Use cases
The problems we see most often, and what we would build for each. These are patterns, not case studies — our shipped work is on the Work page. Tell us which one sounds like your business and we'll say whether it is worth building.
One team, many industries
7 industriesPlants run on data that is captured late, on paper, or not at all. Most of the value is in getting that data in cleanly, then letting AI read it.
The problem
Defects are caught by eye at the end of the line, after the material and machine time are already spent.
What we build
A vision model trained on photos of good and defective parts, flagging rejects at the station so a supervisor confirms them.
The problem
Breakdowns are found when the machine stops, and maintenance runs on a calendar rather than on condition.
What we build
Anomaly detection on the sensor, PLC or maintenance logs you already keep, sending an alert when a machine drifts from its normal pattern.
The problem
Shifts, output and downtime are written in registers and typed up days later, so managers decide on stale numbers.
What we build
QR or mobile entry on the floor, with an AI-written daily summary of output, downtime and attendance for each line.
The problem
Operators wait for a senior person to answer questions that are already written down in a manual nobody opens.
What we build
An assistant that answers from your SOPs and machine manuals, in the operator's language, and cites the page it used.
Margins are thin and support queues are long. AI earns its place where it takes repeat work off the team or stops money sitting in the wrong stock.
Recommended for you
Sneakers
Backpack
Jacket
The problem
Most tickets are order status, returns and sizing questions, answered by hand, often after the customer has given up.
What we build
An agent on chat or WhatsApp that reads your order and product data, answers the routine questions and hands the rest to a person with context.
The problem
Reorders are based on gut feel and last month's sales, so bestsellers run out while slow lines tie up cash.
What we build
A forecast per SKU from your sales history, seasonality and promotions, feeding suggested reorder quantities to the buyer.
The problem
New products go live with thin descriptions and missing attributes because writing them by hand does not keep up.
What we build
Descriptions, attributes and translations generated from supplier sheets and photos, queued for your team to approve.
The problem
Every customer gets the same homepage and the same campaign.
What we build
Recommendations and campaign segments built from purchase and browsing history, pushed into the tools your marketing team already uses.
Logistics runs on documents and status updates. Both are ideal work for AI, and both are usually handled by people copying from one screen to another.
The problem
Invoices, bills of lading and proof-of-delivery photos are keyed in by hand, with errors found at billing.
What we build
Extraction from scans, PDFs and phone photos straight into your TMS or ERP, with low-confidence fields sent for review.
The problem
Dispatchers plan routes from memory and spreadsheets, and re-plan by phone when something changes.
What we build
Suggested routes and load plans from orders, vehicle capacity and time windows, which the dispatcher adjusts and approves.
The problem
Customers and sales call operations to ask where a shipment is.
What we build
A chat or WhatsApp assistant that answers from live tracking data and escalates anything unusual to a person.
The problem
Delays are found when the customer complains.
What we build
Monitoring that flags shipments at risk of missing their window and drafts the customer update for the team to send.
The safe, fast wins are administrative: the front desk, paperwork and follow-ups. Clinical decisions stay with clinicians, and patient data stays under your control.
The problem
The front desk spends the day on the phone booking, rescheduling and chasing no-shows.
What we build
A voice or WhatsApp agent connected to your scheduling system that books, reschedules and sends reminders.
The problem
Intake forms and referral letters arrive as paper or PDFs and have to be retyped.
What we build
Extraction of forms and letters into structured records, with a short summary waiting for the clinician before the visit.
The problem
Clinicians write notes after hours.
What we build
A draft note from the consultation recording, which the clinician edits and signs. Nothing is filed without their review.
The problem
Claims are rejected for missing codes or documents, and resubmission eats staff time.
What we build
A check of each claim against payer rules and the supporting documents before submission, listing what is missing.
Deals are won on response speed and lost in paperwork. AI helps with both ends.
Listing written for you
The problem
Portal enquiries sit for hours, and by the time an agent calls, the buyer has spoken to three others.
What we build
An agent that replies to every enquiry within minutes, asks about budget, location and timeline, and books site visits into the agent's calendar.
The problem
Renewal dates, escalation clauses and obligations are buried in PDFs across the portfolio.
What we build
Extraction of key terms from every lease into one searchable register, with reminders ahead of renewals and escalations.
The problem
Listings go live with thin copy because writing them is nobody's job.
What we build
Descriptions drafted from property details and photos, in the tone and languages you sell in, for the agent to approve.
The problem
Maintenance requests come in by call, text and email, and urgent ones get missed.
What we build
One intake channel that sorts requests by urgency and trade, and routes them to the right vendor.
Regulated work is document-heavy and review-heavy. AI takes the first pass and people make the call, with an audit trail.
Transactions
The problem
ID documents, bank statements and applications are checked by hand, so onboarding takes days.
What we build
Extraction and cross-checking of application documents, with mismatches and missing items flagged for the reviewer.
The problem
Rule-based alerts miss new patterns and bury the team in false positives.
What we build
An anomaly model alongside your existing rules, ranking alerts so reviewers see the unusual ones first.
The problem
Staff search long policy documents, or ask compliance, for answers that are already written down.
What we build
An internal assistant that answers from your approved policies and circulars, and cites the clause it used.
The problem
Customers call about balances, statements and policy terms that the system already knows.
What we build
A chat or voice assistant that answers from account data after verification, and hands off to an agent for anything else.
Institutions answer the same questions thousands of times and grade the same assignments by hand. That is where AI frees up staff for teaching.
Intro
Practice
Quiz
The problem
Admissions and admin teams answer fees, deadlines and timetable questions all day, especially at peak season.
What we build
An assistant on the website and WhatsApp that answers from your prospectus and policies, and books counselling calls.
The problem
Teachers spend evenings marking and writing the same feedback again and again.
What we build
Draft marks and feedback against your rubric, which the teacher reviews and edits before anything reaches a student.
The problem
Students who are falling behind are noticed after the exam, when it is too late to help.
What we build
Flags from attendance, submissions and grades that surface students to their mentor while there is still time to act.
The problem
Quizzes, summaries and revision notes take faculty hours to prepare.
What we build
Quizzes and summaries generated from your own lecture material, for faculty to review before publishing.
Next step
Tell us the problem and we'll come back within one business day with a straight view on whether AI is worth it for you, and what it would take to build.