Applied AI & Machine Learning Engineering
Move beyond brittle API wrappers. Nymph engineers governed, production-grade applied AI systems — combining custom fine-tuned models, semantic retrieval pipelines, and autonomous agent workflows with strict cryptographic privacy and low latency.
Frequently asked questions
Transparent answers regarding contracts, security protocols, IP transfer, and day-to-day operations.
Will our proprietary business data be used to train external models?
Never. We build on zero-data-retention enterprise API endpoints or deploy open-weights models (such as Llama 3.3 or DeepSeek) entirely inside your own private VPC or on-premise hardware.
How do you prevent hallucinations in enterprise RAG systems?
We implement hybrid search (combining dense vector retrieval with BM25 keyword matching), cross-encoder reranking, and deterministic constraint guardrails that force the model to cite specific source documents or explicitly state when information is absent.
What is the cost difference between private hosting and OpenAI/Claude APIs?
For low-volume prototypes, API calls are cost-effective. However, once monthly token volume exceeds ~50M tokens, deploying quantized open-weights models on dedicated cloud GPUs (e.g. AWS g5 instances) typically reduces monthly inference expenses by 60% to 80%.
Can you deploy AI models on our existing cloud infrastructure?
Yes. Our engineers routinely deploy inside client AWS, GCP, Azure, or private Kubernetes clusters using Terraform and Helm charts, ensuring full compliance with your existing security policies.
How long does it take to deploy a custom AI prototype?
A working, benchmarked proof-of-concept on your data is typically delivered within 5 to 10 business days, followed by production hardening and security audit reviews.