Most vendors arrive holding an agent and looking for a problem. We arrive with five questions and a whiteboard. The business model dictates the architecture. Where AI helps, AI ships. Where it doesn't, we say so.
Every operations team has a stack of work that hurts. Some of it is hand-touched because nobody automated it. Some of it is hand-touched because automation was tried and failed. Some of it is hand-touched for a good reason — the judgment call belongs to a human.
The first job is telling those three apart. Then deciding which of the first two is worth changing this quarter. Then deciding what shape of automation actually fits — process redesign, plain workflow tooling, agentic AI, or a human staffing change. Agentic AI is one tool in that toolbox, not the whole toolbox.
Before automating a workflow, ask if it should exist. Many "AI use cases" are reports nobody reads or approvals nobody respects. Delete first, automate second.
Agentic systems fail loudly when the prompt is wrong and silently when the data is wrong. We design every workflow with a verification gate: a human, a check, or a system-of-record write that has to succeed.
The model will change. The vendor will change. The integration won't. We build the orchestration layer to outlast the models behind it. When Claude 5 drops, your workflow doesn't get rewritten.
The intro is one call, 30 minutes, no deck. We ask five questions, in order. The answers tell us whether there's a real engagement to scope or whether you should keep your money and try a $50 SaaS subscription first.
Before architecting a single agent, we map where AI actually creates leverage in your specific business model. There are only four shapes of leverage. Most "AI projects" fail because they're chasing the wrong one for the company they're inside.
Where does the dollar enter the business? Where does it leave? What's the bottleneck in scaling either side? AI lives where the bottleneck is.
Cost out (replace expensive manual). Speed up (compress cycle time). Capacity up (handle more volume per person). Accuracy up (fewer errors that cost money downstream). Each is a different architecture.
Compliance boundaries, system-of-record locks, contracts with vendors, regulated activity trails. These constrain the design before we pick a model or a tool.
NOW we pick the model, the orchestration, the integration pattern, the human-in-loop checkpoints. The architecture is downstream of the economics, not the other way around.
This is why our deployments ship in a quarter instead of dying after a six-month "AI strategy phase." We skip the strategy phase. The first three stages take a week. The build is the rest.
The market is loud right now because AI pitches are cheap and easy. The reason most of them don't ship is they skipped the first three stages and started building. We do the boring work of asking what should exist before we build it.
Each workflow at imorbis.com/workflow is the methodology above run against a specific mid-market vertical: logistics, finance, sales, CS. Open architecture, no email gate, no signup. Read them. Run them on Orbis SaaS yourself. Or, if your team doesn't have the cycles, this is what we ship for you.
The same architectures we ship for paying customers, free. The architecture is the cheap part. The implementation is where engagements live.
Browse the workflow library→Bring the workflow that hurts most. We'll tell you whether it's worth automating, what shape that should take, and what would break first. Written recommendation in 48 hours. You keep the doc either way.