AI & Workflow Automation
Most AI projects fail at the same point: the demo works and nothing changes, because the automation lives in a separate dashboard nobody opens. We build automation into the tools people already use. That means the messaging app your customers are already on, the inbox your team already watches, and the systems that already hold your data.
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Conversational agents and assistants
Agents that handle a real task end to end, such as taking a booking, answering from your own documents, or triaging an enquiry. Scoped to what they can do reliably, with a clear path to a human for everything else.
Messaging and channel integration
WhatsApp, web chat, email and the channels your customers already use. The automation goes where the conversation already happens rather than asking anyone to move.
Document and data extraction
Turning invoices, forms, delivery notes and PDFs into structured records. Extraction is checked rather than trusted, with confidence surfaced so a person reviews the cases that need it.
Process and system automation
The connective work between systems that were never designed to talk. Scheduled jobs, event triggers and integrations that remove the manual step somebody currently does twice a day.
How we work
Find the task worth automating. Not the most impressive one, the most repeated one. We look at what a person actually does each day before deciding what a system should do instead.
Establish what correct looks like. Automation without a definition of correct is a demo. We agree the measure first, so there is something to hold the result against.
Build the narrow version. One task, done reliably, in the place it already happens. Scope grows once the first one is trusted.
Verify against real cases. Run against actual historical inputs, not curated examples, and measure where it fails as well as where it works.
Keep the human path open. Every automation has a defined escalation. Nothing gets silently handled badly.
Our tech stack
- Models and orchestration
- Anthropic Claude, OpenAI GPT, function calling and structured output validation, retrieval-augmented generation (RAG)
- Data and retrieval
- vector search (MongoDB Atlas Vector Search, Pinecone), document parsing and OCR pipelines
- Channels and integration
- WhatsApp Business API, webhooks, REST APIs, n8n / Make / Zapier for lightweight orchestration
- Application
- Node.js, TypeScript, Next.js, queue-backed job processing
Why work with us
We automate the task, not the demo.
The measure of success is that a person stops doing something, not that a model produced impressive output once.
We define correct before we build.
Anything that produces output can look right. We agree what right means first.
We keep a human in the path.
Confidence is surfaced and escalation is designed in, so the failure mode is a person reviewing something rather than a customer receiving something wrong.