AI development and consulting for small businesses
Production AI, engineered end to end. A prototype that works in a notebook and a system that answers customers at 2am are different engineering problems. We build the second kind: retrieval pipelines grounded in your own data, models fine-tuned on your domain, and the inference infrastructure, evaluations, and monitoring that keep them accurate and affordable once real traffic hits them.
AI solutions we build
Chatbots and conversational systems
Retrieval-augmented generation over your documents, tickets, and product data — chunking and embedding strategy, hybrid semantic plus keyword search, reranking, citation of sources, and clean escalation to a human when confidence drops.
LLM application engineering
Tool calling and agentic workflows that act on your systems, structured extraction with schema-validated output, context and prompt engineering, guardrails against injection and data leakage, plus prompt caching and routing to hold latency and cost down.
Model training and fine-tuning
PyTorch training pipelines, parameter-efficient fine-tuning with LoRA and QLoRA, distillation of a large model into a small one you can afford to run, custom embedding and classification models, and the dataset curation and labeling that decide whether any of it works.
AI infrastructure and MLOps
GPU capacity planning, quantized inference serving with batching and autoscaling, vector database design, experiment tracking, model registries and CI/CD, plus observability on latency, token spend, and quality drift after deployment.
Tools we work in
- PyTorch
- Hugging Face
- LoRA / PEFT
- vLLM
- ONNX Runtime
- CUDA
- Amazon Bedrock
- Amazon SageMaker
- pgvector
- OpenSearch
- Ray
- MLflow
- Docker
- Terraform
How we deliver
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1. Evaluate before you build
We assemble an evaluation set from your real cases and define the metric that matters — answer accuracy, extraction F1, escalation rate — before writing the system. Without it, "it seems better" is the only signal you'll ever have, and it isn't one.
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2. Retrieval first, training second
Most problems that look like they need a trained model need better retrieval and context. We exhaust prompting and RAG first, then fine-tune only where the evaluations show it earns the cost — because a fine-tune is a maintenance commitment, not a one-time task.
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3. Serve it, then watch it
Deployment on right-sized inference infrastructure with cost per request measured, quality tracked against a held-out set, and drift caught by monitoring rather than by a customer complaint.
Let's talk
Tell us about the process that eats your team's week — the quoting, the data entry, the chasing. We'll tell you honestly whether it's worth automating and what it would take. We reply within one business day.