Connect OpenAI, Claude, retrieval-augmented generation, vector search, structured outputs, and prompt pipelines to your existing product or workflow. Production-grade integrations with the reliability, observability, and cost controls that real usage demands.
The opportunity
Calling an LLM API for the first time takes ten minutes. Building an integration that is accurate, reliable, cost-controlled, and maintainable in production takes considerably more. The gap between a prototype and a system you can ship to customers is where most AI projects stall — and where most of the value is either captured or lost.
The hard parts are not the model. They are everything around it: grounding outputs in your real data so the model does not hallucinate, structuring responses so downstream code can rely on them, managing context windows and token costs, handling failures and rate limits gracefully, evaluating quality as models and prompts change, and keeping the whole system observable so you know when something breaks.
We build that layer. Whether you are adding an AI feature to an existing SaaS product, embedding intelligent document processing into an internal tool, or standing up a knowledge base that actually answers questions accurately, the integration architecture is what determines whether the result is a toy or a product.
What we build
A real integration is a pipeline of specialized components. Here is what we typically assemble, tuned to your use case.
We evaluate OpenAI, Anthropic Claude, and open-source models against your requirements — accuracy, latency, cost, data sensitivity — and build with a provider abstraction layer so you can swap models as the landscape shifts without rewriting your application.
We ground model outputs in your own documents, knowledge base, and data so answers are accurate, citeable, and current. The model reads your information first, then responds — dramatically reducing hallucinations on domain-specific questions.
We index your content using embeddings so the system can find semantically relevant information fast — across thousands of documents, tickets, product specs, or past conversations. This is the retrieval engine that powers accurate RAG.
We enforce JSON schemas and validation so the model returns data your code can trust, not free-form text that breaks downstream. This turns an LLM from a chatbot into a reliable data-processing component inside a larger pipeline.
Production prompts are rarely a single call. They are multi-step pipelines: retrieve, reason, draft, self-check, format, and output. We design and version these pipelines with evaluation harnesses, fallback logic, cost controls, and full logging so quality is measurable and the system degrades gracefully under load or failure.
Production concerns
A demo that works on three examples is not a product. Every integration we ship is designed for the realities of production traffic: unpredictable inputs, rate limits, cost spikes, model drift, and the occasional user who tries to break things. We bake in the safeguards that keep the system honest.
That means token budgets and cost monitoring so a runaway loop cannot drain your API budget overnight. It means evaluation suites so you know whether a prompt change improved or regressed quality before shipping. It means graceful degradation — if the primary model is unavailable, the system falls back rather than crashing. And it means observability: you can see every request, every cost, every failure, in real time.
Cost controls
Token budgets, caching, and per-request cost tracking prevent surprise bills and keep unit economics sane.
Evaluation harnesses
Automated test suites run before every prompt or model change so regressions are caught, not discovered by users.
Observability and fallbacks
Full request logging, latency tracking, and automatic fallback to backup models when a provider has an outage.
How it works
01
We define the use case, success metrics, data sources, accuracy requirements, and constraints. We evaluate providers and pick the right architecture before writing production code.
02
We build the pipeline — retrieval, prompting, structured outputs, guardrails — and validate it against real examples with an evaluation harness. Accuracy is measured, not assumed.
03
We ship to production with monitoring, cost controls, and fallbacks. We watch real usage, tune quality, and hand over documentation so your team can maintain and extend the system.
Common questions
Which LLM provider should we use?
+It depends on your use case, latency requirements, budget, and data sensitivity. OpenAI and Anthropic Claude are the most common choices for production workloads, but open-source models like Llama can make sense for cost or privacy reasons. We help you evaluate the tradeoffs and build with provider abstraction so you can switch models as the landscape evolves without rewriting your application.
What is RAG and do we need it?
+Retrieval-Augmented Generation grounds the model in your own documents and data so answers are accurate and citeable rather than invented from the model's training data. If your use case depends on internal knowledge — company policies, product specs, past support tickets, legal documents — you almost certainly need RAG. Without it, the model will confidently make things up.
How do you prevent hallucinations?
+We use a combination of techniques: grounding via RAG so the model references real sources, structured output schemas so responses are constrained, confidence thresholds so low-certainty outputs are flagged, and human review for high-stakes decisions. No LLM integration is one hundred percent accurate, but the right architecture keeps failures rare, visible, and recoverable rather than silent and damaging.
Can you integrate with our existing codebase?
+Yes. We work with Python, Node.js, Next.js, and most modern web stacks. We can build new API endpoints, add AI features to existing products, wrap LLM calls behind your own internal API, or integrate with platforms like Supabase, Vercel, and standard cloud infrastructure. We meet your codebase where it is.
Book a free audit. We assess your use case, recommend an architecture, and identify the highest-value integration to build first.