What do we build with AI and automation?
We build LLM agents that do real operations and support work: Slack and WhatsApp bots connected to your systems, RAG over your own documents and data so answers come from your information — not the internet's — and process automation written in code, like Zapier but without its volume, logic or per-task cost limits. We use OpenAI and Anthropic models on top of our usual stack — Laravel, TypeScript and PostgreSQL — with the same engineering discipline we apply to any production system.
When does AI automation make sense (and when it doesn't)?
It makes sense when the process combines three things: high volume, fuzzy rules and text or documents in the middle. First-level support, email classification, answers over internal manuals and policies, data extraction from PDFs — there, AI pays for itself.
It doesn't make sense when the process runs on fixed rules (traditional automation is cheaper and more predictable), when volume is low (a person does it better) or when the cost of an error is catastrophic and there is no way to supervise it. Our rule is simple: only when the numbers add up — no AI theater. We build the business case with you before writing a line of code, and if it doesn't close, we say so.
How much does implementing AI cost?
A focused agent or automation starts at around US$5,000; systems with multiple integrations and critical flows can exceed US$50,000. There is a second number almost nobody shows you before selling: the monthly inference cost — what OpenAI or Anthropic charge per interaction. We estimate it with you during discovery, because an agent that saves US$2 per ticket but costs US$3 in tokens is not automation, it is charity toward your AI provider.
How do we control errors and hallucinations?
By assuming from the design stage that the model will get things wrong. That translates into concrete guardrails: the agent can only execute the actions we allow, sensitive decisions go through human approval, every response is traced and auditable, and quality is measured against a baseline defined before starting. If the agent answers over your data via RAG, access control travels with the query: each user sees only what they are entitled to.
Why Freshwork
We have spent more than 10 years building production software from Santiago, Chile, and we treat AI with that same discipline: metrics, maintenance and zero smoke. A useful agent almost always lives inside a larger system — which is why this service pairs naturally with our custom software development. If you work in financial services or logistics, where ticket and document volume is brutal, you probably have a case that closes. And as a nearshore team on US East Coast hours, we iterate with you in real time. Tell us about your process and we will reply with numbers within one business day.