Ship Llama-powered applications with enterprise guardrails
Oodles designs, fine-tunes, and deploys Meta Llama models in secure, enterprise environments. We build Llama-based applications with private hosting, retrieval pipelines, safety controls, and full observability so teams can launch and scale with confidence.
What we deliver with Llama
End-to-end Llama deployments aligned to your data, security posture, and runtime performance requirements.
- • Model strategy using Llama 3, Code Llama, and domain-adapted variants
- • Retrieval-augmented generation with vetted connectors and vector stores
- • Evaluation, red-teaming, and safety layers for PII and jailbreak risks
- • MLOps, cost governance, and latency monitoring for production workloads
Private & Hybrid Hosting
Host Llama models on AWS, Azure, GCP, or on-prem infrastructure with network isolation, IAM, and secrets management.
Fine-tuning & Adapters
Parameter-efficient tuning with LoRA and QLoRA, prompt tuning, and instruction alignment on private datasets.
Retrieval & Connectors
RAG pipelines for Llama using chunking, metadata, vector search, and policy-aware retrieval.
Observability & Guardrails
Built-in telemetry, evaluation harnesses, content filters, and prompt hardening for safe usage.
High-impact Llama use cases
Knowledge Assistants
Llama-powered assistants for internal knowledge, SOPs, and policy documents with citations and controls.
Developer Productivity
Code assistance, refactoring, and test generation using Code Llama models tuned to your repositories.
Document Intelligence
Summarization, Q&A, and structured extraction across contracts, tickets, and enterprise documents.
Process Automation
Llama-driven agents that orchestrate workflows through APIs, ticketing systems, and knowledge bases.
Multilingual Support
Multilingual Llama deployments with PII masking, audit logs, and role-based access control.
Integrations & tooling
Oodles integrates Llama models with your data platforms, orchestration layers, and enterprise controls.
Delivery approach
A structured delivery model used by Oodles to take Llama applications from concept to production-ready deployment.
1
Goals & Risk Posture: Define business outcomes, compliance requirements, and data boundaries.
2
Data & Policy Setup: Connect data sources, configure access controls, and apply safety policies.
3
Prototype & Evaluation: Build Llama pilots with evaluation harnesses, red-team tests, and guardrails.
4
Integrations & Automation: Integrate Llama APIs, webhooks, and monitoring into existing SDLC pipelines.
5
Rollout & Optimization: Launch production workloads, monitor cost and latency, and iterate continuously.
FAQs (Frequently Asked Questions)
Meta Llama is an open-weight LLM family. Use it when you need private hosting, fine-tuning on proprietary data, and no API vendor lock-in. Strong for chat, code, and RAG with full control.
Llama 3 8B: 1×24GB GPU. 70B: 2×80GB or 4×48GB. Use quantization (4-bit, 8-bit) to reduce requirements. We help size and deploy on AWS, GCP, Azure, or on-prem.
Yes. Use LoRA, QLoRA, or full fine-tuning. We train on your data for domain jargon, formats, and behavior. Typical dataset: 500–5k examples depending on use case.
Code Llama is optimized for code generation and completion. Use for IDE tools, code review, or docs. Supports Python, C++, Java, and more. Pairs well with RAG over codebases.
Output filters, PII redaction, content moderation, and guardrails. We add evaluation harnesses and human review where needed. Align with your compliance and audit requirements.
Llama 3 uses Meta's Llama 3 Community License. Free for most commercial use below a revenue threshold. Check current terms. We help structure deployment within license bounds.
Basic deployment: 1–2 weeks. RAG or fine-tuned setup: 4–8 weeks. Full production with observability and safety: 2–3 months. Depends on infra, data, and integrations.
Ready to build with Llama? Let's talk
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