What it is
Rozell helps companies put large language models to work in a way that is practical, private, and maintainable. We treat AI as a component in a well-built system, not a product on its own, and we build it on the same Python and AWS stack that runs the rest of your software.
What you get
- A scoped AI opportunity review: where a model would actually save hours or create a feature, and where it would not.
- Retrieval-augmented generation (RAG) over your documents, tickets and data, with sources cited and access controls respected.
- Document intelligence that extracts structured data from PDFs, forms, invoices and email, with human review where confidence is low.
- Internal assistants and copilots connected to your systems, deployed inside your environment.
- AI-powered automation: classification, routing, drafting and summarization built into the workflows we automate.
- Evaluation and monitoring, so you know how the system performs and can see when it drifts.
How an engagement runs
- Opportunity review (1 week). We look at your data, your processes and your constraints and propose one or two first projects with expected value.
- Pilot (2–6 weeks). A working system in production for a limited group, with measurement built in.
- Expand or stop. With numbers in hand, you decide whether to roll out, extend, or move on.
Who it’s for
- Companies with a lot of unstructured information: documents, tickets, contracts, email.
- Product teams that want an AI feature without a research project.
- Operations teams whose manual work is mostly reading, classifying and responding.
- Regulated businesses that need AI inside their own environment and region.
Common questions
Which AI models and providers do you work with?
We build against OpenAI, Anthropic and AWS Bedrock, and keep the model behind an interface so you can switch as pricing and quality change. For retrieval we usually start with pgvector on the PostgreSQL database you already have, and move to a dedicated vector store only when the data demands it.
How do you keep our data private?
Data stays in your AWS account. We use provider APIs with training opt-outs, redact sensitive fields before they leave your systems where required, and can run open-weight models on Bedrock for workloads that must not leave your region.
What is a realistic first AI project?
The best first projects are narrow and measurable — answering questions over your internal documents, extracting fields from inbound PDFs, drafting responses for a support queue, or classifying and routing requests. Each can be live in a few weeks and shows whether a larger investment is justified.
Do we need a data science team to maintain this?
No. The systems we build are ordinary software with an API call to a model inside. Any engineer who can maintain a Python service can maintain them, and we leave evaluation tests behind so changes can be checked.