We build systems thatrun the busyworkso your team doesn’t have to.

Invoices, contracts and client files arrive by email or land in a shared Drive folder in no particular order. Someone has to open each one, work out what it is, rename it, move it to the right place and log it — a repetitive job that is easy to fall behind on.

A watcher triggers the moment a new file appears in Gmail or Google Drive. The document is parsed and handed to Claude, which reads its contents and classifies the document type. The scenario renames the file to a consistent naming convention, moves it into the matching Drive folder structure, and writes a row with its metadata into a Google Sheet. A confirmation notice is sent once filing is complete.

Every incoming document is classified, renamed and filed within seconds of arriving, with a searchable log kept automatically — removing an entire category of manual admin work.

Routine operational questions — status checks, numbers from a tracker, "what’s the latest on X" — interrupt whoever happens to know where that information lives. The answer usually exists already, somewhere in a spreadsheet or document, but retrieving it still takes a person’s time.

A team member asks a question directly in Slack. The scenario passes the question to Claude along with access to the relevant Google Sheets data, which interprets the intent, looks up the right rows or figures, and composes a direct reply in the same thread. Every exchange is logged for an audit trail.

Routine questions get answered instantly, at any hour, without pulling a team member away from their work — and the underlying data never has to be manually dug through.

Customer questions arrive at all hours across different channels, and response time directly affects how a business is perceived. Staffing a channel around the clock is expensive, but slow or inconsistent replies cost trust.

A customer messages the business’s Telegram bot. Claude reads the conversation, answers using the business’s own information, and keeps context across the exchange. When a question needs a human — a complex complaint, a sensitive request, anything outside its confidence — the scenario escalates the conversation to the team on Slack with full context attached, rather than giving a guessed answer.

Customers get an instant, consistent first response any time of day, while the team only steps in for the conversations that actually need a human — cutting response time without cutting quality.

Keeping a consistent publishing schedule across social channels takes steady writing time that most teams don’t have spare — and handing it fully to AI, with nobody checking the output, is a quick way to publish something off-brand.

On a schedule (or triggered by a new item in an RSS feed), the workflow briefs Claude with the brand voice and topic, which drafts post copy for review. The draft is posted to Slack, where a team member approves, edits, or rejects it. Only approved content is pushed to Buffer, scheduled across the relevant social platforms.

A steady content pipeline runs on autopilot while a human always has the final word before anything publishes — consistent output without the daily writing grind.

As a chatbot takes on more responsibility — support, sales qualification, scheduling — a single general-purpose prompt stops being good enough at any one of those jobs, and conversations lose context between sessions.

A message arrives through Telegram. Claude first classifies its intent, then hands the conversation to the matching specialist branch — a support agent, a lead-qualification agent, or a scheduling agent — each with its own instructions and tools. Conversation state is persisted in PostgreSQL so context carries across sessions, and anything ambiguous or high-value is escalated to the team on Slack for a human decision.

One entry point now reliably handles several distinct jobs at once, remembers context between conversations, and knows exactly when to bring in a human — a system that scales with the business instead of being rebuilt for every new use case.