LLM Integration & Orchestration
Calling an API is the easy part. Building the retrieval pipeline, structured outputs, error handling, and operational layer around it is the actual work.
What production LLM integration requires
A prototype that calls GPT and prints the result is not a production system. Production means structured prompts, retrieval pipelines, output parsing, retry logic, fallback behavior, cost controls, and monitoring — all built into your existing product architecture.
Retrieval-Augmented Generation (RAG)
Ground model responses in your data. We build embedding pipelines, vector storage, retrieval strategies, source attribution, and freshness management so outputs are defensible and current.
Orchestration & chaining
Complex AI features often require multiple model calls, conditional logic, tool use, and state management. We build orchestration layers that handle sequencing, error propagation, and timeout behavior.
Structured outputs & parsing
LLMs produce text. Your system needs structured data. We implement schema enforcement, validation layers, and fallback parsing so downstream code gets predictable inputs regardless of model variability.
Provider management
Model selection, version pinning, fallback providers, cost routing, and rate limit handling. We abstract the provider layer so you're not locked to one vendor or one model version.
Operational requirements we build in
- Token usage tracking and cost attribution per feature, tenant, and request
- Latency monitoring with alerting on degradation or timeout spikes
- Request/response logging for debugging, auditing, and quality evaluation
- Retry and fallback logic for transient provider failures
- Caching strategies to reduce cost and latency for repeated or similar queries
Need to integrate LLMs into a production system?
We can scope the retrieval pipeline, build the orchestration layer, and ship the integration with the monitoring and guardrails it needs.