Do these 3 things before closing this tab:
1Fix the driver behind crashes, sound loss and screen glitches2Clear out junk files and repair common Windows errors3Scan for outdated or missing drivers - takes under a minuteiTechGuides is reader-supported. When you buy through links on our site, we may earn an affiliate commission. As an Amazon Associate I earn from qualifying purchases. Learn more
AI-native engineering firms that sell forward-deployed engineering (FDE) work usually offer a small team of senior engineers who sit with the customer, choose a valuable business workflow, connect AI models or agents to the customer’s data and systems, deploy a production system, and hand it over to the customer’s own staff. The packages differ. Some are tied to one vendor’s platform, some describe themselves as technology-neutral, and some pair a platform with industry consulting and change management. The figures these companies publish are their own claims. As of early October 2026, the public pages examined here do not disclose comparable pricing, standard contract terms, or independently verified time-to-value, so buyers have to test each offer against their own workflow.
What the engagement involves, step by step
Providers use different labels, but their descriptions follow a similar sequence. Not every provider describes every step, so treat this as the common pattern rather than a guaranteed scope.
- Choose the workflow and the outcome. OpenAI’s Deployment Company describes a diagnostic to find valuable opportunities, followed by selection of priority workflows with customer leadership and operating teams. Atlassian says a customer can bring a high-value workflow or ask for help finding one, and that its FDEs can move a stalled pilot into production. Taller starts with the workflow, the people who operate it, and the result the business wants.
- Work alongside the customer’s teams. AWS embeds engineers with customer business, engineering, and security teams, working with the customer’s own data, governance, and processes.
- Connect models or agents to enterprise data and systems. Atlassian says its FDEs work within permissions, access controls, and data policies. Forward Labs describes connecting frontier models to customer data, tools, and controls.
- Build, test, and deploy. OpenAI describes designing, building, testing, and deploying production systems connected to customer data, tools, controls, and processes. ServiceNow and Accenture describe their program as a way to move enterprise agentic AI from pilot to production.
- Hand over the capability. AWS says engagements can leave behind systems, runbooks, architecture documentation, and trained internal champions. Taller says its engineers stay through production adoption.
Provider offers at a glance
The table summarises what each provider states on its own page or announcement. Product names and figures use the providers’ own wording.
| Provider | Engagement model as described | Stated scope | Platform link |
|---|---|---|---|
| Atlassian | Senior engineers embedded with customer teams to build, deploy, and scale AI in high-friction workflows | Scope a first use case or move a stalled pilot into production | Built on Rovo, Teamwork Graph, and the customer’s Atlassian environment |
| ADEL | Embedded senior FDEs, AI experts, and data experts, offered as consulting, embedded Forward Deployed AI Pods, training, and agentic software engineering | Define difficult AI problems, build production-ready systems, and transfer capability | Described as technology neutral; clients keep their model and platform choices |
| OpenAI Deployment Company | Diagnostic, priority workflow selection, then FDE work inside the organization | Design, build, test, and deploy production systems connected to customer data, tools, controls, and processes | OpenAI venture; the announcement does not detail model-choice terms |
| AWS Forward Deployed Engineering | Engineers embedded with customer business, engineering, and security teams | Build and deploy production AI using customer data and governance; leave systems and trained champions | Not stated whether non-AWS environments are in scope |
| ServiceNow and Accenture | Purpose-built pod for each customer-specific value chain (May 2026 program) | Move enterprise agentic AI from pilot to production | ServiceNow AI Platform |
| Accenture and Microsoft | Joint FDE practice (March 2026 announcement) | Design, build, and operationalize AI across the enterprise, including process redesign and change management | Microsoft’s AI platform and technology |
| Taller Technologies | “Frontier Engineers” who redesign workflows, build systems, and stay through production adoption | Includes Echo, an agentic enablement layer, and Chiron, a shared development environment | Taller’s own products |
| Forward Labs | Senior engineers embedded in client operations | Connect frontier models to customer data, tools, and controls; hand over production systems for customer teams to run | Not stated |
How the main models differ
Three broad patterns appear in these offers. The distinction matters more than the label. A platform-linked program makes the most sense if your workflow already runs on that platform, while a firm with its own products may ask you to adopt them.
Popular on Amazon
Platform-linked programs
Atlassian’s service page puts its position directly: “Atlassian FDEs are software and applied AI engineers who embed with your teams to build production solutions, not just recommend them.” The work targets high-friction workflows in software delivery, service management, and other critical work, so it fits teams whose critical processes already run in Atlassian products.
OpenAI’s Deployment Company was announced as a venture, and OpenAI said it agreed to acquire Tomoro. The announcement made that deal subject to customary closing conditions, including applicable regulatory approvals. It does not confirm completion, so check the status directly with OpenAI’s announcement and with OpenAI before counting on the acquired team.
AWS describes its model in three parts. In the words of 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 says customer engineers move from observers to co-builders to autonomous operators. Its announcement also quotes Gary Brantley, Chief Information Officer of the National Football League: “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.” That is one customer’s account, published by AWS, not an independent measure.
Recommended Free Tools
Also popular on Amazon
The ServiceNow and Accenture program staffs each engagement with a purpose-built pod built around a customer-specific value chain, drawing on platform-native, AI-native, and industry expertise. The Accenture and Microsoft practice is aimed at the stage after a pilot works. Accenture’s Chief Strategy and Services Officer, Manish Sharma, framed the goal this way: “AI value does not come from technology access but from the ability to convert it into sustained business impact.”
Specialist and product-led firms
Taller Technologies calls its embedded AI-native engineers “Frontier Engineers.” Its offering also includes Echo and Chiron, both of which are Taller’s own products and claims. Before signing, ask whether a deployment requires them or whether the team can build in your existing environment.
Forward Labs describes embedding senior engineers in client operations, connecting frontier models to customer data, tools, and controls, and handing production systems to customer teams to run. Its public description does not name a platform, so ask which models, tools, and environments it uses on a given project.
Also popular on Amazon
Technology-neutral claims
ADEL describes its approach as technology neutral and says clients keep their model and platform choices. That is a claim to confirm in the contract. Ask which models and cloud environments the team has delivered on, and who owns the code and configurations when the engagement ends.
Free tools Windows power users keep installed
One-click scans. No signup required.
Figures providers publish, and what they do not show
Each number below comes from the provider’s own page or announcement, dated 2026. None is an independent measurement, and because each provider defines and counts differently, the numbers cannot be ranked against one another.
- Atlassian: “80+ production AI agents built and deployed,” “~12 weeks to measurable business value,” and “100+ enterprise customers,” all displayed on its FDE page. The page does not publish the baseline or workflows behind the 12-week figure, so treat it as a target to test rather than a typical result.
- AWS: a $1 billion investment in its Forward Deployed Engineering organization. This describes AWS’s own spending, not what a customer pays.
- ServiceNow and Accenture: more than 300 pre-built AI agent skills and agentic workflows on ServiceNow’s AI Platform. This is a catalogue size, not evidence of outcomes.
- OpenAI: more than $4 billion of initial investment for the Deployment Company, and approximately 150 FDEs and deployment specialists that the announcement said Tomoro would bring.
What the customer must bring
An FDE engagement is not a hand-off of a problem. Providers consistently describe the customer as an active participant. Atlassian says its engagements are co-engineering partnerships and typically require a clear workflow, a dedicated customer partner, and access to the relevant systems. AWS works alongside business, engineering, and security teams. Published pages do not set a minimum staffing level, so ask each provider to list the customer roles it expects and the time each role must commit. In practice, that usually means:
Also popular on Amazon
- A workflow with a named business owner and a measurable outcome.
- A dedicated counterpart who can make decisions about priorities and trade-offs.
- Access to the systems and data the workflow touches, with permissions agreed before build starts.
- Business, engineering, and security reviewers available during build and deployment.
- Staff who will run the system after handover.
Questions that separate one offer from another
Ask every provider the same questions and compare how specific the answers are, not how the offer is labelled. Controls are where marketing and contracts diverge: security guarantees and the division of responsibility belong in each contract and technical review, not on a services page.
| Axis | Ask the provider | A useful answer names |
|---|---|---|
| Workflow and outcome | Which process changes, and what defines success? | The owner, the baseline measure, and the target |
| Delivery scope | Will the team advise, or also build, integrate, test, deploy, and support? | Named deliverables and who runs each step |
| Platform and model fit | Is the offer tied to a platform or model? | Which models, tools, and environments are in scope, and whether any vendor product is mandatory |
| Data and permissions | How are access, data handling, and model boundaries controlled? | The permission model, data location where relevant, and who approves access changes |
| Evaluation and oversight | How is output quality tested, and who reviews it? | The evaluation method, human review points, and escalation path |
| Monitoring and governance | What runs after go-live, and who responds to failures? | The monitoring owner, alerting, and change control process |
| Handoff | What does the customer receive and keep? | Code, documentation, runbooks, and training, and whether post-handover support is included or billed separately |
| Evidence | Were the cited results measured on a comparable workflow? | The baseline, the measurement method, and references on the same use case |
”
The Bottom Line
Choose a provider by the model it actually delivers, not by its label. Start with one workflow that has a named owner, ask for the deliverables and handover list in writing, and test the headline numbers against a reference customer running a comparable use case. If your processes already run on one vendor’s platform, that vendor’s FDE program is the natural first conversation. If you want model and platform choice kept open, write that neutrality into the contract.
Quick wins for a faster PC:
Clear out junk files and repair common Windows errorsFree Scan →Scan for outdated or missing drivers - takes under a minuteDriver Scan →Repair Windows errors before they cause bigger problemsFix Now →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.
