AI features that work reliably in production — not just in the demo.
Integrating a large language model into a product is straightforward. Making that integration reliable, cost-controlled, accurate, and maintainable in production is the hard part. Most LLM integrations fail in one of four ways: hallucinations that erode user trust, latency spikes that break UX, cost spirals that make the feature uneconomical, or no evaluation pipeline that catches quality regressions after a model update. We design LLM integrations with all four failure modes addressed upfront — retrieval-augmented generation to ground responses in your actual data, structured output schemas to constrain model behaviour, token budgets and caching to control cost, and evaluation harnesses to monitor quality over time.
What's included
- Retrieval-augmented generation (RAG) architecture
- OpenAI, Anthropic & open-source model integration
- Structured output & JSON schema enforcement
- Token budget management & prompt caching
- LLM evaluation pipeline & regression testing
- Fallback chains & graceful degradation
How we deliver
- 1AI feasibility & use-case assessment
- 2RAG architecture design & vector store setup
- 3LLM integration with structured output layer
- 4Prompt engineering & system prompt documentation
- 5Evaluation harness with test suite
- 6Cost monitoring & usage dashboard
Technologies we use
- OpenAI API
- Anthropic Claude
- LangChain
- LlamaIndex
- Pinecone
- Weaviate
- pgvector
- Redis
- AWS Bedrock
- Azure OpenAI
- Vercel AI SDK
Why Origin for LLM Integration & RAG Development
Failure modes designed before the happy path
We specify what happens when the LLM is unavailable, when confidence is too low, and when the response fails schema validation — before writing the success path. Production AI needs all three.
Evaluation harness as a non-negotiable deliverable
Every LLM integration we ship includes an automated eval suite. Model updates shouldn't surprise your users — they should trigger a test run that catches regressions before deployment.
Cost attribution on every LLM call
We instrument token usage by feature, user cohort, and prompt type. You always know what each AI feature costs to run — and which users are disproportionately expensive to serve.
Industries we serve
“Our first attempt at an AI feature used GPT-4 with no guardrails. Users caught hallucinations within a week and trust collapsed. Origin rebuilt it with RAG and a structured output layer. It's been running for eight months — not a single hallucination complaint.”
Frequently asked questions
- What is RAG and when do we need it?
- Retrieval-Augmented Generation grounds LLM responses in your actual data by retrieving relevant documents before generating an answer. You need it whenever the LLM needs to answer questions about your specific content — internal documentation, product knowledge bases, customer records, legal documents — rather than general world knowledge. Without RAG, the model either makes things up (hallucination) or tells users it doesn't know. With RAG, it answers from your data with citations you can verify.
- How do you prevent hallucinations in a production AI feature?
- By designing the system so the model can't produce arbitrary outputs. RAG constrains the model to your data. Structured output schemas (JSON mode or tool use) constrain the response format. Confidence thresholds route low-confidence outputs to a human review queue rather than showing them directly to users. System prompts explicitly instruct the model to acknowledge uncertainty rather than fabricate. And an evaluation pipeline catches regressions — if a model update starts hallucinating more, we know before users do.
- How do you manage LLM API costs in production?
- Token efficiency and caching. We audit prompts to remove unnecessary tokens (system prompt verbosity is a common waste). We implement prompt caching where the API supports it (Anthropic's cache_control, OpenAI's prompt caching) — a cached prefix costs 90% less per token. We set per-user and per-feature token budgets enforced at the application layer. We instrument every LLM call with cost attribution so you can see which features and which users are driving spend. Cost surprises happen when nobody is watching.
- Which LLM should we use — GPT-4o, Claude, or an open-source model?
- Depends on your use case, latency requirements, cost budget, and data privacy constraints. GPT-4o is strong for code generation, reasoning, and multimodal tasks. Claude is our preference for long-context document processing and instruction-following consistency. Open-source models (Llama 3, Mistral, Qwen) are appropriate when data can't leave your infrastructure or when inference cost at scale makes proprietary APIs uneconomical. We evaluate on your specific task before recommending — model benchmarks don't always predict performance on your exact use case.
- How do you handle LLM quality regressions when models are updated?
- With an evaluation harness that runs automatically against every deployment. We build a test suite of representative inputs with expected outputs (or evaluation criteria) and run it after every code change and after every model update. When a GPT-4o or Claude update changes behaviour on your specific prompts, the eval suite catches it before it ships to users. This is the missing piece in most LLM integrations — teams discover regressions from user complaints rather than automated tests.