LLM Architecture & Design
We design production AI systems that learn from your data, stay accurate under real world traffic, and stay safe through every answer, from retrieval and fine-tuning to guardrails and multi-model routing.
Start a ProjectWhat We Engineer →
RAG Pipeline Design
Retrieval-augmented generation systems with vector databases, embedding strategies and context window optimization for accurate, grounded responses.
DiscussPrompt Engineering & Optimization
Systematic prompt design, chain-of-thought architectures and few-shot strategies that maximize model performance for your specific use cases.
DiscussFine-Tuning & Adaptation
Domain-specific model fine-tuning using LoRA, QLoRA and full fine-tuning approaches to create specialized models that outperform general-purpose LLMs.
DiscussGuardrails & Safety
Content filtering, output validation, hallucination detection and compliance frameworks that keep LLM outputs safe, accurate and on-brand.
DiscussEvaluation & Testing
Automated evaluation pipelines, benchmark suites and regression testing frameworks that measure and maintain LLM quality over time.
DiscussMulti-Model Orchestration
Architecture for routing between multiple LLMs based on task complexity, cost optimization and capability matching for the best results at the lowest cost.
DiscussOur Engineering Process
Technology We Work With
LLM Providers
- ▹ OpenAI
- ▹ Anthropic
- ▹ Meta/LLaMA
- ▹ Mistral
Frameworks
- ▹ LangChain
- ▹ LlamaIndex
- ▹ Semantic Kernel
- ▹ Haystack
Vector DBs
- ▹ Pinecone
- ▹ Weaviate
- ▹ Chroma
- ▹ Qdrant
Evaluation
- ▹ RAGAS
- ▹ DeepEval
- ▹ Promptfoo
- ▹ Custom Suites
Infrastructure
- ▹ AWS Bedrock
- ▹ GCP Vertex AI
- ▹ Azure OpenAI
- ▹ Modal
Monitoring
- ▹ LangSmith
- ▹ Helicone
- ▹ Weights & Biases
- ▹ Datadog
Ready to build with LLMs?
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