01 / AI SERVICES
Three service paths.
One goal: AI in real tasks.
Services can run standalone or in sequence. First define what AI should accomplish this time, then organize models, compute and integration.
AI Integration & Application Adaptation
Embed model capability into existing business systems: knowledge Q&A, content generation, document processing and state judgment. Confirm interfaces and data conditions first, then define AI touchpoints and human review points.
- Requirement analysis and integration-point assessment (systems, data, workflows)
- Retrieval-augmented Q&A (RAG) configuration and tuning
- Agent workflow setup (task breakdown, node flow, human review)
- UI integration (web systems, mini programs, WeCom)
Applicability: existing system or clear business process, with test data available. No promises for effects unverified with real data; AI output is advisory, with human confirmation at key nodes.
AI Compute Selection
Select edge boxes, local GPU workstations or cloud compute based on model size, concurrency and latency. Check power, cooling and network conditions. No configuration beyond actual load.
- Load assessment (model size, concurrency, latency, budget)
- Device selection (edge box / GPU workstation / cloud API)
- Environment check (power, cooling, network, space)
- Acceptance testing (inference speed, stability, full-load run)
Applicability: clear target models and concurrent users. Device sources, brands and models are listed in the proposal; consumables and maintenance are not included by default.
Model Deployment & System Connection
Deploy selected models to the target environment and connect existing data interfaces and account systems. Local and cloud models route uniformly. Verify connectivity and quality before daily use.
- Deployment (local / private server / cloud)
- API integration (data interfaces, account system, permissions)
- Multi-model routing (unified scheduling, fallback strategy)
- Monitoring and logging (call records, anomaly alerts)
Applicability: model decided or selection advice accepted; data location confirmed. For sensitive data, confirm deployment location, access permissions and log scope first.
COMBINATIONS
How services combine
depends on where you start.
Three common combinations. Choose by current conditions; no need to build a full system at once.
Integrate first, then extend
Verify RAG Q&A with one department's documents, confirm answer quality and usage, then extend to workflows and more systems.
For: clear business scenariosCompute first, then connect
Select devices to run local models, verify speed and cost, then connect inference capability to business systems and UIs.
For: sensitive data, local-firstDeploy and connect directly
Model already decided: go straight to deployment and API integration. Run small-scale first, scale after stability.
For: model and stack readyQUESTIONS BEFORE WE BUILD
Let's be clear first.
Can we start with AI without an existing system?
Yes. First define the task and acceptance criteria, then decide whether software, equipment or engineering services are needed. You can start without historical data, but sample materials are needed for testing.
Can model quality be guaranteed?
No blanket promises. We verify with your real data first, and write achievable quality, failure cases and handling into the proposal. If verification fails, we say so honestly.
Must data go to the cloud?
Not necessarily. Choose local deployment, private servers or cloud APIs based on data sensitivity. For sensitive data, confirm deployment location, access permissions and log scope first.
How are pricing and timeline determined?
Confirm task scope, data volume, interface conditions and acceptance criteria first, then estimate development, deployment and testing. Technically uncertain parts get separate verification, quoted in the proposal.