Generative AI & RAG Development
From proof-of-concept to production LLMs. We build generative AI systems that work on your private data — document intelligence, retrieval-augmented generation, fine-tuned models and computer vision — with the LLMOps discipline to keep them accurate, fast and affordable in production.
What we deliver
LLM applications
Document intelligence, chat over knowledge bases, summarization and extraction — built for accuracy on your domain, not demo conditions.
RAG over private data
Retrieval pipelines on Pinecone, Weaviate, ChromaDB and FAISS with chunking, reranking and evaluation tuned to your corpus.
Fine-tuning & prompt engineering
Domain-adapted models where RAG alone is not enough — with rigorous benchmarks proving the lift before you pay for training.
Computer vision & ML in production
YOLOv8 detection, classification and OCR pipelines with automated retraining — 98% accuracy systems running in the field today.
LLMOps on AWS
Bedrock and SageMaker deployment with monitoring, cost control, evals and drift detection — production discipline for probabilistic systems.
Technologies we work with
Proven in production
From Handwritten Deeds to $8.5M in New Leases: AI-Powered Mineral Rights Intelligence
90%
Faster deed processing
Read the Glacier Analytics story →
From 48-Hour Lab Waits to 15-Second Answers: Computer Vision for Water Testing
15 sec
Analysis time, down from 48 hours
Read the PoolWater Pro story →
Generative AI & RAG Development — FAQs
Should we use RAG or fine-tune a model?
Start with RAG for knowledge that changes — it is cheaper, auditable and updatable without retraining. Fine-tune when you need consistent style, structured outputs or domain reasoning that prompting cannot achieve. Many production systems combine both. We benchmark on your data before recommending either.
Can you build LLM applications on our private data securely?
Yes. We deploy within your cloud account (AWS Bedrock, SageMaker or self-hosted models), so data never leaves your environment. We implement access controls, PII handling and audit logging as standard.
How do you measure whether a generative AI system is good enough?
We define an evaluation suite with you before building — accuracy against ground truth, hallucination rate, latency and cost per request. Every release is measured against it. No launch without numbers.
What does a generative AI project cost?
A scoped proof-of-concept starts at $2,000; production RAG or document-intelligence systems from $5,000, with a fixed quote agreed upfront.
Ready to talk Generative AI & RAG?
Tell us what you want to automate, build or improve. Our team will review your project and reply by email with practical next steps. No obligation.