Where AI beats a rules-based script
Traditional automation is perfect when the input is predictable: this field equals that, do this. It falls apart when the input is human — an email in someone's own words, a PDF with a different layout every time, a support ticket that could mean five different things.
That's where a language model earns its place. We use LLMs for the interpretation step — reading, summarizing, extracting, classifying, drafting — and keep deterministic code around it for anything that has to be exactly right.
- Read invoices, resumes, or contracts and pull out structured fields
- Classify and route incoming emails or support tickets by intent
- Draft first-pass replies a human reviews before sending
- Summarize long threads, documents, or meeting notes
- Turn messy free-text notes into clean, structured records
Guardrails so the AI doesn't run your business unsupervised
An LLM is a powerful reader and writer, but it can be confidently wrong. We design around that instead of pretending it away.
Consequential steps get a human check, outputs get validated against real data, and the model is grounded in your actual documents rather than left to improvise. You decide where AI drafts and where a person approves.
- Human-in-the-loop review on anything that sends, pays, or deletes
- Grounding in your own data so answers cite real sources, not guesses
- Validation and formatting checks on every AI output before it's used
- Confidence thresholds that route uncertain cases to a person
- Logging of prompts and results so you can audit what happened
More on automation & scripts
Frequently asked questions
What if the AI makes something up or gets it wrong?
We assume it can and build for it. Language models can be confidently incorrect, so we validate outputs against your real data, ground answers in your actual documents, and keep a human approval step on anything with real consequences. AI drafts and interprets; it doesn't get to take irreversible actions on its own unless you explicitly want that for a low-risk task.
Where does my data go, and is it used to train someone's model?
That depends on the provider and plan we choose together. Business and API tiers from major providers generally don't train on your data by default, and for sensitive cases we can favor stricter or self-hosted options. We'll be explicit about the data path before building anything.
How is AI automation priced, and are there ongoing costs?
There's a one-time build cost driven by workflow complexity and how many integrations are involved, plus ongoing usage-based fees you pay the AI provider per request. Those per-use costs scale with volume, so we'll estimate them against your expected load in a free consultation rather than quote a blanket figure.