A selection of the AI solutions we have built for our clients. Each project reflects our approach: understand the business challenge first, then apply the right technology to deliver measurable results.
Shipment Notification Monitoring
Every incoming shipment starts with a notification. Carriers and logistics partners send updates to a shared inbox, but those messages arrive in a different format from every sender, often bundle in unrelated routing detail rather than a single clean update, and change constantly as shipments are delayed, rerouted or rescheduled. Reconciling that inbox against the business's own tracking and scheduling system, and working out which updates actually needed action, was consuming close to a full-time role.
We built an AI agent that monitors the shared inbox continuously, extracting the relevant detail regardless of format or sender, and cross-references it against the business's live tracking and scheduling system. Instead of someone manually re-checking every notification against everything already in progress, the agent flags exactly which shipments need attention. It runs inside the customer's existing Microsoft Copilot Studio environment, so no new platform, infrastructure or security review was required to get it live.
Copilot Studio AI agents
- Freed close to half a full-time role from manual inbox monitoring and reconciliation
- Built and live in around six weeks, on top of systems already in place and already security-approved
- Lets the operations team react faster to changes and keep shipments moving without manual chasing
Loan Application Validation
Every loan application starts with an email. A broker sends across the application form together with around ten supporting documents, bank statements, signed privacy consent, identification and more, and none of it arrives in a standard shape. Checking a single application against the relevant product's criteria, the right statement period, a valid signature, a matching ABN, took an operator around 25 minutes. Multiplied across a large network of brokers, that manual checking absorbed close to two full-time roles.
We built an AI agent that reads the application and every attachment as they land, validating each one against the relevant product's criteria. Instead of an operator working through statements, consent forms and identification by hand, the agent produces a plain-language readiness report and a structured data snippet the operator can paste straight into the system used to create the loan record. It runs on Copilot Cowork and Copilot Studio AI agents already inside the customer's Microsoft environment, so no new platform, infrastructure or security review was required to get it live.
Copilot Studio AI agents
- Cut processing time from around 25 minutes to around 5 minutes per application
- Freed close to two full-time roles for higher-value assessment work
- Built and live in around six weeks, on top of systems already in place and already security-approved
Compliance Workflow and AI Validation
A fast-growing compliance business ran its work across three systems that did not talk to each other. Requests arrived in a shared Outlook inbox and were allocated by hand, Zendesk carried the tagging and ticket history, and Smartsheet tracked the projects. Every compliance check was done manually before an operator wrote the report that went back to the client. Workload was difficult to see across the team, and two overlapping tools cost around $15,000 a year in licensing.
We replaced Zendesk and Smartsheet with one application built around how the work actually runs. An email now converts into a project in a single step, carrying its attachments with it, and from there the work is allocated, tracked and reported on in one place. Vision-based AI validates incoming compliance work before an operator picks it up, so the manual check starts from a first pass rather than from nothing. Finished reports export back out as a client-ready document and a searchable history.
Azure AI Foundry, Docker, Node.js, React with Vite
- Removed around $15,000 a year of Zendesk and Smartsheet licensing
- Cut the administrative load so the team takes on more work without adding people
- Gave leadership one view of workload and progress across the team
- Built custom, so the workflow and the compliance know-how now sit with the business
Document Understanding
Intensely manual process to cross-check project documentation against requirements, tracking exceptions to ensure project success.
Evolving to AI Agents by starting with Q&A chatbots to enable source cross-checking of documents uploaded into SharePoint. Agents are dynamically selected based on the document type. Once analysed, the AI agent notifies the person who uploaded the document with actionable guidance.
Microsoft SharePoint, Azure AI Foundry, Azure AI Search, Llamaindex, GPT4o, o3-mini, Copilot Studio, Azure CosmosDB
ROI:
- Increased market value of this firm to assist in upcoming sale
- Reduced cost of servicing projects drives improved margins
- Reduced risk of projects impacting reputation and outcomes for stakeholders
Custom AI App Development
Build an AI app which is customer-ready and marketable in a short period of time.
We built Jummbo, an app that enables salespeople to quickly identify new prospects they can target. The AI agent guides the user to find suitable business types and locations, searches the internet, and then researches each prospect. It returns a spreadsheet complete with customer names, social media links and tailored cold email and cold call scripts ready to action.
GPT4o and GPT4o mini, MongoDB, NodeJS, React
Jummbo.ai is a fast-growing SaaS app designed for sales and marketing teams to accelerate their sales pipeline development. www.jummbo.ai
AI Vision (Vehicle Safety)
This company manages tens of thousands of deliveries each year. Although they have stringent safety standards, including manually inspecting each vehicle to ensure loads are securely fastened prior to departure, they wanted to leverage AI to minimise risk further.
We helped the customer understand how multimodal AI models can analyse images. Through a short proof of concept we showed them how to perform a red/amber/green assessment of photos collected at the warehouse prior to departure, identifying potential safety issues. We then helped their IT team in a "build with" capacity to apply these learnings to their technology stack.
GPT4o on Azure dedicated compute (PTUs)
The organisation has made significant advances across multiple domains in leveraging AI to reduce risk, improve safety and improve general operational performance.
Call Centre Uplifts
Call centre operators are seeking ways to leverage AI to improve all parts of their operations, including reducing average handling time (AHT) and better understanding the needs of their customers.
Solutions vary across customers. Typically organisations start with a Q&A knowledge chatbot internally, then extend it to web chat and voice channels to deflect calls and reduce AHT. Setting up a "data product" using speech transcriptions enables these organisations to better understand customer needs to inform better design of products, services and experiences.
AWS Connect, Salesforce Service Cloud Voice, and multiple Azure services
Improved customer satisfaction, reduction in cost to serve, improved marketing outcomes through a deeper understanding of customer needs.
Content Creation
The customer runs a popular directory related to the primary and secondary agricultural industries in Australia. They wished to enrich the directory with more information about each member listing without expending significant manual effort.
We developed a crawler for the organisation that researches each member's website, capturing an overview of the company, key products they sell, information about their region and links to their social media sites.
MySQL, OpenAI, NodeJS, Python, Langchain
Improved member listings deliver a better consumer experience. Higher quality content on the website has also delivered an increase in web traffic from search, and longer average time on site from visitors.
Start Your AI Project
Every project above started with a conversation. Explore our AI Quickstarts for structured engagement options, learn more about why organisations choose Hypergen, or get in touch to discuss your specific needs.
Frequently asked questions
What kinds of AI projects has Hypergen delivered?
Hypergen has delivered AI agents that monitor and reconcile shared inboxes in logistics and in business lending, document understanding for an engineering services firm, a custom AI application for sales prospecting, an AI vision proof of concept for vehicle load safety in building and construction, call centre uplifts across telco, financial services and energy, and AI content enrichment for an Australian agricultural directory.
How long does it take to build and deploy an AI agent?
Two of the AI agent builds in Hypergen's project portfolio went from start to live in around six weeks. One monitors shipment notifications for a logistics operator, the other validates broker-submitted business loan applications. Both were built with Copilot Cowork and Copilot Studio AI agents on systems the client already had in place and had already security-approved, which is what kept the timeline short.
Do we need new infrastructure to run an AI agent?
Not always. In the logistics and business lending projects Hypergen delivered, the agents run on Copilot Cowork and Copilot Studio AI agents inside the customer's existing Microsoft environment, so no new platform, infrastructure or security review was required to get them live. Other projects, such as document understanding for an engineering services firm, use Azure services including Azure AI Foundry, Azure AI Search and Azure CosmosDB.
What results have Hypergen's AI projects delivered?
The loan application validation agent cut processing time from around 25 minutes to around 5 minutes per application and freed close to two full-time roles for higher-value assessment work. The shipment notification monitoring agent freed close to half a full-time role from manual inbox monitoring and reconciliation. Both were built with Copilot Cowork and Copilot Studio AI agents and were live in around six weeks.
What technologies does Hypergen build AI solutions with?
Across its project portfolio Hypergen has built with Copilot Cowork and Copilot Studio AI agents, Microsoft 365 and SharePoint, Azure AI Foundry, Azure AI Search, Azure CosmosDB and Azure dedicated compute, GPT4o and o3-mini, Llamaindex, Langchain, Python, NodeJS, React, MongoDB, MySQL, AWS Connect and Salesforce Service Cloud Voice. Each project starts with the business challenge, then applies the technology that fits it.
Does Hypergen build custom AI applications as well as Microsoft Copilot solutions?
Yes. Hypergen built Jummbo, a custom AI application that helps salespeople find new prospects. The AI agent guides the user to suitable business types and locations, searches the internet, researches each prospect, and returns a spreadsheet with customer names, social media links and tailored cold email and cold call scripts. It was built with GPT4o and GPT4o mini, MongoDB, NodeJS and React.