Your whole business, in one system.
A custom ERP built around how the business actually works, without an SAP-sized project. Then the infrastructure it runs on, and the automation around it. Below is what each one delivers, the signs it is the right fit, and roughly how long it takes.
Custom ERP & Business Software
One system for the whole business, built around how it actually works.
A custom ERP that puts projects, HR, recruitment, staff records, assets, inventory and field check-ins in one place — built around how your business already works, without an SAP-sized project or the licence bill that comes with one. The hard part is rarely any single module; it is keeping one shared picture of the business that every module agrees on. An existing Laravel ERP can be extended or taken over, and the systems you already run get connected, not replaced.
Typically 3 — 10 monthsAn ERP built module by module, starting with the one that hurts most
Customisation or takeover of an ERP you already have
Connections to the systems you already run, built to fail safely when the other end is down
Dashboards and reports on the same data, so nobody rebuilds a spreadsheet by hand
Deployment, hosting and support after launch, plus a handover your team can own
“Someone re-keys the same information into two systems every day.”
“The business runs on spreadsheets and three apps that never agree.”
“An SAP quote came back bigger than the problem it was meant to solve.”
“You have a system nobody wants to change because nobody is sure what it does.”
Platform & DevOps
Infrastructure your engineers use without filing a ticket.
Internal platforms, deployment pipelines and cloud architecture built so that the common path is self-service. The goal is never the technology — it is removing the queue between a developer having an idea and being able to test it.
Typically 4 — 12 monthsInternal platform or developer portal with a real API underneath it
GitOps deployment and ephemeral environments per pull request
Build and CI overhaul — caching, sharding, reproducibility
Cost visibility, attributed to the teams and changes that cause it
“Getting an environment takes days and involves asking a person.”
“Your pipeline is slow enough that people start another task while they wait.”
“The infrastructure team has become a ticket queue and stopped building.”
AI Automation
AI systems that are trustworthy enough to keep in production.
Agents and pipelines that do real operational work — triaging incidents, reading documents, handling the repetitive middle of a process. Built with strict schemas, confidence thresholds, human checkpoints and an evaluation suite, because the hard part was never getting a demo to work.
Typically 2 — 7 monthsAutomation design with the human checkpoints deliberately placed
Structured extraction or agent loops with validation at every boundary
An evaluation harness built from your real cases, run before every change
Confidence-based routing so people spend time only where the machine is unsure
“People retype information between two systems every day.”
“You have an AI prototype that impressed everyone and nobody trusts in production.”
“Your on-call rotation spends its first twenty minutes reconstructing a timeline.”
How an engagement runs
No surprisesA conversation
Thirty minutes on what is actually breaking. If I am not the right person I will say so and try to point you at who is.
A written proposal
Scope, the approach, what I would deliberately not build, and what it costs. Short enough to read in one sitting.
Build in the open
Working software early and often, with your team involved throughout. No reveal at the end — you see it as it forms.
Handover that holds
Documentation, runbooks and enough pairing that the system belongs to your team, not to me. That is the actual finish line.