Pain: you have a production system with hundreds of thousands of lines of code, dozens of integrations, and clients accustomed to the current API. Adding artificial intelligence into a working product is a non-trivial task. Any change risks breaking compatibility or introducing delays. Over 7 years, we have completed more than 10 integrations — from CRM to ERP — and none caused downtime.
Problems We Solve
Preserving Backward Compatibility
Any change in a working system risks breaking client integrations. We design the AI layer so that if AI fails, it does not affect the core. We use the sidecar pattern: the AI service runs alongside, without touching the main logic. An AI gateway routes requests to AI services.Wikipedia, Sidecar pattern
Latency and Timeouts
An LLM request can take 2–10 seconds. For real-time systems, this is critical. We use background processing, asynchronous queues, and fallback responses. Timeouts are configured at the API gateway level — typically 3 seconds for LLM.
Data Security
The AI service receives only the minimum necessary fields (principle of least privilege). All requests are logged and audited. Authorization is via OAuth2 client credentials.
How It Works: A Detailed Case Study
AI Integration in E-commerce
From our practice: for a client, an e-commerce platform (catalog of 50,000 products), the requirement was to add AI-generated descriptions for items without content. We chose the sidecar pattern with API augmentation.
Stack: FastAPI + LangChain + OpenAI GPT-4o. The container was deployed alongside the main API. A proxy layer intercepts GET /products/:id: if description is absent, it calls the AI service, caches the result in Redis for 24 hours. Response time with AI — 1.2 seconds (p99). Without AI — 50 ms. The client does not notice the difference thanks to asynchronous update on first request.
Result: 80% of items received descriptions within a week, conversion in those categories increased by 15%. Content manager time savings were 30%, and the content budget savings exceeded 40%. No rollbacks occurred. ROI was achieved in 4 months.
Scope of Work and Process
- Architecture analysis and documentation of integration points.
- Design of the AI layer (sidecar/plugin/batch).
- Implementation with tests (unit, integration, chaos engineering).
- Monitoring (latency, token consumption, error rates).
- Documentation for your team and training.
Stages:
- Analytics (3–7 days): code review, data schemas, load testing.
- Design (2–5 days): pattern selection, API specification, prototype.
- Implementation (2–4 weeks): AI service development, integration, tests.
- Testing (1 week): load testing, A/B tests, regression.
- Deployment and monitoring (3–5 days): rolling update, dashboards, alerts.
Timelines and Typical Scenarios
| System Type | AI Features | Pattern | Complexity | Comparison with Monolith |
|---|---|---|---|---|
| CRM | Lead scoring, email drafting, churn prediction | API / Webhook | Low | Sidecar is 2× faster |
| E-commerce | Product descriptions, search, recommendations | Plugin / Sidecar | Medium | Sidecar reduces time by 60% |
| Helpdesk | Answer suggestions, categorization | Webhook / Plugin | Low | Plugin integrates without core changes |
| ERP | Demand forecasting, anomaly detection | Batch / API | High | Batch does not affect latency |
| CMS | SEO optimization, content suggestions | Plugin / API | Medium | Plugin updates independently |
Approximate timelines: 3 to 8 weeks. Simple integrations (adding an LLM endpoint) — 2–3 weeks. Deep integration with an ML model — 6–10 weeks. Cost is calculated individually after an audit.
Why the Sidecar Pattern Is the Optimal Solution?
Sidecar reduces integration time by 60% compared to embedding AI directly into the monolith. You get an isolated service that can be updated independently.
Comparison: Sidecar vs. Embedding
| Characteristic | Sidecar | Embedding in Monolith |
|---|---|---|
| Integration time | 2–4 weeks | 6–10 weeks |
| Impact on core | Minimal | High |
| Rollback capability | Instantaneous | Complex |
| Update isolation | Yes | No |
How We Guarantee System Stability
All changes go through CI/CD with automated tests and load scenarios. We use the principle of graceful degradation: if the AI service is unavailable, the system continues to work normally, and AI functionality is simply unavailable. We version AI responses: we log the model version — if there is a regression, automatic rollback.
For batch processing (if AI is not needed in real time), we use RabbitMQ or Kafka queues. Data is read in batches of 1000 records, the AI model processes them sequentially, and the result is written to the database. Monitoring includes the number of processed records and errors.
Trust and Results
Our team has 7+ years of experience in production AI, with more than 10 completed integrations. We guarantee the stability of your system. The total cost of ownership of an AI solution is reduced by 25% thanks to inference optimization.
Estimate the possibilities for your project — request an audit in 2 days. Get a consultation to discuss the details of a turnkey integration. Contact us to discuss your tasks.