Llama integration and customization services
We deliver end-to-end integration of Llama models into your existing systems. Our team develops Llama-powered applications, including API, RAG pipelines, fine-tuning, and secure deployment.

Our Generative AI development expertise
We provide complete support for Llama integration and deployment
What we cover in Llama integration and customization services
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1. Discovery & Architecture design
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2. Proof-of-Concept development
We deliver a functional prototype to test Llama’s capabilities in real-world conditions.
This includes:
- Implementing a RAG-powered knowledge retrieval system for enterprise applications.
- Developing an executable demo with performance benchmarking.
- Comparing model outputs against baseline models to optimize for accuracy and efficiency.
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3. Data pipeline engineering
We build high-performance data pipelines to prepare and feed structured data into Llama models.
This includes:
- Chunking strategies to enhance retrieval efficiency.
- Automated PII redaction for compliance with data security policies.
- Multi-format data ingestion, supporting PDFs, databases, and APIs for comprehensive knowledge integration.
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4. Model optimization phase
We fine-tune and optimize Llama for performance, cost efficiency, and deployment feasibility.
This includes:
- Quantization (with FP8 precision and GPTQ), reducing VRAM usage for deployments under 16GB.
- Speed optimization using TensorRT-LLM and vLLM.
- Accuracy improvements through RAG enhancement and RLHF.
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5. Security hardening
We implement advanced security measures to ensure the integrity and compliance of Llama deployments.
This includes:
- Model weight encryption (AES-256) and runtime integrity checks to prevent unauthorized access.
- Role-Based Access Control (RBAC) integration with Azure AD for enterprise authentication.
- Audit logging and compliance tracking for SOC 2 security standards.
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6. Deployment & Scaling
We deploy Llama on-premise, in the cloud, or in hybrid environments with auto-scaling capabilities.
This includes:
- Containerized deployment via Docker and Kubernetes, supporting high-availability scaling.
- Optimized inference infrastructure (e.g. by leveraging NVIDIA Triton Inference Server for real-time processing).
- Cloud cost optimization, using spot instances for fine-tuning and serverless model serving.
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7. Continuous improvement
We provide ongoing monitoring, updates, and performance tuning to keep Llama running at peak efficiency.
This includes:
- Real-time hallucination detection using entropy-based thresholding.
- Model drift alerts and usage analytics dashboards to track AI performance.
- Regular updates (e.g. including adapter swapping for new capabilities and security patches).
They trusted our expertise
Tools and technologies
Our AI tech stack for Llama integration
Llama Services
NLP Tools
Chatbot Frameworks
Deployment
Monitoring & Analytics
We build effective AI apps
Llama stack integration and orchestration
Llama stack integration
We integrate Llama into your infrastructure with prebuilt connectors for PyTorch, TensorRT-LLM, and vLLM to ensure efficient deployment and compatibility with your existing AI stack.
Observability suite
We provide real-time monitoring tools to detect model drift, hallucinations, and performance issues. Our observability solutions help maintain AI reliability and optimize ongoing performance.
Multi-LLM orchestration
We can implement fallback mechanisms that combine Llama with Claude, GPT-4, and other LLMs for enhanced accuracy and resilience. This ensures uninterrupted responses and improved AI decision-making.
Testimonial
What our clients say
By automating certain customer interactions, bank employees are provided with a prepared “semi-product”, which enables them to dedicate more time to personalizing and empathizing with customer communication, and thus taking even better care of their needs.
Why choose us
Llama integration company
Advanced integration architecture
Industry standards compliance
Deep domain knowledge
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