AI Implementation
Most AI projects die as impressive demos. The model does something clever in a sandbox, everyone nods, and then it never touches real work. We do the opposite. We put practical models where they earn their keep, inside the workflows your team already uses.
Green Lion Refinery treats AI as one more tool in the system, not the point of it. We start from a task worth improving, choose the simplest model that does the job, and wire it in with the checks that keep it trustworthy.
What we build
LLM assistants and agents
Assistants that answer from your own material and agents that carry out real steps, scoped tightly so they help with a specific job rather than pretending to do everything.
RAG over your own data
Retrieval grounded in your documents, tickets, and records, so answers cite what you actually have instead of guessing. This is what turns a generic chatbot into something your team trusts.
Document and intake processing
Pulling structure out of invoices, forms, emails, and PDFs, then routing the result into the systems that need it. The tedious reading-and-typing step disappears.
Evaluation and guardrails
Tests that measure whether the model is actually right, plus limits on what it is allowed to do. You ship AI you can defend, and you know when it drifts.
How the engagement works
We begin with one narrow, high-value use case and hold it to a real bar for accuracy. If a model cannot clear that bar, we say so rather than shipping something that guesses.
- Pick one task where good answers save real time or money
- Ground the model in your data and wire it into the workflow
- Add evaluation so quality is measured, not assumed
- Set guardrails and hand over something you can trust in production
Who this is for
Small businesses, solo operators, and startups that want AI to do useful work, not sit in a slide deck. If you have a pile of documents or a repetitive judgment call that a grounded model could handle, that is the signal.
Common questions
How do you keep the AI from making things up?
We ground models in your own data through retrieval and add evaluation that measures whether the answers are actually correct. If a use case cannot clear a real accuracy bar, we tell you rather than shipping guesswork.
Which model or provider do you use?
Whatever fits the task and the budget. We choose the simplest model that clears the bar instead of defaulting to the biggest one, and we can change providers as your needs change.
Is my data used to train someone else's model?
No. We build with providers and settings that keep your data yours, and we set guardrails on what the system is allowed to access and do.
Related services
Practical AI depends on good inputs. Clients usually pair this with Data Engineering so the models have clean data to work from, and Workflow Automation to put the outputs to work.
Want to see if this fits? Tell us the one task you wish AI could take off your plate. We will tell you honestly whether it is ready for that, and how we would prove it.