What we deliver

• LLM model selection (open-source vs proprietary) and sizing
• Prompt engineering frameworks, templates, and CI validation
• Fine-tuning with LoRA / QLoRA / adapters using PyTorch
• RAG pipelines with vector databases (FAISS, Pinecone, Weaviate)
• LLM safety layers, red-teaming, and policy guardrails
• Latency, token-cost optimization, and observability dashboards

Why it works

Oodles' LLM delivery approach combines strong evaluation practices, grounded retrieval, and safety-first design—allowing teams to ship LLM features with confidence before scaling usage.

Customer & employee assistants

Grounded, safe responses with real-time knowledge sources.

Content & knowledge workflows

Summarization, redaction, translation, and enrichment at scale.

Developer & ops copilots

Code review aids, runbook agents, and automated SOP drafting.

Data & analytics

SQL/text-to-DSL helpers with guardrails and lineage tracking.

Need the right LLM stack?

We balance model choice, safety, latency, and cost—then ship with evals and monitoring.

Discovery & data mapping

Map tasks, data sources, compliance, and latency/cost constraints.

Model & grounding design

Select base model, retrieval strategy, safety layers, and observability plan.

Fine-tuning & evals

Apply LoRA/QLoRA, build eval harnesses, and red-team critical workflows.

Delivery & integration

Wire APIs/SDKs, CI for prompts, and connect monitoring dashboards.

Launch & optimize

Roll out safely with rate limits, eval gates, and continuous cost/quality tuning.