Build. Prove. Improve.
Engineers who work side-by-side with your experts to build workflows, integrations, and AI agents around how your company actually works — then measure against the outcomes you care about.
One Embedded Engineering Model. Two Specializations.
Start with the operational outcome, not a predetermined technology. Your FDE team brings the right mix of platform configuration, workflow engineering, AI evaluation, and production support to make it work.
Workflow FDE: Build the Operation
We map the work with the people who do it, then build the apps, forms, templates, approvals, automations, integrations, dashboards, and custom extensions needed to run it in MangoApps. Your team reviews working software in short loops and owns the production system at handoff.
AI FDE: Improve Workflow Performance
We turn a high-value workflow into a measurable AI system: representative tasks, expert rubrics, approved knowledge, tools, agent design, and the right model. We compare alternatives, capture failures and corrections, and keep improving quality, cost, latency, safety, and completion in production.
The Forward Deployed Loop
Traditional consulting gathers requirements, disappears for months, and delivers a document. Forward Deployed Engineering works in short, visible loops — observe the real problem, build something working, put it in front of your team, and adapt. Every loop ends with software your people can use and evidence the next loop can improve.
Observe
Your FDE embeds with the people doing the work — the shift lead juggling callouts, the coordinator chasing paperwork, or the expert reviewing AI output — and maps how work actually happens.
Build
Within days, not months, a working version is live in MangoApps: configured apps, custom forms, automations, integrations, or an AI agent grounded in your approved knowledge and tools.
Show
Every loop ends with real software your people can click, test, and challenge. Feedback lands while the context is fresh — a working session, not a change order.
Adapt
Real usage exposes what no requirements session can. Your FDE tightens the workflow or improves the AI system's knowledge, prompts, tools, routing, and model choice, then starts the next measured loop.
What Your Forward Deployed Team Delivers
This is not open-ended consulting and the deliverable is not a recommendations deck. Every engagement is tied to a working production system and measurable workflow performance.
Production Workflows
Apps, forms, templates, approvals, automations, dashboards, permissions, and integrations configured around your operation — with custom code only when the platform genuinely needs extending.
Customer-Specific AI Systems
The complete system around the model: approved data, RAG, prompts, tools, agent orchestration, permissions, model selection, and governed deployment inside the workflows your employees already use.
Benchmarks & Evaluations
Representative tasks, expected outcomes, expert rubrics, failure categories, and a repeatable evaluation environment for comparing changes against the same real work.
Continuous Improvement
Production monitoring turns errors, overrides, and expert corrections into the next evaluation set, so quality, cost, latency, safety, and workflow completion improve instead of drifting after launch.
Side-by-Side, Not Status Calls
A forward deployed engineer is not a project manager relaying requirements to a distant team. They work next to the people who know the operation — watching the workflow break, testing decisions with domain experts, and fixing the system while everyone still has the same context.
- Embedded in your meetings, channels, and working sessions
- Builds in your tenant, with your data and your people
- Decisions validated live with the teams who do the work
- Direct line back to MangoApps Product and Engineering
AI Performance, Measured Against Your Work
A generic model score cannot tell you whether an agent can complete your workflow. Your AI FDE creates a customer-specific benchmark, tests the complete system, and improves the highest-impact failure modes first. Fine-tuning or LoRA is used only when the evidence shows that simpler changes have reached their limit.
- Compare models against the same representative tasks and expert rubric
- Improve RAG, prompts, tools, agent design, and model routing as one system
- Track quality, cost, latency, safety, and workflow completion targets
- Turn expert corrections and production failures into the next improvement cycle
You Keep the Machine, Not the Memo
Every engagement leaves production software behind: workflows running in your tenant, admins trained to own them, AI agents continuing to execute, and the benchmarks needed to prove future changes are better. What repeats across customer work also strengthens the MangoApps platform without turning your deployment into an unsupported fork.
- Production workflows owned by your team from day one
- Evaluation assets, documentation, and admin enablement included
- AI agents keep running and remain measurable after handoff
- Reusable improvements flow back into the supported platform
How Forward Deployed AI Engineering Works
Every AI FDE engagement follows a disciplined process for evaluating, optimizing, and continuously improving customer-specific AI — from a promising use case to measurable production performance.
Select the Workflow
Choose a high-volume or high-value process with clear boundaries, available domain expertise, and an outcome the business can measure.
Build the Benchmark
Create representative tasks, expected answers or actions, expert rubrics, baseline performance, and failure categories before optimizing the system.
Build the System
Combine approved customer knowledge, retrieval, prompts, tools, workflows, permissions, agent architecture, and the model best suited to the task.
Optimize It
Compare alternatives against the same benchmark. Improve the full system first and use fine-tuning or LoRA only when the measured result justifies it.
Deploy and Improve
Monitor production outcomes, collect expert corrections, convert failures into new tests, and continuously improve the workflow after launch.
Engagement Options
Start with one workflow and the smallest engagement that can produce evidence. Expand only after the system proves its value in real work.
Workflow Launch
A focused 2–4 week engagement to take one operational workflow — onboarding, scheduling, inspections, approvals — from whiteboard to production. Fixed scope, fixed timeline, working software at the end.
AI Workflow Assessment
A 2–3 week sprint to select the use case, define success, create a representative benchmark, establish baseline performance, and recommend the production approach.
AI Workflow Launch
A 6–10 week embedded engagement to build, evaluate, optimize, and launch a customer-specific AI workflow with governed data, tools, integrations, and a measurable production improvement loop.
Fractional FDE
An ongoing monthly engagement that keeps an engineer attached to your account. Launch new workflows, adapt existing ones, and continuously improve production AI without starting a new project each time.
The MangoApps team is really responsive. Any questions get answered immediately. And they didn’t overpromise—they’ve delivered exactly what they said they would. The customer service really stands out.
Alison van Zijl
Head of Human Resources
Full House
CUSTOMER SUCCESS
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Frequently Asked Questions
A MangoApps forward deployed engineer (FDE) embeds directly with your team — in your meetings, your tenant, and your day-to-day operation — to build working software with the people who will use it. Workflow FDEs configure and extend operational systems; AI FDEs turn customer-specific workflows into measurable AI systems and improve them in production.
Forward Deployed AI Engineering applies the FDE model to an AI workflow. We select a valuable process, create a benchmark and expert rubric, build the complete system around the model, compare alternatives, deploy it with enterprise controls, and use production failures and corrections to drive continuous improvement.
In standard onboarding, MangoApps trains your admins, advises on best practices, helps with core integrations, and guides your team as it configures the platform. In an FDE engagement, our engineer does the deep building with you: mapping the real operation, configuring workflows, wiring integrations and agents, establishing AI evaluations where needed, and iterating until the production result fits.
We define the measures before optimization. Depending on the workflow, that can include answer quality, task success, expert override rate, cost per completed task, latency, safety failures, and end-to-end completion rate. Every major change is compared against the same representative tasks and expert rubric.
No. We first improve the full AI system — approved knowledge, retrieval, prompts, tools, workflow design, agent architecture, and model choice. Fine-tuning or LoRA is used only when the benchmark shows that those simpler, more maintainable approaches have reached their limit and training produces a meaningful gain.
It is a hybrid model — on-site for operational shadowing and working sessions where being in the room matters, remote for many build and evaluation loops. The travel plan depends on the workflow, locations, and engagement type and is agreed during scoping.
Usually not — and that is by design. MangoApps ships with 100+ apps, a deep template library, integrations, and an AI agent framework, so most work is delivered through configuration, composition, evaluation, and automation your admins can own. When a workflow genuinely needs something the platform does not have, the FDE can build a scoped, supported, upgrade-safe extension.
Workflow Launch engagements typically run 2–4 weeks. An AI Workflow Assessment runs 2–3 weeks, and an AI Workflow Launch typically runs 6–10 weeks. Fractional FDE is an ongoing model for organizations that want to keep launching workflows and improving production systems over time.
You keep working software, not a report. Workflows remain live in your tenant, admins are trained to own them, AI agents continue to execute, and the benchmark and evaluation assets remain available to validate future improvements. Ongoing optimization is available through Fractional FDE.
Bring Us the Workflow That Is Hard to Build — or Hard for AI to Get Right
Tell us where work breaks, where generic software stops short, or where an AI demo has not become a dependable production system. We will scope the right Workflow FDE or AI FDE engagement around one measurable outcome.
Let's Talk
Since 2008, we've been building the employee platform for the frontline, earning the trust of 2 million+ users and an NPS of 78.
Why Choose Us?
- Frontline AI: Governed AI for every employee and workflow.
- Top Security: HITRUST, ISO & SOC 2 certified.
- Exceptional UX: Delightful on mobile and desktop.
- Proven Results: 98% customer retention rate.
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