AI Implementation
Forward Deployed Engineering is how PRESHos gets fitted to your operations. One PRESHai engineer, in your environment, customizing plugins, skills, knowledge stores, and approval policy to how your business actually runs. The deliverable is a deployed, governed, integrated PRESHos.
THE ENGAGEMENT MODEL
Forward Deployed Engineering is the engagement model PRESHai uses when the deliverable is a working PRESHos deployment that fits your operations specifically. The engineer is present for the work, not handing off a spec. The build happens in your environment, with your team, on the cadence your operation actually runs.
The relationship continues into Managed AI Ops when the run phase opens. The same engineer is part of the handoff so the operating layer continues to know the why behind every plugin, every skill, and every approval policy.
HOW THE ENGAGEMENT RUNS
Each phase produces a defined artifact, sets up the next phase, and contributes to the operating system you exit with. The cadence below assumes a single first workflow scope. Larger engagements run the phases in parallel with shared review gates.
The first two weeks are about getting honest about the work. The forward deployed engineer walks the operations with your team, names the first Agent Environment, defines the data and tool boundaries, and writes the success criteria the business will measure against. The exit artifact is a scoped engagement, not a deck.
Plugins, skills, knowledge stores, and tool grants are built to how your operation actually runs. The code lives in your environment from day one. The engineer pairs with your team on the parts that need their judgment. PRESHos itself becomes specific to your business as the build proceeds.
A scoped pilot runs inside the environment. Approval routing is on. The audit log is on. Evaluation rubrics tie to business outcomes, not model metrics. The engineer is present for the run, tuning permission levels and routing rules as the team gets a baseline of agent behavior.
Production cutover with a rollback path one click away. The same engineer hands off either to your team or to Managed AI Ops. The environment continues, the integrations continue, the audit log continues. The work becomes a capability you operate.
WHAT THE ENGAGEMENT DELIVERS
Continuity, customization, compliance fit, and an evidence trail. These hold across every phase and beyond cutover.
One forward deployed engineer carries the engagement from discovery through cutover. The plugins they wrote, the skills they shaped, and the knowledge stores they seeded all carry the same context. Continuity is the deliverable.
Plugins and skills are built against the platform's catalog with the right risk class, the right permission levels, and the knowledge attachments the work needs. Generic primitives get fitted to the operation the business actually runs.
Secret grants resolve through the GCP Secret Manager-backed vault. Approval routing follows the policy your compliance program already enforces. RBAC matches the org structure your team already uses.
The audit log, run history, usage tracking, and retrieval audit are on from the first pilot session. When the business asks how the work got done, the chain is intact because the runtime built it as the work happened.
WHERE THIS LANDS
MSP BUILD OUT
DISTRIBUTOR CO-OP
MANUFACTURER PARTNER ACTIVATION
BUILD WITH US, RUN WITH US
Most clients move from Forward Deployed Engineering into Managed AI Ops as one continuum. The engineer who built the system is part of the run phase, so the operating layer keeps its institutional memory.
When your team is ready to own the run phase themselves, the handoff is structured. Documentation comes from the platform itself. The plugins and skills are versioned. The audit log is the operating record. Your team picks up a system, not a black box.
Tell us where the work is stalling. We will scope an engagement, name the first environment, and walk the operations with you.
