Oodles AI provides experienced LLM developers who design, build, and operate production-grade language model systems with evaluations, observability, security guardrails, and cost controls.
Work with Oodles AI’s LLM developers to build production-ready applications using large language models. Our engineers specialize in retrieval-augmented generation (RAG), prompt engineering, fine-tuning, safety layers, and scalable APIs aligned with enterprise data and governance requirements.
Our LLM developers manage the full lifecycle of language model systems using Python, FastAPI, LangChain, vector databases, and cloud infrastructure. We deliver RAG pipelines, prompt frameworks, fine-tuned models, safety filters, monitoring dashboards, and operational runbooks to keep LLM features stable in production.
Targeted engineering support across product, data, and platform teams.
Search, summarization, and assistant applications using vector search, citations, and fallback logic.
Multi-step agent systems with tool calling, orchestration, and guardrails for controlled execution.
Fine-tuning and PEFT methods to align model outputs with domain, tone, and compliance requirements.
PII detection, output filtering, jailbreak testing, and audit-ready logging pipelines.
API-driven LLM features integrated into web, mobile, CRM, and internal platforms.
Golden datasets, regression testing, drift detection, and dashboards for quality and cost visibility.
A disciplined build-measure-iterate workflow used by Oodles AI to deliver secure, scalable, and production-ready LLM systems.
Architecture & model choice
Select the optimal LLM family, context window, and performance profile.
Data & retrieval setup
Set up embeddings, vector search, and retrieval logic to ground responses.
Safety & guardrails
Implement safety layers including PII masking, abuse filters, and policy checks.
Evals & tuning
Run evaluations, prompt tuning, and fine-tuning to improve accuracy.
Deploy & observe
Deploy APIs with monitoring, alerts, and cost controls for production use.