Integrate AI into your actual business processes, rather than just building a chatbot demo. We build small tools and automated workflows that run long-term and are genuinely useful to your colleagues — writing code, scraping data, organizing reports, batch-processing documents, and adding model capabilities to internal systems.
We handle the entire process from requirement analysis and technology selection to development, deployment, and ongoing maintenance. Our responsibility doesn’t end at delivery; we also manage issues that arise after the program goes live.
Applicable Scenarios
Document and Content Batch Processing
Extract key information from hundreds of contracts, quotations, invoices, and inspection reports for system entry. Using models for structured extraction is much faster than manual review and more adaptable to format changes than pure regular expressions.
Internal Knowledge Base Q&A
Create a searchable knowledge base from product documentation, operation manuals, and historical tickets so new employees don’t have to ask questions repeatedly. Based on Retrieval-Augmented Generation (RAG), answers include source citations.
Automated Scripts and Ops Tools
Batch reconciliation, log analysis, initial monitoring alert filtering, scheduled task orchestration, and natural language log queries. We use these same tools to manage our own servers.
Data Collection and Cleaning
Compliant collection and cleaning of public data requiring login, pagination, or handling anti-scraping measures. We strictly adhere to compliance and explicitly avoid data that requires bypassing authorization.
Customer Service and Sales Assistance
Provide real-time reply suggestions for customer service agents, compile customer backgrounds and follow-up recommendations for sales teams, and automatically route repetitive inquiries.
Mini-Program and Lightweight App Backends
API encapsulation for AI capabilities, rate limiting and billing, authentication, and integration with existing systems.
Technical Capabilities
| Direction | Specific Capabilities |
|---|---|
| Languages & Runtime | Python (primary), Node.js / TypeScript, Go; script-based requirements can be delivered directly |
| Model Integration | Mainstream domestic and international APIs and locally deployed models (vLLM / Ollama, etc.); multi-model routing, cost control, and fallback strategies |
| Retrieval-Augmented Generation | Document chunking strategies, vector databases (Milvus / Qdrant / pgvector, etc.), re-ranking, citation tracing, and hybrid search |
| Agents & Workflows | Task decomposition, tool calling, multi-turn planning, failure retry mechanisms, and human-in-the-loop confirmation nodes |
| Data Processing | Structured extraction, cleaning, deduplication, format conversion, and batch text processing |
| System Integration | Integration with existing systems (databases, APIs, enterprise internal systems); Webhooks, scheduled tasks, message queues |
| Deployment & Ops | Dockerization, CI/CD, logging and monitoring, cost and latency optimization, gray releases, and rollbacks |
Development Process
- Requirement Analysis (Free)
We first understand how the task is currently performed, how much time it takes daily, and the cost of errors. Many requirements discovered at this stage turn out to not need AI at all — a simple script suffices — and we will tell you directly.
- Solution & Prototype
We provide a technical solution, data flow, cost estimate, and timeline. Key steps are validated with small prototypes using real data to avoid discovering the wrong approach after full completion.
- Development & Self-Testing
Developed module by module with clear acceptance criteria for each. Code, configurations, and documentation are delivered together; we do not deliver “black boxes” only the author can maintain.
- Deployment & Trial Run
Deployed to your environment or assisted by us; monitoring and cost alerts are set up; a limited trial run is conducted before full rollout.
- Handover & Training
Documentation clearly covers architecture, deployment methods, how to add features, and how to troubleshoot common issues. Training continues until your team can take over independently.
- Ongoing Maintenance
Model upgrades, dependency updates, API changes, performance tuning, and fault handling. These can be included in a maintenance contract or billed per incident.
Pricing
| Type | Billing Method | Typical Range |
|---|---|---|
| Small Tools / Automated Scripts | Per Project | 3,000 – 12,000 CNY |
| Knowledge Base Q&A / Document Processing Systems | Per Project | 12,000 – 50,000 CNY |
| Business System Integration / Backend Services | Per Project | Evaluated based on workload |
| Ongoing Maintenance & Iteration | Monthly | 2,000 – 8,000 CNY / month (depending on complexity) |
| Technical Consulting | Per Day | 2,000 – 4,000 CNY / day |
Additionally, there is a model invocation fee (pay-as-you-go, charged by the model provider). An estimated monthly usage volume and cost magnitude will be provided during the solution phase, and cost alerts will be built into the program to prevent situations where “it works but the bill is shocking.”
Post-Launch Maintenance
Model and Dependency Upgrades
Model versions update frequently, and API behaviors may change. We monitor invocation anomalies; if issues arise, we rollback to a stable version first, then evaluate the upgrade.
Cost and Latency Monitoring
Thresholds and alerts are set for call volume, token consumption, request latency, and failure rates. Abnormal usage is detected immediately, rather than waiting for the end-of-month bill.
Performance Tuning
Prompts, retrieval strategies, and model combinations can be continuously optimized. Most projects still have significant room for improvement after launch.
Fault Response
Incidents affecting business operations are handled within agreed response times; purely internal tools are scheduled on a per-case basis.
What to Do and What to Avoid
| Situation | Recommendation | Reason |
|---|---|---|
| Daily repetitive rule-based work | Worth doing, and likely doesn’t need LLMs | If a script can solve it, adding a model introduces unnecessary uncertainty |
| Extracting unstructured data into formats | Worth doing | Models offer the best cost-performance ratio for such tasks |
| Quickly locating information in large document sets | Worth doing | RAG is currently the most practical application direction |
| Having AI make decisions directly | Not recommended | Errors are uncontrollable with no safety net. The viable approach is “AI suggests, humans decide” |
| Very small data volume and vague requirements | Wait | Streamline the process first. When processes are unclear, AI only amplifies chaos |
| Expecting “install a plugin and it’s done” | Unrealistic | Successful AI projects require a certain degree of custom development |
FAQ
How is data security handled?
Sensitive data can stay entirely within your server — using locally deployed open-source models or model services supporting private deployment. When external APIs are needed, data flow is explained upfront. Deployment can be on our servers or yours; data does not fall into third-party hands.
Will it become obsolete quickly?
Yes, which is exactly why we offer “ongoing maintenance” rather than just delivering and walking away. The model layer is abstracted and replaceable, while the business layer and your data processing logic remain relatively stable. In practice, a single program lasting two to three years with multiple upgrades is the norm, not an exception.
Do you build products?
We do not develop or promote products for end-users. We build internal enterprise tools, automated workflows, and system integrations — solving specific problems within your own business.
Can I see a prototype first?
Yes, and we recommend it. Run a key process with your real data to validate feasibility before committing resources. Costs are typically around 3,000 CNY.
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