The actual work.
Engagements describe the shape of the relationship. Products describe the tools. This page describes what we build inside those engagements: automation, integration, workflows, and the adoption that makes them stick.
What work should your team stop doing?
Most automation work in AI is not new in concept. Tickets get triaged. Email gets routed. Invoices get processed. Documentation gets drafted. What changes with AI agents is the unit of work they can credibly take on, and the rate at which those units get cheaper to deploy.
We build automation that removes the repetitive busywork your operators are doing today, so the operators can do work only they can do. Email triage, ticket classification, alert triage, invoice routing, content prep, data normalization, documentation drafting, compliance evidence collection. The unglamorous middle of the workday, made cheap.
Every automation we build follows the same principle: the agent should make the operator smarter, not dependent. If the human cannot do the task without the agent, we have built it wrong. Tool, not a crutch.
AI inside the systems you already run.
Integration is the practical work that turns a capable AI model into a useful AI agent inside a real business. Your operators already live in tools that work. Syncro. Microsoft 365. The ticketing system. The CRM. The wiki. Integration is how AI shows up inside those tools instead of asking your team to switch to a new one.
We default to three integration surfaces for every system we touch. A typed SDK so application code can drive it. A command-line interface so operators can drive it from a shell or a script. And an MCP server so AI agents can drive it through the Model Context Protocol. One API, three ways to address it. Syncro SDK is the canonical example. The same pattern works for any system with a real API.
Where vendors will not give us an API, we build the integration against what they do give us: webhooks, event streams, occasionally the export format. Honest about the seams, not pretending the integration is cleaner than it is.
End-to-end, not bolted together.
A workflow is a multi-step process that spans several tools and surfaces a decision where a human needs to make one. Onboarding a new client. Triaging a P1 incident. Closing a quarter. Releasing a feature. The work that lives between the systems instead of inside any one of them.
Most AI workflows fail because they were assembled from disconnected automations. Each step works in isolation; the handoffs break. We design workflows end-to-end, with the handoffs explicit, the data shape stable between steps, and the human-in-the-loop checkpoints named on purpose. The pipeline is the deliverable, not a side effect of stitching three integrations together.
Every workflow we ship runs against the Hexaxia frameworks. AGF defines what each agent in the pipeline is allowed to do. APF defines how it behaves at each step. ASIP governs how the workflow itself evolves. Three frameworks, one pipeline, no silent drift. See the frameworks.
The team has to actually use it.
The technical side of AI implementation is increasingly solved. The harder part now is the human side. People whose work is about to change. Leaders who need a frame to talk about it without overselling or understating. Security teams who need policy in place before someone pastes customer data into a public model. The adoption gap is people, not technology.
We do the education work that closes the gap from the human end. Operator-level training on the agents we ship and the agents your team is already using. Leadership-level framing on how to communicate AI rollout inside the company. Policy work on safe use, much of which sits jointly with the vCISO practice at our sister division so the AI-use policy lives inside the same framework as the rest of your security program. See the security carve-up.
This work is not a course you buy off the shelf. The sessions are built against the actual systems your team is going to use. The substrate we teach is the substrate we run on ourselves. The tool educates while it works.
All four practice areas, every engagement.
Automation, integration, workflows, and adoption are not separate offerings. They are how the engagement actually delivers value, no matter which engagement shape you start with.
Fractional CAIO
Strategy across all three.
The CAIO seat decides what to automate, what to integrate, and which workflows are worth designing end-to-end. Then hands off to your team or to Build-with for execution.
Build-with
Execution across all three.
We sit with your engineers and ship working automation, integrations, and workflow pipelines. Real code in your repo, governed by the same frameworks we run on internally.
Advisory
Judgment on any one.
A focused outside read on a specific automation decision, an integration architecture, or a workflow design. Written judgment with the reasoning, no retainer required.
Tell us what you are trying to automate, integrate, design, or roll out.
If we are the right fit for the work, we will say so. If we are not, we will tell you who is. We do this practice with a small number of teams on purpose.