We're an early-stage startup — no named clients to show yet. But these are realistic examples of the work we do, based on common problems businesses face. If yours looks similar, we can probably help.
AI Chatbot
The problem: Support team was answering the same 50 questions over and over, every day.
What we built: A chatbot trained on product documentation and FAQs, embedded in the website. Escalates to a human when it's not confident.
Result: Handles the majority of routine queries without human involvement. Support team focuses on complex cases.
AI Chatbot
The problem: Staff spent hours hunting through shared drives and old emails to find policy information.
What we built: An internal chatbot that indexes the company's documents and lets staff ask questions in plain English.
Result: Answers in seconds instead of hours. Reduces reliance on tribal knowledge.
Workflow Automation
The problem: Someone was spending 3–4 hours every Monday pulling data from multiple systems and formatting a report.
What we built: A pipeline that pulls from the relevant data sources, transforms the data, and generates a formatted report automatically.
Result: Report is ready first thing Monday morning without anyone touching it.
Workflow Automation
The problem: Manually entering data from PDFs and invoices into a system — slow, error-prone, boring.
What we built: An AI extraction pipeline that reads the documents, pulls out the relevant fields, and loads them into the system.
Result: Hours of data entry replaced by a pipeline that runs in minutes.
Data Pipeline
The problem: Data sitting in three different systems with no easy way to combine or query it.
What we built: A pipeline that pulls from each source, standardises the data, and loads it into one place — updated on a schedule.
Result: Single source of truth. Reports that used to take days to assemble run in seconds.
Data Pipeline
The problem: Wanted to build an AI application but the data wasn't in a usable state — inconsistent, duplicated, missing fields.
What we built: Assessed the data, cleaned and structured it, built a retrieval index and data quality checks.
Result: Data layer that the AI application could actually rely on. Project unblocked.
Got a similar problem?