AI Development & Automation
We build AI that does real work. LLM applications, retrieval over your own data, and automation that takes hours out of operations-heavy teams. We judge it by the work it removes, not the demo it gives.
What this covers
- LLM/AI Applications
- RAG Systems
- Workflow Automation
- AI Agents
Outcomes
What you walk away with.
Automation tied to a number you already track: hours saved, tickets cleared, errors avoided.
RAG and LLM systems grounded in your data, with guardrails against made-up answers.
Evaluations and monitoring, so you can trust the output instead of hoping it is right.
A clear line between where AI helps and where plain software does the job better.
What we build
The work, specifically.
LLM/AI Applications
Production LLM features like assistants, classification, extraction, and generation, wired into your product with evaluation and cost controls from day one.
- Assistants & chat
- Extraction & classification
- Cost controls
RAG Systems
Retrieval pipelines over your documents and data, built on vector stores like pgvector or ChromaDB, so answers stay grounded in your content and current.
- Vector search
- pgvector / ChromaDB
- Grounded answers
Workflow Automation
Automation across your operations with n8n and custom services, removing the repetitive work that quietly eats your team's week.
- n8n pipelines
- Custom services
- Human in the loop
AI Agents
Scoped, observable agents that take real actions in your systems, with the limits, logging, and human checkpoints that keep them safe to run.
- Tool use & actions
- Guardrails & limits
- Full observability
How we work
A process built to hand over.
Discovery
We start where the hours go. A short, paid sprint maps one high-value workflow and the number it should move, so we don't automate the wrong thing well.
Requirements & design
We pick the models, data sources, and retrieval approach, then set the accuracy and cost targets before the build starts.
Iterative build
We build in weekly cycles with evaluations running the whole time, measuring accuracy, cost, and latency against the baseline.
Ship & operate
We deploy with monitoring, fallbacks, and a human in the loop where it matters, then tune against real usage. You see the impact in your own numbers.
Tech stack
Tools we trust in production.
FastAPI
Python
PostgreSQL
Redis
OpenAI
DeepSeek
LangChain
LlamaIndex
Hugging Face
ChromaDB
n8n
Docker
AWSFAQ
Questions,
answered.
Questions specific to this service. More on the main FAQ, or send us a note and we'll answer it directly.
We ground models in your own data with retrieval, add evaluations to measure accuracy against a baseline, and keep humans in the loop where a wrong answer is expensive. We ship monitoring so you can see how it performs in production, not just in a demo.
Often. If a deterministic rule or plain software solves the problem more reliably and cheaply, we'll tell you and build that instead. AI ships only where it pulls its weight.
Discovery is a fixed fee to find a high-value workflow and the number it should move. The build runs as a monthly engagement scoped one cycle at a time, with evaluations and monitoring included so you can trust what ships.
Our other service
Custom Software Development
From the blog