AI automation services you won't have to babysit

Your marketing team knows they should publish more. They don't have the hours. Here's what we build: a workflow that connects to the Ahrefs API, pulls your keyword gaps and ranking opportunities, then generates research briefs for each topic. A second workflow takes those briefs, researches the subject using AI, writes a first draft, and pushes it to your CMS as a draft post.

Set the whole thing on a CRON schedule. Monday morning, your editor opens Sanity and finds five draft posts waiting for review, each targeted at a keyword your competitors rank for and you don't. The AI did the research and the first draft. Your writer does the thinking and the polish.

Each step in the pipeline retries independently. If the Ahrefs API rate-limits you, that step waits and retries. If the LLM call fails, it tries again without re-fetching the keyword data. Deploy a code update while a draft is mid-generation? The workflow finishes on the old version.

If you operate in multiple markets, the same pipeline can generate localised versions of each post. The workflow takes your approved English draft, translates it, adjusts examples and references for the target region, and pushes each version to the correct locale in your CMS. One editorial review produces content for every market you sell into.

When someone submits a contact form on our site, a workflow kicks off within seconds. It extracts the domain from their email, scrapes their company's website for context, then sends everything to Claude. The AI researches the company, looks at what they do, checks for recent news or funding rounds, and generates a structured brief. That brief lands in our Slack within a minute of the form submission.

The entire thing is about 40 lines of TypeScript. Each step uses a "use step" directive, so if Claude's API is slow or Slack returns a 500, that individual step retries without re-scraping the website. We use this ourselves, every day.

For clients, we extend this pattern to push enrichment data into their CRM, score leads based on company fit, and trigger different follow-up sequences depending on what the AI finds. A SaaS company can automatically route enterprise leads to their sales team and self-serve leads to a product tour.

Workflows are great for request-response pipelines. Some problems need an agent that lives in the background, watches for changes, decides what to do, and acts. Stale comparison pages that need fact-checking against a competitor's docs. Brand citation alerts in ChatGPT and Perplexity. A llms.txt file that should regenerate every time the product changes.

We build these on the same Vercel stack as our workflows: typed tool definitions, structured outputs via the AI SDK, Vercel Sandbox for any code execution the agent needs to run, and durable scheduling so the agent fires reliably. Schedules trigger them. Remote webhooks trigger them. Other agents trigger them.

Roboto's own CMS migration pages are kept current by a weekly background agent that scrapes the source CMS's docs, diffs against our YAML, and opens a pull request when something is out of date. The agent we built for ourselves is the same agent we ship to clients. If you want one watching your competitor's pricing, your product changelog, your brand citations across the AI search surfaces, we build it on the same foundation.

An agent without evals is a demo. The moment you connect it to real data, a prompt change can break it silently. We build eval pipelines alongside every production agent: golden datasets of inputs your agent should handle, deterministic checks for the parts you can grade with code, and LLM-as-judge grading for the parts you can't.

Every prompt change runs through the eval suite before it ships. Every model upgrade gets scored before you switch over. Regressions get caught before your editor reviews a bad draft or your sales team gets a wrong brief. The eval suite is the closest thing agentic systems have to a test suite, and it's the difference between "the agent worked last week" and "the agent works".

We treat evals as a productised add-on. They can be the starting point of an engagement if you already have an agent in production that isn't trustworthy, or they can be built alongside a new agent from day one.

A Roboto FDE engagement puts one or two senior engineers into your codebase with the same access as your own team. Shared Slack channel, repo write access, on-call posture for the agents we ship. Weekly demos, daily-or-better async updates, and a documented playbook your team owns when the engagement winds down. We transfer knowledge as we go, so nothing depends on a rushed handover at the end.

The cadence matters because agentic systems aren't ship-and-walk-away projects. Prompt iteration runs daily once the agent hits real traffic. Tool integrations break in ways nobody predicts at scoping. Editorial review needs context only your team can give. An external vendor on a weekly call is too slow for that loop.

Engagements run a minimum of eight to twelve weeks so the build, observe, and iterate cycle has room to play out. Most settle into a rolling monthly retainer once the first systems are live and the next ones are queued.

CMS migrations, headless Shopify builds, and Contentful implementations all ship cleanly as projects with handoff boundaries. Agentic work doesn't. The output is your voice, your data, and your customer-facing automation, and it changes weekly based on what real usage reveals.

That's why FDE applies to agentic workflows and AEO engagements, not to every service Roboto offers. If you're after a fixed-scope build with a clean handoff, our project teams handle that. If you're after an agent or workflow that needs to keep getting better in production, you want the embedded model.