Build AI capabilities as modular, scalable APIs that integrate seamlessly across your entire technology stack. Maximum flexibility, minimal coupling.
Traditional approaches tightly couple AI models with applications, creating rigid systems that can't adapt to changing business needs:
AI logic embedded in applications can't be reused across different products, channels, or teams, leading to duplication.
Scaling AI workloads requires scaling entire applications, wasting resources and increasing costs unnecessarily.
Improving AI models requires redeploying entire applications, slowing iteration and creating deployment risk.
Connecting AI to new systems, channels, or partners requires custom integration for each use case.
We build AI capabilities as standalone, well-documented APIs that integrate anywhere, scale independently, and evolve without breaking clients.
We start with API design, defining clear contracts that meet current needs while allowing future flexibility. OpenAPI/Swagger specifications ensure documentation and client generation.
Each AI capability (NLP, computer vision, prediction) runs as an independent microservice with dedicated resources and deployment cycles.
Centralized gateway handles authentication, rate limiting, caching, and routing, providing consistent access patterns across all AI services.
Auto-generated SDKs in multiple languages (Python, JavaScript, Java, etc.) and comprehensive documentation accelerate integration.
Real-time monitoring of API performance, usage patterns, and model accuracy with automated alerting and optimization recommendations.
Explore our portfolio of scalable AI API platforms serving millions of requests.
AI APIs have unique requirements. Here are key principles we follow:
AI inference can be slow. We use async endpoints for long-running tasks, provide confidence scores to enable client-side caching, and implement webhook callbacks for batch processing. Response times are documented in SLAs.
ML models evolve continuously. We expose model versions in API endpoints (/v2/sentiment-analysis?model=v1.2.3) allowing clients to pin specific versions while we roll out improvements. Deprecation policies give clients time to upgrade.
AI predictions aren't binary. Every response includes confidence levels, alternative predictions, and model metadata. This allows clients to implement custom thresholds and fallback logic based on their risk tolerance.
Single-prediction APIs are inefficient for bulk processing. We provide batch endpoints that process multiple inputs in parallel, dramatically reducing overhead and cost for high-volume use cases.
AI services can fail. We design APIs to return cached results, rule-based fallbacks, or lower-accuracy alternatives when primary models are unavailable. Clients receive status indicators to adjust UX accordingly.
We build various types of AI APIs depending on use case requirements:
Synchronous endpoints for real-time predictions: classification, regression, recommendations. Optimized for low latency (sub-100ms typical).
POST /v1/predict {"model": "churn", "features": [...]}
Asynchronous endpoints for processing large datasets. Submit jobs, poll for status, retrieve results. Ideal for ETL workflows and periodic scoring.
POST /v1/jobs/batch-classify → GET /v1/jobs/{job_id}
Server-sent events or WebSockets for real-time processing: live sentiment analysis, anomaly detection on time-series data, progressive content generation.
WS /v1/stream/analyze → continuous JSON events
Endpoints for submitting corrections and feedback. Powers active learning pipelines that continuously improve model accuracy based on real-world usage.
POST /v1/feedback {"prediction_id": "...", "correction": "..."}
Administrative endpoints for deploying models, monitoring performance, and managing versions. Enables MLOps automation and CI/CD integration.
POST /v1/models/deploy {"model_id": "...", "environment": "prod"}
AI APIs are stateless, allowing unlimited horizontal scaling. We use Kubernetes auto-scaling based on CPU, memory, and request queue depth. New instances spin up in seconds to handle traffic spikes.
Identical requests to AI models waste resources. We implement Redis-based response caching with smart invalidation, CDN caching for public APIs, and client-side caching guidance via HTTP headers.
We use quantization, pruning, and knowledge distillation to create smaller, faster models without significant accuracy loss. ONNX Runtime and TensorRT acceleration reduce inference time by 3-10x.
For global applications, we deploy AI APIs across multiple regions with intelligent routing based on client location. Edge deployments reduce latency to under 50ms for most users worldwide.
We optimize infrastructure costs through spot instances for batch workloads, GPU sharing for multiple models, and automatic scaling down during low-traffic periods. Typical cost savings of 40-60% vs. naive deployments.
Multi-layered security with API keys for simple use cases, OAuth 2.0 for user-context operations, and mutual TLS for high-security integrations. Fine-grained permissions control access to specific models and features.
We ensure customer data isn't used for model training without explicit consent. Request/response encryption, temporary storage with automatic deletion, and optional on-premise deployment for sensitive data.
Token bucket algorithms prevent abuse, with configurable limits per client and endpoint. WAF integration blocks malicious traffic. Graceful degradation under attack to protect legitimate users.
Comprehensive logging of all API requests, predictions, and model versions used. Immutable audit trails for compliance with GDPR, HIPAA, and industry-specific regulations.
Average API response time for predictions
API uptime SLA with automatic failover
Requests per day handled by production systems
REST is simpler and better for public APIs with straightforward operations. GraphQL excels when clients need flexible queries across multiple AI models. We recommend REST for most AI use cases due to better caching and tooling.
We use semantic versioning in URLs (/v1/, /v2/) for breaking changes and model version parameters for non-breaking improvements. Clients can pin specific model versions while we maintain backward compatibility for at least 12 months.
Absolutely. With proper optimization (model quantization, edge deployment, caching), we achieve sub-50ms latencies suitable for real-time UIs, IoT devices, and interactive applications.
Typical models include per-request pricing, tiered subscriptions based on volume, and compute-based pricing for expensive operations. We help design pricing that aligns with your business model and customer value.
Complete OpenAPI specifications, interactive API explorers (Swagger UI), client SDK documentation, integration guides, and code examples in multiple languages. Documentation updates automatically as APIs evolve.
Create scalable, reusable AI capabilities that integrate anywhere. Schedule a consultation to discuss your API strategy.
Related: AI-Powered SaaS Development | Scaling Custom AI Solutions