Three ways I can help, and how each one runs.
Most engagements start because something has outgrown its tooling. Below is what each line of work actually delivers, the signals that it is the right one, and roughly how long it takes.
Software & ERP Systems
Business systems that stay correct when they are under pressure.
Services, APIs and ERP work for problems where being approximately right is not good enough — money, stock, ledgers, anything with an audit trail. That includes Laravel ERP builds covering projects, HR, assets and inventory, where the hard part is rarely any single module — it is keeping one shared model of the business that every module can agree on.
Typically 3 — 10 monthsDomain and data model design, documented well enough to argue with
ERP build or extension, with an integration layer that keeps the vendor’s format out of your business model
REST API integration with the systems you already run, built to fail safely when the other end is down
Continuous reconciliation that proves the two systems agree, and says so when they do not
Runbooks and handover, so the team owns it without me
“Someone re-keys the same document into two systems every day.”
“A batch job decides whether your numbers are right, and it runs overnight.”
“Your ERP and your finance system disagree and nobody knows which is wrong.”
“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.