria and an evaluation set before development begins. Model choices, prompts and retrieval strategies are compared against that contract, not against impressions from a demo.
- 03
Provider independence
Applications depend on internal interfaces, not on a specific model vendor. This keeps options open as model capabilities, pricing and data-residency requirements change over the life of the program.
- 04
Adoption by design
We design AI features inside existing workflows and prepare the people who will own them. Feedback from users flows directly into evaluation sets, so the system improves from real usage.
05 · 整合全景
- Microsoft Entra ID / Okta (SSO)
- Azure OpenAI Service / Amazon Bedrock / Google Vertex AI
- Anthropic and OpenAI APIs
- SharePoint / Confluence / Google Drive
- Salesforce / ServiceNow
- Snowflake / Databricks
- pgvector / Pinecone / OpenSearch vector search
- Model Context Protocol (MCP) tool servers
- OpenTelemetry tracing
06 · 資安考量
- Prompts, retrieved context and outputs are screened for personal and confidential data, with redaction applied before any content reaches an external model provider.
- Retrieval honors source-system permissions, so users only receive answers drawn from documents they are already authorized to read.
- Agent tool access follows least privilege, with write actions scoped, logged and gated by human approval wherever the business requires it.
- Model inputs and outputs are retained in tamper-evident audit logs under retention policies set by the client's legal and compliance teams.
- Adversarial testing for prompt injection, data exfiltration and jailbreak attempts is part of every release rather than a one-time assessment.
07 · 交付階段
- 1
Assess
Inventory current AI activity, data sources and constraints, and prioritize a use-case portfolio with accountable business owners.
- 2
Architect
Define the reference architecture, governance model, evaluation approach and security controls for the shared foundation.
- 3
Build the foundation
Stand up the model gateway, knowledge layer and observability that the first use cases need.
- 4
Deliver use cases
Engineer, evaluate and release prioritized applications into production workflows with named owners.
- 5
Scale and govern
Extend the portfolio, refine evaluation sets from live usage and transfer operational ownership to internal teams.
08 · 商業價值
商業價值
Pilots that reach production
A shared foundation and clear acceptance criteria give promising experiments a defined route into daily operations, instead of stalling once the initial demonstration is over.
Controlled exposure
Centralized access, redaction and audit trails reduce the likelihood that sensitive information leaves governed environments, while still giving employees capable AI tools for their actual work.
Consolidated investment
Common infrastructure replaces overlapping tools and one-off integrations, so spending on AI accumulates into reusable capability rather than a series of isolated projects that each carry their own maintenance burden.
Lasting internal capability
Teams inherit documented architecture, evaluation practices and operating procedures, allowing the organization to extend AI into new domains on its own terms, without returning to an outside partner for every change.