Aviga builds AI-powered startups — from document intelligence tools and AI copilots to autonomous agent systems and LLM-powered SaaS products. We go beyond demos to production-grade AI.
Retrieval-Augmented Generation with vector databases (Pinecone, Weaviate, pgvector) to ground LLM outputs in your specific data.
Fine-tune GPT-4o, Claude 3, or Llama 3 on your domain data for higher accuracy, faster responses, and lower per-query costs.
Multi-step autonomous agents with tool use, memory, and planning — for use cases that require sequential reasoning and external API calls.
We build automated evaluation pipelines to measure AI output quality, detect regressions, and continuously improve your AI features.
Streaming responses, caching, rate limiting, cost monitoring, and prompt version control for production-grade AI deployments.
OpenAI and Anthropic offer the best out-of-the-box quality for most use cases. Open-source models (Llama 3, Mistral) are better when: you have sensitive data that can't go to third-party APIs, you want to fine-tune on your data, or you need to optimize costs at very high volume. We recommend starting with OpenAI/Anthropic and evaluating open-source when you hit scale.
RAG (Retrieval-Augmented Generation) is needed when you want an AI to answer questions about YOUR data — documents, knowledge bases, databases — rather than just its training data. It retrieves relevant chunks of your data and sends them to the LLM as context. Without RAG, the AI makes up answers for domain-specific questions.
Through RAG (grounding answers in retrieved facts), prompt engineering (system prompts that constrain the AI), output validation (checking responses against expected formats), confidence thresholds, and human-in-the-loop for critical decisions. No approach eliminates hallucinations entirely — we design systems that minimize and detect them.
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