Forward Deployed Engineering

AI software is only useful when it works for your business

That is why every Rapidflare customer gets a Forward Deployed Engineer.

Your products, data, workflows, systems, and users are different. Instead of asking your team to figure out how to adapt Rapidflare to them, we assign a Forward Deployed Engineer who works alongside your team from onboarding throughout the entire partnership. They help turn your requirements into working AI experiences, from the first deployment to everything you want to build next.

  • Included with every Rapidflare engagement
  • No separate professional services contract required
Six months after launch#rapidflare · Slack
“New requirement: can answers show live stock levels for distributors?” Your team, Thursday morning
Your FDE · the same engineer since kickoff
Knows your graph Knows your ERP integration Knows why pricing is scoped off
Scoped it as an inventory lookup, distributor audience only
Wired live stock from your ERP into answers
Re-ran your eval suite, all green
SHIP Live in Thursday’s release
No new scope · no ticket queue · no cold start
One week later

An engineer who already knows your deployment, included with every engagement.

More than implementation support

Most implementations end at the same point; Rapidflare doesn't

Most implementations
Product gets deployed Implementation team leaves Your team is on its own
Rapidflare
Onboarding Deployment Your FDE stays involved Throughout the partnership

Your Forward Deployed Engineer builds context around your deployment, product knowledge, technical environment, requirements, and how your users are using Rapidflare day to day. So when something needs to change, improve, integrate, or expand, you have an engineer on the Rapidflare side who already understands the context.

What does your Forward Deployed Engineer do?

One job: make Rapidflare work the way your business runs

That can mean helping across the entire lifecycle of your deployment.

Knowledge and data

Connect and structure the product information your agents need across documentation, catalogs, websites, knowledge bases, and internal systems.

Agent configuration

Configure Rapidflare around your products, users, workflows, terminology, and business requirements.

Technical implementation

Turn product and business requirements into the appropriate Rapidflare configuration and technical implementation.

Integrations

Work through the technical requirements needed to connect Rapidflare with your existing systems and deployment environment.

Evaluation and debugging

Investigate incorrect or incomplete answers, trace problems to their source, and work with the broader Rapidflare team to resolve them.

New requirements

When your business needs something the original deployment did not anticipate, help translate that requirement into the right configuration, workflow, integration, or product capability.

Continuous optimization

Use production feedback and usage patterns to identify opportunities to make the experience more useful over time.

Where the work is divided

Your team brings the domain expertise and we handle the AI engineering

Your people know your products, customers, systems, and business better than anyone. We do not want them spending their time becoming experts in AI infrastructure or learning how to operate another platform. Your team helps us answer questions like:

Your team answersYour FDE builds

“What should the system accomplish?”

Agent configurationScoped to your users, terminology, and workflows

“Which information should it rely on?”

Knowledge ingestionYour sources, structured into the graph

“What does a correct answer look like?”

Evaluation and QAAn eval suite built to your standard

“How should this fit into our existing workflow?”

IntegrationsWired into the systems you already run

“What do our users need next?”

New workflowsRequirements turned into the next release

Five answers from your team. A working system from your FDE.

Continuity, not a cold start

One engineer who already knows your environment

A new requirement six months after launch should not mean starting from zero with someone reading your support ticket for the first time. Your Forward Deployed Engineer already understands the context behind your Rapidflare deployment.

Kickoff

Learns your products, sources, and systems

Launch

Knows what was built, and why it was built that way

Month three

Sees how real users use it, and what they ask next

Month six

Picks up your new requirement without a cold start

What you need What gets built How it performs What gets improved next

You do not lose that context once onboarding is over.

Launch is not the handoff

It is the beginning of the working relationship

Once real users begin interacting with Rapidflare, new opportunities emerge.

They ask unexpected questionsThey expose gaps in product informationThey reveal new workflows worth supportingYour catalog changesYour company launches new productsTeams find new ways to use AINew systems need to be connectedYour requirements evolve

Your Forward Deployed Engineer remains part of that process, helping your Rapidflare deployment evolve alongside your business.

From requirement to implementation

Someone already responsible for understanding both sides of the problem

Your team should not have to decide whether every new AI requirement warrants another internal engineering project.

Your business requirementWhat are you trying to accomplish?
Your product and dataWhat information, systems, and domain knowledge does it depend on?
RapidflareHow should the platform, agent, workflow, or integration be configured?
ProductionHow does it work for real users?
FeedbackWhat needs to be improved next?

Your FDE helps maintain that loop throughout the partnership: feedback flows straight back into the next requirement.

What this means for your team

Six things that change once an engineer is already on your requirements

Less internal engineering work

You do not need to build a team responsible for configuring and operating your Rapidflare deployment.

Faster path from requirement to implementation

When your team identifies a need, there is already someone technically responsible for helping turn it into a working solution.

Less vendor handoff

The technical relationship does not disappear once the initial implementation is complete.

More continuity

Your engineer builds knowledge about your deployment over time instead of forcing your team to repeatedly explain the same context.

Continuous improvement

Production feedback can become product improvements rather than simply accumulating in a dashboard or support queue.

More value from the platform

As your requirements evolve, your Rapidflare deployment can evolve with them.

Not professional services

Forward Deployed Engineering is not scoped around a project

Typical professional services
Project starts Hours consumed Project ends New requirement means new scope
Your FDE at Rapidflare
There during onboarding There when you deploy There when users start generating feedback There as new requirements emerge
Forward Deployed Engineering is part of Rapidflare

Not an add-on.

Not a limited onboarding package.

Not a block of professional-services hours you need to purchase every time something changes.

Every Rapidflare customer gets a Forward Deployed Engineer throughout the partnership, included as part of the engagement.

Because enterprise AI should not require your company to become an AI engineering company just to make it work.

FAQ

Frequently asked questions

Is Forward Deployed Engineering an additional paid service?

No. A Forward Deployed Engineer is included as part of every Rapidflare customer engagement. There is no separate professional services package required to work with your FDE.

How long does our Forward Deployed Engineer stay involved?

Throughout your partnership with Rapidflare, not just during onboarding or the initial implementation. Your FDE continues working with your team as your deployment and requirements evolve.

Does this replace our own engineering team?

No. Your team may still need to provide access, technical context, or involvement for systems you control. But you do not need to dedicate internal engineers to becoming Rapidflare experts or operating the AI system itself. Your FDE owns the Rapidflare side of that technical relationship.

What happens if we have a new requirement after launch?

Bring it to us. Your Forward Deployed Engineer can work with your team to understand the requirement and determine how best to address it through configuration, data, integrations, workflows, or Rapidflare's product capabilities.

Is this just technical support?

No. Technical support is primarily reactive: something goes wrong, and someone helps resolve it. Forward Deployed Engineering is broader. Your FDE works proactively with your team to deploy, improve, adapt, and expand how Rapidflare is used across your organization.

What kinds of people will our FDE work with?

That depends on the deployment. They may work with your product experts, engineering or IT teams, customer support leaders, sales teams, product managers, or other stakeholders involved in the use case, bridging your business and product requirements with the technical Rapidflare implementation.

Your requirements should not end in a support queue

Put an engineer on them

Every Rapidflare engagement includes a Forward Deployed Engineer who works alongside your team from onboarding through everything that comes next. Not just to get Rapidflare live: to keep making it work for your business.

Talk to us about your requirements