AI & Automation
LLM integration, retrieval over your own documents, and automation for the workflows that eat staff time.
What this is
AI & Automation is the practice of applying large language models and workflow automation to specific, bounded business tasks: answering questions against a private knowledge base, extracting structured data from documents, routing and triaging incoming requests, or automating a multi-step process that currently depends on someone doing it by hand. The goal is always a defined task with a measurable output, not a chatbot for its own sake.
Problems it solves
- Institutional knowledge scattered across PDFs, wikis, and old emails, with employees re-answering the same questions because the answer is buried and unsearchable.
- Manual document processing — invoices, contracts, intake forms — that eats staff time on work that is repetitive but not simple enough for a basic script.
- Multi-step workflows spanning several tools (a form submission that should trigger a CRM update, a notification, and a follow-up task) stitched together by hand or not stitched together at all.
- Interest in "using AI" without a clear task, budget, or success metric attached to it — a reliable way to burn a quarter and end up with a demo that never ships.
What the work involves
- RAG architecture: retrieval-augmented generation systems that ground an LLM's answers in your own documents and data, with citations back to source, instead of relying on the model's general knowledge (and its tendency to guess).
- Document processing: extracting structured fields from unstructured documents — invoices, contracts, forms — using a combination of OCR, layout-aware extraction, and LLM-based field parsing, with a human review step for anything below a confidence threshold.
- Agent workflows: LLM-driven agents that carry out multi-step tasks (look something up, decide, take an action) within clearly scoped tool access, not open-ended autonomy.
- No-code/low-code automation: connecting existing systems with n8n or Make for workflows that do not need custom code, reserving bespoke engineering for the steps that actually require it.
- Evaluation: defining what "correct" means for the task before building it, and testing against that definition rather than shipping on vibes.
What you get
An automation or AI system scoped to a task you can measure — hours saved, requests handled, documents processed — with the guardrails (human review, tool-access limits, evaluation criteria) that keep an LLM-based system predictable in production rather than a source of new incidents.
When this isn't the right fit
If a task is simple, deterministic, and rule-based, a conventional script or a straightforward no-code workflow is more reliable and easier to maintain than adding an LLM into the loop. LLMs earn their complexity on judgment-heavy or unstructured-input tasks, not on "if this, then that."
Where this work lands
Industry pages that draw on this service.
Next step
If a specific task in your operation is repetitive, document-heavy, or scattered across tools that do not talk to each other, talk to our team about whether it is a good automation or RAG candidate.