Ship Reliable Large Language Model (LLM) Features Faster
Oodles helps enterprises design, build, and deploy Large Language Model (LLM) solutions using modern GenAI architectures—balancing accuracy, safety, latency, and cost. We work across the full LLM stack including foundation models (OpenAI, Gemini, Claude, Llama, Mistral), retrieval-augmented generation (RAG), vector databases, LoRA/QLoRA fine-tuning, evaluation frameworks, and production-grade deployment with guardrails to ensure LLM systems remain accurate, compliant, and scalable.
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.
How we deliver LLM initiatives
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.
FAQs (Frequently Asked Questions)
LLMs are large transformer-based models trained on vast text. They generalize across tasks with few examples, unlike traditional NLP which needed task-specific training. LLMs support chat, summarization, coding, and more out of the box.
Use RAG when you need up-to-date or proprietary knowledge without retraining. Use fine-tuning when you need consistent style, domain jargon, or format. Many solutions combine both.
Grounding connects LLM outputs to verified sources (RAG, search). It reduces hallucinations by requiring answers to cite retrieved documents. Critical for customer support, legal, and enterprise knowledge apps.
Consider latency, cost, context length, multilingual support, and hosting (cloud vs on-prem). Smaller models (7B–13B) suit high-throughput apps; larger models for complex reasoning. We benchmark and recommend based on your requirements.
Input/output filtering, PII redaction, content policy checks, and logging. Use moderation APIs and custom rules. For regulated industries, add audit trails and human-in-the-loop reviews where required.
Yes. Open-weight models (Llama, Mistral, etc.) can run on your infrastructure. You need GPUs and MLOps tooling. We help with model selection, quantization, and deployment for on-prem or VPC setups.
Track latency, token usage, error rates, and cost. Log prompts and responses for debugging and compliance. Use traces for multi-step flows. We integrate with your existing APM and logging stack.
Ready to ship dependable LLM features? Let's talk
We are ISO 9001:2015 Certified
Connect with us
Follow us