From Slide Decks to Software: How Forward Deployed Engineering Ships Production AI in Weeks

In Brief

As enterprises struggle to move beyond AI proofs-of-concept, AllCloud has launched its Forward Deployed Engineering (FDE) practice. By embedding specialized engineering pods directly inside customer environments, AllCloud bridges complex multi-cloud ecosystems, specifically AWS and Salesforce, to deploy production-ready agentic AI systems in weeks rather than quarters.

Key Takeaways

•  The FDE Model Shift: Moving past traditional strategy slide decks, embedded FDE pods sit directly alongside internal teams and take direct accountability for shipping live production AI.
• Ecosystem Alignment: AllCloud’s FDE practice includes partner alignments with both AWS’s $1B FDE initiative, Salesforce’s FDE Partner Network for Agentforce and strategic partnership with Anthropic. 
• Cross-Platform Integration: Embedded pods act as connective tissue between back-end AWS data lakes (plant operations/supply chain) and front-end Salesforce applications (customer demand/field service).
• Core Pillars: Engagements combine Infrastructure & DevOps (AIDLC), Data & AI (Claude and Amazon Bedrock/RAG), Business Applications (Salesforce), and Business Acumen (ROI & change management).
• Outcome-Based Engagements: Every engagement is scoped around an agreed business outcome and delivers working production software, team training, and a complete documentation handoff.

For years, the technology consulting industry has operated on a familiar rhythm: assess, roadmap, and present. But when it comes to Artificial Intelligence, the traditional model is no  longer enough.

Our customers don’t want another slide deck. They want a short, focused start that produces something real, and then engineers who stay until it is running in production. They want engineers sitting next to their teams, shipping working systems in weeks rather than quarters. The future of AI delivery is a brief discovery that produces a working artifact, followed by an engineering team embedded inside the customer’s environment. It’s a highly specialized engineering team embedded directly inside a customer’s environment, staying completely accountable until the system is running in production.

This shift is happening across the entire cloud ecosystem. Recently, AWS announced a $1B Forward Deployed Engineering (FDE) organization to embed small engineering pods directly inside enterprise customers. Simultaneously, Salesforce launched its FDE Partner Network to accelerate Agentforce implementations at scale.

Operating at the intersection of ecosystems, AllCloud is an AWS FDE pilot partner in North America, a member of the Salesforce FDE Partner Network, and is building its FDE bench against Anthropic’s Claude certification track. In addition, we have formally launched our own Forward Deployed Engineering (FDE) practice. While an embedded delivery model isn’t entirely new to how we build and deliver, what has dramatically changed is the market urgency. Enterprises are moving past the desire for proofs-of-concept; they need production AI systems and a team that remains fully accountable until those systems are operational.

AllCloud’s senior technical leaders and engineering teams actively embed alongside our lighthouse customer pods as Forward Deployed Engineers. Being an FDE means sharing full accountability for the exact same outcomes as the customer’s internal team, and AllCloud doesn’t consider the job done until the system is actively running in production.

Our FDE practice is an additive motion to our traditional professional services. Rather than delivering ad-hoc advisory or isolated projects, we embed cross-functional, highly targeted engineering pods directly inside customer teams. Every engagement is built on four core pillars:

1. Infrastructure & DevOps: Agentically delivered, cloud-native AWS architecture and CI/CD delivered through AllCloud’s AI-Driven Development Lifecycle (AIDLC) framework.
2. Data & AI:  Building the core of agentic systems, Claude on Amazon Bedrock, MCP, and Claude Code inside the AIDLC workflow. Within AWS – evaluation harness, observability, guardrails, tool use and MCP.  Retrieval-augmented generation (RAG) pipelines, and Amazon Bedrock live model deployments as part of the foundational core of GenAI.  
3. Business Applications: Salesforce-led agentic deployments (including Agentforce) that put AI directly into the hands of end-users.
4. Business Acumen: Outcome mapping, ROI analysis, team training, and change management to ensure the technology translates to measurable business value.

Every FDE engagement is value-based, delivering a working production system, team training, and a complete documentation handoff.

To understand why the FDE model is so critical, consider a large-scale industrial enterprise running dual ecosystems on AWS and Salesforce.

Take, for example, a major industrial manufacturer that has spent over six years undertaking a deep digital transformation alongside our teams at AllCloud. Their journey highlights the technical depth and multi-platform maturity required for modern AI deployments:

• A Robust Salesforce Evolution: Beginning with foundational portals for partners and customers, their Salesforce footprint expanded to encompass CPQ, Service, Marketing, Sales, Commerce, Field Service, Bid Process Management, and a complete digital customer experience overhaul.
• A Scalable AWS Foundation: Concurrently, the organization built a formidable AWS infrastructure focused on a comprehensive data strategy, continuous platform enhancements, data lakes, and intelligent document processing.

Despite possessing mature data lakes for analytics and a modernized customer experience on the front end, highly complex industrial enterprises often face a common hurdle: seamlessly connecting back-end supply chain operations with front-line customer demand.

The true strategic gap lies in bridging these distinct environments to move from a reactive posture to a fully predictive, unified enterprise. Without intelligent, cross-platform connective tissue, enterprises struggle to unlock the full potential of their data across plant operations and customer touch points.

A traditional consulting approach to this challenge would likely generate separate integration roadmaps, one for the AWS infrastructure and another for the Salesforce customer portal.

An AllCloud FDE pod takes a fundamentally different path. We embed directly into the customer’s internal teams to actively build solutions inside their live environment.

By deploying autonomous AI agents capable of communicating securely across platforms, the embedded FDE pod connects the AWS-hosted data lake directly with plant operations and customer demand signals in Salesforce.

This creates a seamless flow of intelligence:

• Predictive Alignment: Customer demand updates in Salesforce automatically inform inventory and plant scheduling models on AWS.
• Frictionless Operations: Supply chain constraints are reflected immediately in customer-facing quote and service workflows.
• Accelerated Time-to-Value: Instead of spending six months architecting integrations, the AI agents are engineered, tested, and pushed into production in as little as 6 weeks.

By acting as the connective tissue between AWS data infrastructure and Salesforce business applications, our Forward Deployed Engineering practice ensures that enterprise AI investments translate into measurable, production-ready outcomes.

We look forward to sharing more playbooks and lighthouse outcomes as our FDE pods continue shipping production AI systems across our customer base.