Work / Case Studies

How we approach real problems.

Six case studies in enterprise operations, AI agents, RAG, document AI, CRM and SaaS. Each one sets out the problem, what we built, the technology and the result.

About these case studies

The projects on this page are concept projects: solution designs and prototypes that show how we approach common business problems. They are not client engagements, and each "Result" describes what the design is built to achieve, not a measured client outcome. Client case studies can be shared under NDA on request.

Concept ProjectIntelligent Operations Platform · Logistics

NEXUS

A real-time control tower that unifies shipment data from carriers, warehouses and IoT sensors, with AI that flags delays early.

01 · Problem

A regional logistics operator tracks shipments across several carrier portals, a warehouse system and shared spreadsheets. Delays are discovered when customers call, and staff spend hours each day chasing status updates.

02 · What We Built

An event-driven data platform that ingests carrier APIs, EDI messages and IoT telemetry into one live view. A machine-learning model predicts arrival times, and an AI agent drafts customer updates for exceptions, which staff approve before sending.

03 · Technology

PythonKafkaPostgreSQL / TimescaleDBReactXGBoostLLM agentAWS

04 · Result

Designed to give operations one real-time view of every shipment, surface delays hours before customers notice, and remove most manual status chasing.

Concept project: result describes the design target, not a measured client outcome.

  • Live shipment map and exception queue
  • Predicted ETA with confidence range
  • AI-drafted customer notifications with approval step
  • Carrier performance dashboards
Concept ProjectRAG Knowledge Assistant · Professional Services

FLUX

An internal assistant that answers staff questions from policies, procedures and past work, with a citation for every answer.

01 · Problem

Staff at a professional services firm search shared drives, the intranet and long PDF manuals to answer routine questions. Answers are inconsistent, and compliance is wary of any AI tool that can't show its sources.

02 · What We Built

A retrieval-augmented generation (RAG) assistant that respects existing document permissions, answers in English or Traditional Chinese, and cites the source paragraph. An evaluation set of real staff questions measures accuracy before each release. The assistant runs inside Microsoft Teams.

03 · Technology

Azure OpenAI / ClaudeLlamaIndexAzure AI SearchPython / FastAPIMicrosoft TeamsEntra ID

04 · Result

The prototype returns cited answers in seconds. It is built so accuracy can be measured and signed off by compliance before rollout, and so staff only see content they already have access to.

Concept project: result describes the design target, not a measured client outcome.

  • Permission-aware retrieval
  • Citations linked to the source document
  • Bilingual questions and answers
  • Accuracy and usage reporting
Concept ProjectIntelligent Document Processing · Finance Operations

LEDGER

AI that reads supplier invoices in English and Chinese, checks them against purchase orders and posts them to the ERP.

01 · Problem

An accounts payable team keys thousands of supplier invoices into the ERP each month from PDFs, scans and photos. Manual entry causes errors, duplicate payments and late-payment penalties.

02 · What We Built

A document pipeline combining OCR with LLM extraction, then validation against purchase orders and the vendor master. Each field gets a confidence score, and low-confidence invoices go to a review queue. An agent emails suppliers about missing information, and approved invoices are posted through the ERP API.

03 · Technology

PythonAzure Document IntelligenceLLM extractionPostgreSQLReact review UIERP API

04 · Result

Designed so clean invoices flow straight through without manual keying, people review only the exceptions, and every decision has an audit trail.

Concept project: result describes the design target, not a measured client outcome.

  • English and Chinese invoice extraction
  • Automatic three-way match
  • Exception review queue
  • Supplier follow-up agent
Concept ProjectCustom CRM · B2B Distribution

HARBOUR CRM

A CRM built around how a distributor's sales team actually sells: on the road, over WhatsApp and with live stock and pricing.

01 · Problem

Sales reps at a B2B distributor manage customers through WhatsApp and personal spreadsheets. Management has no pipeline visibility, and quotes take days because stock and pricing sit in the ERP.

02 · What We Built

A custom CRM with accounts, pipeline and quoting linked to live ERP stock and price lists. A WhatsApp Business integration logs conversations against the customer record. AI summarises calls and chats and suggests next steps, and a mobile app lets reps work from anywhere.

03 · Technology

ReactReact NativeNode.js / NestJSPostgreSQLWhatsApp Business APILLM summariesAWS

04 · Result

Designed to give management a single view of every customer and the pipeline, and to cut quote turnaround from days to the same day.

Concept project: result describes the design target, not a measured client outcome.

  • Mobile-first pipeline and quoting
  • WhatsApp conversation logging
  • AI call and chat summaries
  • Live ERP stock and pricing
Concept ProjectMulti-Tenant SaaS · Freight Forwarding

ORBIT

A multi-tenant SaaS platform for freight forwarders to manage bookings, documents and tracking, with a portal for their own customers.

01 · Problem

A freight software vendor installs a separate copy of its system for every client. Onboarding takes weeks, upgrades are painful and hosting costs grow with each new customer.

02 · What We Built

A multi-tenant SaaS platform with tenant isolation at the database level, per-tenant configuration and branding, booking and document management, shipment tracking, a customer portal, SSO and subscription billing.

03 · Technology

TypeScriptNestJS microservicesPostgreSQL row-level securityKubernetes (EKS)TerraformStripe

04 · Result

Designed so a new operator can be onboarded in days instead of weeks, every customer runs the latest version, and hosting cost per tenant is predictable.

Concept project: result describes the design target, not a measured client outcome.

  • Tenant isolation and custom branding
  • Customer self-service portal
  • SSO and role-based access
  • Usage-based subscription billing
Concept ProjectHeadless Commerce · Retail

MONO

One catalogue, one inventory and consistent promotions across web, app and stores for a multi-channel retailer.

01 · Problem

A retail brand runs separate systems for its website, mobile app and stores. Inventory is out of sync, promotions differ by channel and the website is slow on mobile.

02 · What We Built

A headless commerce architecture with shared product, inventory and pricing APIs feeding a Next.js storefront, a React Native app and store POS. It includes personalised recommendations and local payment methods.

03 · Technology

Next.jsReact NativeHeadless CMSNode.jsRedisPayment gateways

04 · Result

Designed to keep stock and promotions consistent on every channel, with a storefront built to pass Core Web Vitals on mobile.

Concept project: result describes the design target, not a measured client outcome.

  • Unified catalogue and inventory APIs
  • Fast, SEO-friendly storefront
  • Personalised recommendations
  • Local payment methods

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