What’s actually slowing this PC down?
Pick the symptom - the matching free tool is one click away.
AI-native engineering companies that sell forward-deployed engineers (FDEs) offer embedded, hands-on delivery for enterprise AI work. As of early October 2026, the provider descriptions reviewed for this article share one core offer: help the customer choose a workflow worth changing, work alongside its staff, connect models or agents to enterprise data and systems, build and deploy a production system, and leave the customer able to run it.
The packages differ. Some are tied to a single software platform, some are technology-neutral engineering pods, some bundle training and consulting, and some are large partnerships that pair a software platform with industry and change-management expertise. The figures these providers publish are their own claims, and no comparable pricing or standard contract terms are published across them.
As an Amazon Associate I earn from qualifying purchases.
What each provider offers
The table below sets out how each provider describes its FDE offer, the scope it names, and the figures it publishes. Where a provider does not publish a figure, the cell says so.
| Provider | Model as described | Scope named | Published figures (owner, year) |
|---|---|---|---|
| Atlassian | Senior engineers embed with customer teams as co-engineering partners. Builds on Rovo, Teamwork Graph and the customer’s Atlassian environment, within existing permissions, access controls and data policies. | Scoping a first use case or moving a stalled pilot into production. Focus on high-friction workflows in software delivery and service management. Typically requires a clear workflow, a dedicated customer partner and access to the relevant systems. | 80+ production AI agents built and deployed; about 12 weeks to measurable business value; 100+ enterprise customers (Atlassian, 2026) |
| ADEL | Embedded senior FDEs, AI experts and data experts. Technology-neutral; clients keep their model and platform choices. | FDE and AI engineering consulting, embedded Forward Deployed AI Pods, FDE training, and agentic software engineering. Stated aim is to define difficult AI problems, build production-ready systems and transfer capability. | Not stated |
| OpenAI Deployment Company | A diagnostic to find valuable opportunities, then selection of priority workflows with customer leadership and operating teams, then FDE work inside the organization. | Designing, building, testing and deploying production systems connected to customer data, tools, controls and processes. | More than $4 billion of initial investment; approximately 150 FDEs and deployment specialists expected from the announced Tomoro acquisition (OpenAI, 2026). The acquisition was subject to customary closing conditions, including regulatory approvals, at announcement. |
| AWS Forward Deployed Engineering | Engineers embed with the customer’s business, engineering and security teams. Emphasises agentic development and customer self-sufficiency. | Building and deploying production AI systems on customer data, under the customer’s governance and processes. Engagements can leave systems, runbooks, architecture documentation and trained internal champions. | $1 billion investment in its FDE organization (Amazon/AWS, 2026). This is a company investment figure, not a customer price. |
| ServiceNow and Accenture (May 2026 announcement) | A purpose-built pod for each engagement, organised around a customer-specific value chain, combining platform-native, AI-native and industry expertise. | Moving enterprise agentic AI from pilot to production, with access to pre-built agents and workflows on ServiceNow’s AI Platform. | More than 300 pre-built AI agent skills and agentic workflows (ServiceNow and Accenture, 2026) |
| Accenture and Microsoft (March 2026 announcement) | A joint FDE practice that combines Microsoft’s AI platform and technology with Accenture’s industry workflows, process redesign, change management and global deployment capability. | Designing, building and operationalising AI across the enterprise. | Not stated |
| Taller Technologies | AI-native engineers it calls “Frontier Engineers,” who redesign workflows, build systems and stay through production adoption. | Includes Echo, an agentic enablement layer, and Chiron, a shared development environment. Both are Taller products. | Not stated |
| Forward Labs | Senior engineers embedded in client operations, connecting frontier models to customer data, tools and controls. | Handing over production systems for customer teams to run. | Not stated |
How an engagement typically runs
Provider descriptions follow a broadly similar sequence. Each provider words it differently, and none publishes a fixed timeline, so treat the order below as the common pattern rather than a contract standard.
#1 Best Overall
- Find the workflow. OpenAI describes a diagnostic that finds valuable opportunities, followed by selecting priority workflows with customer leadership. Atlassian says a customer can bring a high-value workflow or ask for help finding one. Taller starts from the workflow, the people who operate it and the result the business wants.
- Work inside the operating team. AWS embeds engineers with the customer’s business, engineering and security teams. The ServiceNow and Accenture model places a pod around the customer’s value chain. The goal is to move from observing the process to changing it.
- Connect the system to the business. Models or agents are wired to customer data, tools, permissions and existing processes. The sources describe this in general terms; the specific connections depend on the customer’s environment.
- Build, test and deploy. OpenAI describes designing, building, testing and deploying production systems. Atlassian describes building and deploying AI into live workflows, not handing over a recommendation.
- Hand over and operate. Providers name the artifacts customers may receive, covered in the next section.
What the work includes beyond building
Integration and production controls
Every provider that describes production work says the system must operate within the customer’s permissions, data policies and governance. Atlassian names permissions, access controls and data policies. AWS names customer data, governance and processes. The controls themselves, such as data handling rules, evaluation methods, human oversight, monitoring and security guarantees, are not described in detail in the public material. These belong in each provider’s contract and technical review.
Handoff and capability transfer
Handoff is a stated goal for several providers. AWS says engagements can leave behind systems, runbooks, architecture documentation and trained internal champions. ADEL and Forward Labs describe transferring capability or handing production systems to customer teams to run. Atlassian describes co-engineering that builds lasting capacity. None of these describes a guarantee of autonomy after the engagement ends. Ask which artifacts, training and support are included in writing.
Rank #2
What customers have to bring
FDE offers depend on customer participation. Providers name the following inputs, but none publishes a standard minimum staffing level.
Quick wins for a faster PC:
Scan for outdated or missing drivers - takes under a minuteDriver Scan →Repair Windows errors before they cause bigger problemsFix Now →- A specific workflow with a clear owner and a measurable outcome.
- A dedicated counterpart who can make decisions about the process.
- Access to the relevant systems, data and tools.
- Participation from business, engineering and security stakeholders.
Reading the published figures
The numbers above come from the providers themselves, and they measure different things:
Rank #3
- Atlassian’s 80+ agents, about 12 weeks and 100+ customers are figures shown on its service page in 2026. They are vendor-displayed rather than independent measurements, and the page does not say how “measurable business value” was defined or measured.
- AWS’s $1 billion is an investment in its own FDE organization. It says nothing about what a customer will pay or how long a project will take.
- OpenAI’s $4 billion and roughly 150 FDEs relate to a venture and an announced acquisition. The acquisition was subject to closing conditions at announcement, so confirm its current status before relying on it.
- ServiceNow and Accenture’s 300+ agent skills describe a catalogue of pre-built components on ServiceNow’s platform, not results delivered to a customer.
No independent benchmark compares these providers, and no published figure shows that one provider will achieve the same result for a new customer on a different workflow.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.In the providers’ own words
- Francessca Vasquez, Vice President of Frontier AI Engineering and Services at AWS: “The AWS FDE model is different in three key ways: it is agentic-first, it compresses timelines from months to days, and it is designed so customers are self-sufficient when a deployment ends.” (AWS announcement)
- Gary Brantley, Chief Information Officer of the National Football League, on the AWS engagement: “To create new digital experiences for our fans, the NFL partnered with AWS FDE and got engineers building alongside our team to launch into production in just weeks.” (AWS announcement)
- Manish Sharma, Chief Strategy and Services Officer at Accenture: “AI value does not come from technology access but from the ability to convert it into sustained business impact.” (Accenture and Microsoft announcement)
- Atlassian’s service page: “Atlassian FDEs are software and applied AI engineers who embed with your teams to build production solutions, not just recommend them.”
What is not yet established
The public material leaves several questions open, and these are the ones to resolve with any provider before committing:
Rank #4
- Pricing, contract terms and minimum project size. None is published on a comparable basis.
- How long a typical engagement takes to reach production. Atlassian’s 12-week figure is its own claim, not a standard.
- Eligibility requirements, geography and availability, which can differ by provider and region.
- Whether a provider’s offer fits your existing platforms. Platform-tied offers suit organisations already using that vendor’s systems. Technology-neutral offers suit organisations that want to keep their model and platform choices.
When comparing providers, ask each one for the same details: a scoped workflow, named deliverables, the deployment environment, data and model boundaries, governance design, the measurement method and its baseline, customer responsibilities, the operating handoff, and references for the same use case.
Quick Recap
Product prices and availability are accurate as of the date/time indicated and are subject to change. Any price and availability information displayed on Amazon at the time of purchase will apply.
