The operating problem
AI pilots often stall after the first prototype because the real work lives across ERP data, files, inboxes, approvals, custom software, training, and change management.
Solution Playbook
Forward-deployed engineering
Embed OpenTeam engineers with your team as technologists and trusted advisors to define use cases, prototype quickly, connect systems, and move the first agentic workflows into production.
Milestone-based
bootcamp, prototype, controlled pilot, production handoff, and adoption measures instead of open-ended hourly consulting
10
systems and data sources in the playbook
5
repeatable workflow steps before approval
5
control points for human review
Customer profile
Who this is for.
Enterprise customers and complex operators that have real AI use cases, messy systems, approval requirements, and a need to move beyond demos into production workflows.
The OpenTeam outcome
OpenTeam FDEs work with the customer team through a milestone-based rollout: discover the workflow, design the agent architecture, connect systems, build the first skills, run a measured pilot, and hand off a production runbook.
Connected systems
What OpenTeam connects for this workflow.
Available connectors and built-on-request integrations are separated on purpose, so customers can see the current starting point and the custom scope for rollout.
4 Available 6 Built on request
OpenTeam workspace
AvailableTeam runtime, shared history, files, approval controls, connected accounts, and reusable skills for the deployment.
Agent architecture
Built on requestRetrieval, orchestration, policy-based routing, tool invocation, evaluation harnesses, and lifecycle observability for enterprise-ready agents.
Provider abstraction
Built on requestIntegration layers across OpenAI, Anthropic, Google, local models, and customer-approved endpoints so workflows can route to the right model or tool.
Everyday work apps
AvailableEmail, calendars, documents, files, spreadsheets, and shared folders used by operators in the workflow.
Business systems
AvailableAccounting, commerce, CRM, legal, messaging, and operational systems that already exist in the connector catalog.
ERP, databases, and custom APIs
Built on requestCustomer-specific read paths, export flows, database views, API access, and approval-gated writeback.
Private infrastructure
AvailableReserved hosts, dedicated VPCs, private cloud, or customer-controlled deployment boundaries for regulated or strategic use cases.
Cloud-native delivery
Built on requestContainerized services, microservice boundaries, serverless jobs, and event-driven handoffs when the workflow needs scalable customer-side infrastructure.
SDLC automation
Built on requestUse of enterprise agentic development tools such as OpenAI Codex, GitHub Copilot, and Claude Code to accelerate implementation, testing, and documentation.
Training and handoff
Built on requestOperator enablement, admin runbooks, acceptance criteria, support cadence, onsite or embedded working sessions, and adoption reporting for the first production workflows.
Daily workflow
How the work runs.
These are the repeatable steps a customer can turn into a Team workflow, skill, or managed review process.
01
Run workshops and bootcamp
Start with one high-value workflow, the real operators, the source systems, design workshops, proof-of-concept scope, code-with sessions, and a concrete definition of production success.
02
Map the system and agent boundary
Identify available connectors, custom APIs, exports, database views, model providers, routing policy, security constraints, and which actions must remain approval-gated.
03
Build the first production path
Create the Team workspace, connect approved systems, build retrieval and tool paths, package reusable skills, and turn repeated operator steps into a controlled workflow.
04
Pilot with real work
Run the workflow against real records with human review, dry runs, exception queues, and metrics for accuracy, latency, safety, cost, and adoption before expanding scope.
05
Handoff the operating model
Document runbooks, support ownership, training notes, approval policy, observability, SDLC automation patterns, and the backlog for the next workflow.
Forward-deployed engineering
What the FDE team does.
FDE is the hands-on path for enterprise AI workflows that need more than a demo. OpenTeam engineers work beside the customer team, turn one real workflow into a production-grade agentic system, and leave behind the operating model to run it.
01
Discover the operating workflow
Embed with the customer team to understand the real operators, source systems, approvals, exceptions, and production success criteria.
Design workshopsProof-of-concept scopeStakeholder alignment
02
Design the production agent
Translate the workflow into an agent architecture with retrieval, orchestration, policy routing, tool use, provider choices, and evaluation criteria.
Agent architectureProvider routingSafety and cost metrics
03
Build inside customer systems
Connect approved apps, custom APIs, exports, databases, and infrastructure, then package the repeated operator steps as reusable Team skills.
Connector pathsTeam skillsCloud-native delivery
04
Pilot, measure, and hand off
Run controlled pilots against real records, measure accuracy, latency, safety, cost, and adoption, then hand off runbooks and support ownership.
Real-work pilotObservabilityProduction runbook
Deployment artifacts
What gets handed over.
Artifacts are grouped around the rollout, so customers can see how strategy, implementation, and launch readiness fit together.
P
Plan
Shared definition of the workflow, system boundary, approvals, and rollout milestones.
Forward-deployed engineering rollout plan
Workflow and system boundary map
Connector and data-access scope
B
Build
Technical design and implementation assets for the first production-ready agentic workflow.
Enterprise agent architecture
Provider abstraction and routing design
Team skills and approval runbook
L
Launch
Operational evidence, automation patterns, and handoff material for production adoption.
Evaluation and observability plan
Prototype and pilot backlog
SDLC automation plan
Production handoff report
Approval controls
Where people stay in the loop.
FDE is scoped as a milestone-based deployment package, not unlimited staff augmentation.
Custom connector and data access should begin read-only unless the customer approves write scopes.
Model providers, policy routing, data residency, safety metrics, and cost controls are reviewed before production rollout.
External sends, ERP writes, file changes, and operational actions remain approval-gated until the customer accepts the control model.
Onsite work, embedded cadence, travel, security review, and longer allocation are quoted with the deployment plan.
Rollout path
How to start.
1
Start with a short bootcamp or discovery sprint around one workflow with real data and named operators.
2
Use workshops, proofs of concept, and code-with sessions to turn the workflow into a testable agentic system.
3
Build a prototype that proves the workflow can use approved systems, model providers, tools, and reviewable outputs.
4
Run a controlled pilot with acceptance criteria, evaluation metrics, training, and support ownership.
5
Move to production only after the customer approves access scopes, writeback rules, runbooks, and rollout metrics.
Deployment support
Forward-deployed engineering when the workflow needs hands-on rollout.
Some teams can start with the playbook and existing connectors. Enterprise teams can add a forward-deployed engineer package when the workflow requires custom system access, onsite discovery, user enablement, or a measured production launch.
Map
Confirm the source systems, permissions, approval owners, exception paths, and success measures for this workflow.
Build
Connect available apps, create custom connector or export paths, and turn repeated operator steps into reusable Team skills.
Launch
Run a controlled pilot, train the first users, document the runbook, and graduate the workflow from prototype to production use.
Related playbooks
Build the next workflow around it.
Custom systems
ERP and custom integrations
Use forward-deployed engineering to connect ERP, custom databases, APIs, exports, and operating documents around a production workflow.
Private AI infrastructure
Canadian sovereign AI deployment
Deploy private AI workflows for Canadian organizations that need data residency, customer-owned infrastructure, local model options, and reviewable human approval.
Finance AI
Financial services AI workflows
Use OpenTeam to research filings, summarize market signals, reconcile financial records, draft review packs, and keep finance work source-backed before approval.
OpenTeam prepares operational work inside a Team workspace. Customers remain
responsible for professional review, compliance decisions, and final approvals.
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