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Calling a model API is simple; integrating AI into an enterprise estate is not. Requests must respect identity and data permissions, sensitive fields must be handled correctly, costs must be attributable, and applications should not break when a provider retires a model version or changes its pricing.
We build the integration layer that makes AI a dependable platform capability: model gateways, secure connectors to systems of record, and the monitoring that lets architecture and finance teams see how AI is actually being used.
商業應用
- 01
Enterprise model gateway
Provide a single, governed entry point to multiple model providers, with authentication, routing, fallback, caching, quota management and per-team cost attribution, so product teams can adopt AI without each building their own plumbing.
- 02
AI inside existing products
Add summarization, search, classification or recommendations to established applications through well-defined APIs and feature flags, without rewriting the core system or disrupting its release process.
- 03
Serving classical ML models
Package and serve trained models for scoring, forecasting or ranking behind versioned APIs, integrated with the transactional systems and event streams where their predictions are actually used.
- 04
Data protection in the request path
Detect and redact personal or confidential information before prompts leave your environment, enforce data residency rules, and log requests in a form that supports audit without retaining unnecessary content.
工程方法
- 01
Assess the integration landscape
Catalog the applications, data sources, identity providers and network constraints involved, along with the security, residency and compliance requirements each AI use must satisfy.
- 02
Design provider-independent interfaces
Define internal APIs and schemas that isolate applications from specific model vendors, so models can be swapped, compared or combined without changes to consuming services.
- 03
Implement secure connectors
Build connectors to systems of record with least-privilege service identities, permission-aware data access, managed secrets, and encryption in transit and at rest.
- 04
Add observability and controls
Instrument every call with tracing, latency, token usage and error metrics, and apply rate limits, budgets and content filters at the gateway rather than separately in each application.
- 05
Validate and roll out
Test integrations under realistic load and failure conditions, verify output quality with evaluation sets, and release progressively behind feature flags with documented rollback steps.
技術與平台
Model platforms
- Amazon Bedrock
- Azure OpenAI
- Google Vertex AI
- OpenAI API
- Anthropic API
- Hugging Face
Gateway and API management
- LiteLLM
- Kong
- Azure API Management
- Amazon API Gateway
- Envoy
Model serving
- MLflow
- KServe
- NVIDIA Triton Inference Server
- ONNX Runtime
- Amazon SageMaker
Security and observability
- OpenTelemetry
- HashiCorp Vault
- Microsoft Presidio
- OAuth 2.0 and OpenID Connect
- Datadog
所提及之技術名稱均為其各自所有者之商標，並不代表任何合作或背書關係。
您將獲得的成果
- Integration architecture and provider abstraction design
- Model gateway with routing, fallback, quotas and cost attribution
- Secure connectors to your applications and data sources
- PII detection and redaction in the request path
- Observability dashboards for usage, latency, cost and errors
- Integration test suite and progressive rollout plan
決策者關心的問題
Should we standardize on one model provider?
Standardizing simplifies procurement and operations, but different tasks often suit different models, and providers change pricing and availability. A gateway lets you set a sensible default while keeping the option to route specific workloads elsewhere. We help you decide how much flexibility is worth the additional operational overhead.
Can AI respect our existing access controls?
It must. We pass user identity through the integration layer and filter retrieved data according to the permissions in your source systems, so a model does not receive content the requesting user could not access directly. Access decisions are logged for audit, and we test these controls as part of acceptance.
How do we keep AI costs under control?
By making them visible and enforceable. The gateway attributes token usage to teams and applications, applies budgets and rate limits, caches repeated requests and routes simpler tasks to smaller models where evaluation shows quality holds. Shared cost dashboards give finance and engineering the same view of spend.