Forward Deployed AIEnterprise MVP, telecom

Field OperationsAI Helpdesk

My roleForward Deployed AI Architect

ContextField technician operations

System classForward Deployed AI

FocusPermissions, knowledge, escalation

Brief

The problem

Field technicians moved between fragmented knowledge, job systems, and back-office support while working on-site. They needed one operational entry point for role, job context, and escalation.

Constraints

The field context

01The primary experience lived inside a technician-facing Android application with a supervisor web surface.

02Authorization depended on SSO claims, role context and controlled supervisor impersonation for training.

03Knowledge arrived from databases, Confluence, SharePoint, PDFs, Word documents, incident records and images.

04The system needed grounded answers, citations, and safe escalation.

Architecture

The request path

The assistant starts with the technician's job and permissions, then selects knowledge, workflow, or human escalation.

Field Operations / logical architectureSanitized view

01ExperienceTechMobile + supervisor UI

02Manager agentIntent, role, job context

03Domain agentsJobInsights, TechAssist, Supervisor

04Knowledge layerS3, extraction, search / vectors

05OperationsRBAC, citations, HITL, telemetry

Decisions

Routing and escalation

Route by intent and authorization together

The manager selects a domain path only after establishing what the user is allowed to access.

Separate domain agents, shared operational context

Job data, technical guidance and supervisor workflows have different tools and policies but share one request envelope.

Ingestion as a repeatable pipeline

Source-specific extraction feeds normalized chunks, metadata, permissions and index updates instead of ad-hoc uploads.

Escalation is a designed outcome

When confidence, permissions, or source coverage are insufficient, the system moves the technician to the right human workflow.

Production

Controls in the field

01Permission-aware retrieval and source metadata propagation.

02Nightly batch ingestion with failure visibility and reprocessing.

03Citation requirements for technical guidance.

04Supervisor and technician behavior separated through SSO claims.

05Operational telemetry across routing, retrieval, tool use, and escalation.

Forward deployed engineeringCustomer workflow mappingLangGraphMulti-agent routingRAGSSORBACAWSOpenSearchpgvectorDocument ingestionHuman escalationObservability

Outcome

Technicians receive job-aware guidance or a handoff inside the tools they already use.

Relevant problem?

Bringing AI into a difficult operating workflow?

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