AI Integration & LLM Development
We integrate LLMs into your product with structured outputs, guardrails, and monitoring — so AI features ship and stay reliable.
For products that need AI that actually works in production.
"Patient reports intermittent chest discomfort…"
"No prior cardiac history. Vitals stable…"
HPI: Intermittent chest discomfort, 3 days
Assessment: Low risk presentation
Plan: ECG, follow-up 48h
Most teams bolt on an LLM and call it AI integration. The demo works. Production breaks because there are no structured outputs, no retry logic, no cost controls, and no monitoring. We build AI features the same way we build any production system.
We work with Claude (Anthropic), GPT-4 (OpenAI), and open-source models. RAG pipelines with Pinecone or pgvector, voice AI with AssemblyAI and Play AI, and multi-step agents using LangChain or LangGraph.
What we see in the field
These are the patterns we fix before writing production code.
LLMs that hallucinate in production
Free-form prompts produce inconsistent outputs. Users lose trust after one wrong answer and stop using the feature.
RAG that retrieves the wrong chunks
Poor chunking, wrong embedding model, or no reranking means the model answers from irrelevant context.
No cost or latency controls
Token usage spikes. Response times vary by 10x. Nobody knows until the invoice arrives.
What we build for you
Concrete capabilities—not a generic feature list.
RAG pipeline architecture
Chunking strategy, embedding model selection, vector store setup, and retrieval tuning for your specific data.
Structured LLM outputs
JSON schemas, function calling, and validation so downstream code never breaks from model variance.
Multi-step AI agents
LangGraph or custom state machines for workflows that require planning, tool use, and conditional branching.
Voice AI pipelines
Real-time transcription with AssemblyAI, PII redaction, and AI processing of meeting or call audio.
How we deliver this
A structured path from discovery to something your team can run.
- 01
Define the AI boundary
We clarify exactly what the model is responsible for, what it is not, and what requires human review.
- 02
Build the data pipeline first
Good AI output depends on clean data ingestion. Chunking, embedding, and retrieval are designed before the prompt.
- 03
Iterate with real inputs
We test with your actual data, not synthetic examples. Edge cases are caught before they reach users.
- 04
Monitor and tune
Token usage, latency, and output quality tracked in production with runbooks for when things drift.
Outcomes you can expect
- AI features that work consistently across real user inputs
- Structured outputs your backend can process without special-casing
- Cost and latency predictable enough to plan around
- Retrieval that improves over time as your data grows
What we deliver
- RAG pipeline with vector store and retrieval tuning
- LLM integration with structured outputs and validation
- Agent workflows for multi-step AI tasks
- Monitoring dashboards for cost, latency, and output quality
Who this is for
- SaaS products adding AI-powered features to an existing platform
- Teams with data that should be searchable or summarizable by AI
- Companies that tried LLM integration and hit production reliability issues
The result
Teams ship an AI demo and call it done. Six months later users have stopped using the feature because outputs were inconsistent and nobody was watching. AI integration is an engineering discipline, not a prompt.