Systems integration
Many vendors, one design.
We architect, connect and test the whole, and take single-point accountability for how it behaves together.
Systems thathold together.
Fio Labs is a systems integrator for the age of AI. We bring infrastructure, networks, software, applications and AI into one system that works, and we stay accountable for it after it ships.
Systems rarely fail in the parts. They fail between them.
Every organization runs on components chosen at different times, by different people, from different vendors. Each one works. The trouble starts where one meets the next: a handoff nobody owns, a permission nobody reviewed, a change in one place that quietly breaks another.
AI sharpens this. A model is a component that changes underneath you. An agent is a component that acts. A pilot looks convincing because it runs in a protected corner, with tidy data and nothing at stake.
Gartner expects more than 40% of agentic AI projects to be canceled by the end of 2027, citing rising cost, unclear value and weak risk controls.1 Those are problems of integration as much as of models.
One system, from the wire to the workflow.
We design and connect every layer a modern system depends on, across vendors. We treat the newest layer like the rest: something to engineer, test and own.
Compute, storage, connectivity and the security that runs through them. Chosen for fit, sized for what comes next.
Cloud services, data platforms, identity and the tools between them. Selected on merit, not on who sold them to us.
Business applications connected through clean interfaces, with data that arrives complete, current and trusted.
Models, retrieval, agents, and the permissions that govern what they can see and do. Evaluated before launch, watched after.
The work itself: who does what, where a person steps in, and how a result becomes an action. Integration ends here, not at the API.
Three disciplines, one accountable team.
AI systems
Models and agents built into the system, not beside it.
Retrieval grounded in your data, tool access with least privilege, and evaluation that runs before every release.
Workflow engineering
We start from the work, not the demo.
We map how a result moves from a model to a decision to an action, and where a person should stay in the loop.
Five steps, each one visible.
Map
We trace the system as it really runs: components, owners, handoffs and failure points.
Design
We settle the architecture and its trade-offs in writing, so choices are visible and, where possible, reversible.
Build
We connect, configure and write what is missing, in small releases that each work on their own.
Prove
Before anything reaches users, we test the seams: load, failure, permissions and, for AI, evaluation against real cases.
Run
We stay on. Monitoring, drift, cost and change control stay with the team that built it.
We stay accountable after go-live.
A launch is the start of a system's life. Models drift, vendors change terms, usage grows, and a new tool arrives that has to fit. We keep watch over the whole, so your team inherits something supported, not something orphaned.
- Monitoring across every layer.
- Drift and cost kept in view.
- Change handled in the open.
How we think about the work
Integration is design. The seams deserve the same care as the parts.
AI earns its place. We use it where it is the best answer, and say so when it is not.
Plain is a feature. Fewer moving parts, clear owners, written decisions.
Independent by design. We choose components for fit.
What this looks like
Illustrative scenarios. These describe typical situations, not client work.
We connect the sources of truth, limit what the assistant can do, log every decision, and route anything above a set threshold to a person.
We fix identity, queuing and retries, build an evaluation set from real documents, and design the fallback to a person.
We define the interfaces first, test the failure cases, and assign an owner to every handoff.
Bring us the system you cannot quite make work.
Tell us what you run, what you are adding, and where it hurts. We will reply with questions first and a view second.