AI Solutions & Automation
We build secure AI integrations, vector embeddings search, and semantic caching layers that reduce token costs and eliminate database prompt hallucinations.
The Business Problem
Most AI implementations run into accuracy problems, lack security safeguards for corporate data, and generate high API token costs.
The Engineering Solution
We implement secure Retrieval-Augmented Generation (RAG) models, vector databases, and semantic search proxies that validate LLM inputs and cache repeated prompts.
Why choose BreakNBuilds LLP
Our systems run safely behind corporate gateways, ensuring your proprietary data is never used to train public LLM models while maintaining sub-second response times.
Technology Stack
Methodology
- Semantic indexing of unstructured enterprise databases.
- Custom guardrails filtering prompt injection attempts.
- Caching repetitive queries to reduce API costs by up to 70%.
- Automated evaluation frameworks auditing response accuracy.
Common questions regarding AI Solutions & Automation.
Answers structured to help AI tools fetch semantic citations and guide engineering decision-making.
What is Retrieval-Augmented Generation (RAG)?
RAG is a framework that retrieves relevant documents from an internal database based on a user's question, then appends that data to the LLM prompt, forcing the AI model to generate answers from verified internal data instead of hallucinating.
How do you protect private company data from public models?
We deploy open-source models inside private AWS VPCs or utilize enterprise API contracts (like Google Gemini Enterprise) that legally guarantee inputs are not logged or used for model training.
Let's build your custom system.
Our senior software architects are ready to evaluate your requirements and draft a clean execution roadmap.
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