PROBLEM

A 9-person B2B team received around 120 inbound leads a month through a website form and a shared inbox. Roughly two thirds were students, job seekers or out-of-scope requests, and the ones worth talking to waited an average of 31 hours for a first reply because someone had to read everything manually.

SOLUTION

An assistant that reads every inbound lead from both channels, scores it against a written ideal customer profile, explains the score in one sentence a human can argue with, creates the CRM record, and drafts the first reply.

PROBLEM

A subscription product received about 900 tickets a month, and roughly 40% were the same eleven questions. Answers varied by agent, and first response time on a weekend stretched past a day.

SOLUTION

A support agent that drafts answers grounded in the help centre, always cites the article it used, and refuses to guess — anything it is not confident about goes straight to a human with the context attached.

PROBLEM

A 140-person company kept policies in Drive, specs in Confluence and decisions in Slack. New joiners asked the same questions for months, and half the answers they got were out of date.

SOLUTION

A private assistant that answers internal questions with links to the source, and never shows a document the asking person could not open themselves.

PROBLEM

A three-room clinic took most of its bookings on WhatsApp. Messages arrived in the evening, nobody replied until the next morning, and the owner estimated a handful of appointments a week were lost to a faster competitor.

SOLUTION

A WhatsApp agent that answers the common questions, checks real calendar availability, books the appointment and hands over to a human the moment anything is unusual.

PROBLEM

Small finance teams re-type invoice data from PDFs into their accounting tool. On a sample of 200 real invoices, manual entry averaged just over three minutes each and produced errors on roughly one in twenty.

SOLUTION

A pipeline that extracts structured invoice data, validates it against rules the finance team can read, flags anything suspicious and queues the rest for approval.

PROBLEM

A store with 4,000 SKUs had supplier-provided descriptions on most products. They read identically to three competing stores and converted badly, but rewriting them by hand was never going to happen.

SOLUTION

A generation engine that writes on-brand copy per product from the real attributes, with a human review step before anything reaches the storefront.

PROBLEM

Clinics, studios and salons take most bookings over WhatsApp. Requests arrive in the evening, staff answer them the next morning, and by then some customers have booked elsewhere.

SOLUTION

An assistant that reads the request, offers three openings taken from the live calendar, books the one the customer picks, confirms it and reminds them a day before. Anything that is not a booking goes to a person.

PROBLEM

A company website answers most questions somewhere, but visitors do not find them. The few visitors with real intent read a page, leave, and the company never learns who they were.

SOLUTION

An assistant on the site that answers from the company's own pages with links to them, says plainly when something is not covered, and asks for an email once a visitor shows real intent.