When no product fits your documents, your systems and your approval rules, we design and build the AI that does. It runs with human review where it matters, logs what it decides, and is handed over so your team can run it.
Custom development earns its cost when the work is specific to you. The common cases look like this: staff rekeying invoices, statements or forms from PDFs into an accounting or ERP system; several systems that do not talk to each other, so someone copies data between them every week; reports assembled by hand from spreadsheets each month; exceptions and approvals that live in email threads; or sensitive records that cannot be pasted into a public chatbot.
Size has little to do with it. A business with one finance officer and a group with dozens of entities can have the same problem. What matters is whether the process repeats often enough, follows rules that can be written down, and draws on data you can reach.
If you are still deciding which problem to tackle first, an AI strategy engagement or our free AI readiness assessment is the better place to start. If the process is mostly rules with no judgement involved, plain process automation may do the job without a model at all.
Most projects combine two or three of these. Each card links to a guide on how that kind of system works.
Invoices, statements, contracts, delivery dockets and forms read, classified and checked before the data reaches your systems. Each field carries a confidence score, and anything uncertain goes to a person.
Multi-agent document processingAgents that match transactions, chase missing information, draft replies in your voice and route exceptions to the right approver, working inside the systems your team already uses.
Xero reconciliation agentPlain-English questions answered from your finance and operational data, with the query and source shown so a manager can check the number before it goes in a board pack.
Plain-English BI agentsAI connected to ERP, practice management and line-of-business applications, including the ones with limited APIs, without replacing the system underneath.
Legacy system integrationAssistants that answer from SharePoint and Teams content need the permissions underneath them cleaned up first. We pair these builds with a Copilot readiness review.
Copilot readinessSelf-hosted models for work where documents cannot go to a public AI service, including asset-heavy operations with plant data, which is one specialty among the others here.
Private AI infrastructureThe model is one step among six. Most of the build effort goes into validation, the review queue and the integration, because that is where a system earns trust from the finance or operations team who rely on it. The same pattern is in Carbonly, the founder's own product, which uses a 7-phase multi-agent pipeline to extract energy data from utility bills that arrive in many different formats.
Running the system after launch is part of the design. Our post on operating AI agents in production covers monitoring, model changes and cost control, and managed AI services can carry that load if your team would rather not.
See what this would take in your business
A free 30-minute review on Microsoft Teams. Bring the process you want fixed and leave with a clear first step.
Each phase ends with a decision point, so you can stop or change direction before committing to the next one.
Our proof of concept framework explains phase two in detail. Where the build touches several applications, it runs with our system integration work. If you have developers who want to build it with us, team augmentation puts a senior person inside your team instead.
Agreed at scoping and adjusted after the proof of concept.
The governance note follows the approach in our posts on AI agent governance and human override and Privacy Act compliance and AI.
Our build vs buy decision framework walks through these questions in more depth.
Our documented client work is the Tier 1 infrastructure carbon reporting case study, where standardised templates and Power BI dashboards replaced manual ESG data collection.
Carbonly is a multi-agent document extraction system for carbon and energy data. SupportAgent is a self-hosted investigation tool that runs local models through Ollama, so no data leaves the customer's infrastructure. Before both came a workforce management SaaS.
Solve8 is based in Brisbane, and builds for organisations across Australia, with on-site sessions in South East Queensland when they help.
Our principal consultant brings 18 years of enterprise delivery experience from projects for BHP, Rio Tinto and Senex Energy, delivered with previous consulting employers. These companies are not Solve8 clients. You work directly with senior people, not a sales team handing you off to juniors.
See what this would take in your business
A free 30-minute review on Microsoft Teams. Bring the process you want fixed and leave with a clear first step.
Book a 30-minute scoping call. Bring an example document or a description of the process, and we will tell you whether custom AI is worth building and what the first phase would look like.