We don't build things
that look good.
We build things that
change how a business runs.

The Problem

The client operated four completely separate storefronts — eBay, Amazon, Shopify, and TikTok Shop — managed entirely by hand. When a product sold on Amazon it stayed purchasable on every other platform until someone noticed and manually corrected the stock count. Overselling was a weekly occurrence: orders they couldn't fulfil, accumulating negative feedback, and mounting account warnings from Amazon's seller metrics team.

Listing a new product meant logging into four separate platforms, recreating the listing from scratch on each, manually mapping categories, and hoping nothing drifted out of sync. A growing catalogue of 1,400 products on a system designed for ten.

What We Built

A custom real-time inventory synchronisation engine connecting all four platforms via their native APIs. The moment a sale lands on any platform, a webhook fires, the central inventory database updates, and stock levels are pushed to all remaining storefronts within seconds. One sale on Amazon equals an instant deduction everywhere else — no lag, no manual check.

Product creation was rebuilt entirely. The seller creates a product once in a master dashboard we built — maps attributes and category per platform — and the system handles simultaneous listing creation across all four channels. Variations, bundles, and pricing tiers are managed centrally.

The Problem

Junior attorneys and paralegals were spending 6–12 hours per case researching precedents — manually trawling public legal databases, reading through hundreds of irrelevant verdicts to find the handful that actually applied to the scenario in front of them.

The firm wanted something their attorneys could query in plain English — the way you'd ask a very experienced colleague: "What's the precedent on X in circumstances like Y, and has it ever gone the other way?"

The hard constraint: the system had to run entirely on the firm's own infrastructure. No data to third-party servers. No OpenAI API. No cloud LLM. Fully private, fully controlled. Client case data is privileged — that isn't negotiable.

What We Built

Phase 01 — Data acquisition & structuring. We scraped 400,000+ publicly available legal verdicts from authorised public databases — the complete public record relevant to the firm's practice areas. Every document was cleaned, deduplicated, and structured: jurisdiction, date, parties, charge type, outcome, key legal principles extracted and tagged.

Phase 02 — Vector search architecture. We built a vector embedding system using a locally-hosted embedding model. All 400,000 documents were indexed into a private Qdrant vector database with a hybrid retrieval layer combining semantic search with hard metadata filters.

Phase 03 — The attorney interface. A private, ChatGPT-style interface accessible only inside the firm's network. An attorney types a natural language query. The system retrieves the most relevant verdicts, and a locally-hosted LLM synthesises a response — citing specific cases, explaining the legal reasoning, surfacing conflicting precedents, and flagging jurisdictional differences. Every response shows its sources. The model only synthesises from retrieved documents.

The Problem

The client received 750,000 products from a supplier via API. The data arrived completely unstructured — product names inconsistently formatted, descriptions missing or duplicated, categories absent, attributes scattered across freeform text fields. None of it was usable for a storefront without significant manual processing.

With no meta titles, no meta descriptions, no sitemap, and no structured categorisation, the site was virtually invisible to search engines. They were getting roughly 200 organic clicks per month on a catalogue of three-quarters of a million products. The opportunity gap was enormous — but the data problem had to be solved first.

What We Built

We built a background processing pipeline that consumed the raw API feed and passed every product through a multi-stage AI model. The model extracted structured fields — category, subcategory, brand, attributes, dimensions, materials — from unstructured text. Products were then classified into a clean category taxonomy we designed for the store's architecture.

For each of the 750,000 products, the model generated a unique meta title and meta description calibrated to the product's actual attributes — not templated filler. A multi-layer XML sitemap was generated and submitted, ensuring every product URL was indexable and crawlable at scale.

The supplier API sync runs weekly — new products are processed through the same pipeline automatically and go live without any manual intervention. Removed or discontinued products are unpublished cleanly.

The Problem

The client's finance team was drowning in manual expense processing. Receipts arrived by email, WhatsApp photo, and physical paper. Someone had to open each one, decide what category it belonged to, enter it into a spreadsheet, and reconcile it against the bank statement at month-end. Errors were common. Expenses were submitted late, miscategorised, or lost entirely.

Management had no real-time visibility into spending patterns. Monthly reports were produced manually by the finance team and arrived 2 weeks after month-end — by which point the decisions that could have been made with that data had already been made without it.

What We Built

We built a full expense management system with a mobile-first submission interface. Employees photograph a receipt — the system uses OCR and an AI classification model to extract the amount, vendor, date, and VAT, and categorise it automatically against the company's own expense categories. Submissions take under 2 minutes with zero manual data entry.

For management, we built a real-time dashboard showing spend by category, department, and individual — updated as expenses are submitted. Weekly automated reports are generated and emailed to relevant stakeholders every Monday morning without any manual input. Anomaly detection flags unusual expense patterns automatically.

The Problem

The client ran a wholesale company selling to both retail customers and tiered wholesale accounts (Gold and Platinum tiers with different pricing). Their existing setup required staff to manually check which customer category an order came from and apply the correct pricing — a process prone to error and abuse. Retail customers sometimes got wholesale pricing. Wholesale customers sometimes overpaid.

The POS system they had was a basic till — it processed transactions but gave them nothing useful. No weekly performance breakdown, no profit analysis per product line, no visibility into which wholesale accounts had outstanding credit. Chasing credit was done by someone with a phone and a spreadsheet. It consumed hours every week.

All products were managed only in the POS — nothing was live on their website. The two systems were completely separate, meaning online visibility was zero.

What We Built

We built a modern, integrated platform that replaced the POS, the website product catalogue, and the credit management workflow simultaneously.

The POS front end is built for speed — clean interface, fast transaction processing, automatic tier detection. When a customer account is scanned or looked up, the system identifies their tier (retail, Gold wholesale, Platinum wholesale) and prices the entire basket accordingly — automatically, with no staff decision required. Retail customers on the website see retail pricing. Gold and Platinum wholesale customers log into the same site and see their tier's pricing on every product, with preorder functionality enabled for out-of-stock items.

Every product in the POS is automatically synced to the website. New products added to the POS appear on the site within minutes. Stock levels update in real time across both channels.

For administration, the system generates a weekly performance report covering revenue by channel, profit margins by product line, top and bottom performers, and outstanding wholesale credit balances — emailed automatically every Monday. Wholesale accounts with overdue credit receive automated reminder emails at configurable intervals, with escalation logic built in. No staff member needs to chase a payment manually.