Ten systems that run without us in the room.

Reads the script image and pulls out patient, NHS number, script number and every line item. Validates the NHS checksum and cross-checks the EPS barcode against what it read. Resolves products and account codes against CIN7, reads the order back for confirmation, and raises a draft order once the customer says yes. Anything it is not confident about goes to a review queue instead of being guessed.

Orders arrive the way customers already communicate, no portal, no app, no training. Staff stop retyping prescriptions into the ERP, and the only cases that reach a human are the ones that genuinely need judgement.

Handles model, spec and availability questions in natural conversation, captures the lead, and routes it to the right rep with the full thread attached. Rapid-fire messages are debounced into one coherent turn, so a customer typing three lines in a row gets one good answer rather than three fragments.

Enquiries get an immediate, accurate answer at 11pm on a Sunday. Reps open a conversation that already has context instead of a bare phone number and a callback request.

Pulls line items and pricing off supplier documents and customer quotes, handling South African number formatting correctly, R7 200,00 is seven thousand two hundred, not seventy-two thousand. Produces a short brief for each lead so a rep can pick it up cold.

Removes the most error-prone step in quoting, which is a human retyping numbers off a PDF. The errors it removes are the expensive kind, an order of magnitude on a price.

Extracts fields from a photographed ID, then applies deterministic checks the model cannot talk its way around, the 13-digit checksum, date-of-birth and citizenship digit consistency, and a barcode cross-check. A calibrated decision engine combines the vision read and the arithmetic into a score with stated reasons.

Vision models are confident when they are wrong; arithmetic is not. Pairing them produces a decision you can put in front of a compliance officer and defend line by line.

Takes an unsorted pile of scanned documents, classifies each one by type, extracts the fields that type carries, validates identity numbers, applies the claim rules, and assembles the pack, with a report naming exactly what is still missing.

Claim packs fail on omissions, and the omission is usually found weeks later by the insurer. This finds it before submission, when it costs an email rather than a resubmission.

Continuously scrapes and consolidates listings, maps every vehicle to its M&M code so like is compared with like, enriches against TransUnion data, and serves pricing to both a web dashboard and a mobile client.

Turns a market scattered across a dozen listing sites, each with its own naming, into one comparable price picture you can actually make a buying decision on.

Extracts entities and relationships from years of email, resolves which mentions refer to the same client, deal or product, and stores the result as a knowledge graph rather than a pile of embeddings. Access is enforced per advisor, so each user queries only their own book. Compliance packs are generated from the same source.

Institutional knowledge that lived in individual inboxes becomes searchable across the firm, without widening who can see what. When two clients share a surname, the graph separates them by adviser and account, not by guessing at similarity.

Aggregates club registries, venue directories and competitor booking platforms across regions, then reconciles the duplicates and naming variants that make the same venue look like three different clubs. Tracks which platform already owns each venue.

Answers the two questions that decide a go-to-market plan: where is the market actually concentrated, and who already has the relationship.

Pulls mail, calendar and tasks through Microsoft Graph, tracks clients and portfolios, and matches assets under management across naming variants so the same holding recorded three ways still totals once. WhatsApp conversations land in the same inbox.

One place for the client relationship instead of four, and an AUM figure the advisers trust because they can see how it was derived.

Reads pick PDFs, reconciles outstanding backorders against CIN7, and produces the paperwork the warehouse needs. Covered by an automated test suite, so a change to the pick-list format is caught before it reaches the floor.

The people who need it can run it. No terminal, no Python install, no developer on standby, which is the difference between a tool that gets used and one that does not.