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Case Study • Cybersecurity Training

Embedded Engineering for a Cybersecurity Training Company

AccelOne embedded 2 to 5 nearshore engineers in a cybersecurity training company’s development team. The team shipped features, fixed bugs, built subsystems, and migrated legacy systems. All major milestones were delivered on time.

Cybersecurity Training
Engagement: Embedded Web Development, Subsystem Design, and Legacy System Migration
Partnership: Nearshore Embedded Engineering Team, Scaled From 2 to 5 Engineers
Two engineers reviewing code on a monitor in a dimly lit server room
2 to 5 Engineers
Nearshore team scaled as project phases required
5 Workstreams
Features, fixes, support, subsystems, and migration
On time
All major milestones delivered
No improvement areas
Identified by the client across the engagement
At a Glance
~0
Manual tagging remaining
Near-complete elimination.
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Manual tagging remaining
Near-complete elimination.
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Manual tagging remaining
Near-complete elimination.
Overview
Cybersecurity Training Company: cybersecurity, training, web development

The client is a cybersecurity training company. Its web applications are the core delivery mechanism for its training, so they must work reliably, meet high security standards, and evolve as the curriculum expands.

The client found AccelOne through an online search, with no prior relationship or referral, and selected AccelOne on merit. AccelOne engineers joined the client’s internal team and worked in the same standups, workflows, and communication channels. The engagement covered new product work and the maintenance, support, and migration work that keeps a live product healthy.

The Challenge

Continuous Development Held to a Security Standard

The client’s customers are security professionals. They notice vulnerabilities and expect rigorous code practices. A platform that teaches secure development has to be well built itself.

The client’s development needs were continuous, not project-shaped. New features, ongoing bugs, shifting priorities, subsystem work, and legacy migrations do not fit a fixed statement of work. The client needed engineers who could work inside its organization, meet its code review standards, and adapt as requirements changed.

the challenge

Search and tagging had become a serious bottleneck

With millions of videos, search and tagging had become a serious bottleneck.

challenge 01

Manual tagging didn't scale

Description here.

challenge 01

Manual tagging didn't scale

Description here.

challenge 01

Manual tagging didn't scale

Description here.

challenge 01

Manual tagging didn't scale

Description here.

Highlights

Engineering Highlights

01

One Team, Not a Separate Vendor

AccelOne engineers operated as an extension of the client’s development organization. Shared standups, workflows, and communication channels kept institutional knowledge inside the team instead of with an outside vendor.

02

Security Built Into Implementation

The team treated security as part of implementation, not an afterthought. In a security context, bugs can be vulnerabilities, so every fix and feature followed the client’s code review and quality standards.

03

Staffing That Matched Each Phase

The team scaled from 2 engineers in lower-intensity phases to 5 engineers in critical delivery phases. Headcount changed with project needs, without renegotiating a statement of work for each adjustment.

the solution

A hybrid AI video intelligence pipeline built for scale

Description here.

The Impact

Key Impact: Cost Reduction by Three Orders of Magnitude

One of the most significant achievements of this engagement was the reduction of AI processing costs by three orders of magnitude.

By combining on-premises transcription models with selective use of commercial APIs, AccelOne designed a hybrid architecture that:

This approach made the system not only scalable and secure but also economically viable at a massive scale, unlocking growth for Kurator without cost bottlenecks.

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The result was not just faster delivery, but stronger alignment around how

Monetate builds and evolves its platform.

How it works

7-step AI pipeline

Each component is modular and optimized for cost and reliability.

Step 01

Step title

Step description.

Tag
Step 01

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Step description.

Tag
Step 01

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Step description.

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Step 01

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Step 01

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Handing off these tasks to AI allowed senior engineers to focus more time on architecture, edge cases, and customer-impacting decisions. Quality remained non-negotiable.

As Austin puts it:

AI helps our QA team get from zero to 85 percent of their job much faster, so they can focus on the 15 percent where they add real value.

The outcome enabled operational acceleration without sacrificing discipline.

Smiling developer holding a laptop in an office with brick walls and code on the monitors
Solutions

Embedded Engineering Across the Live Product

AccelOne provided ongoing embedded web development across five workstreams. Embedded engineering means external engineers join a client’s team and work inside its development organization, rather than delivering a fixed scope as a separate vendor.

New Feature Development: The team developed new capabilities for the client’s existing web applications. Each feature was designed, reviewed, and shipped to the same standards as the internal team’s own work.

CAPABILITY 01

Features

Bug Fixes: The team diagnosed and resolved defects across the application, with the understanding that a bug in a security product can also be a vulnerability.

CAPABILITY 02

Security

Support Issue Resolution: The team resolved customer support issues that needed engineering involvement, closing the gap between support tickets and development fixes.

CAPABILITY 03

Resolution

Subsystem Design and Implementation: The team designed new subsystems from scratch and implemented them within the existing application architecture.

CAPABILITY 04

Subsystems

Legacy System Migration: The team wrote production-ready code to migrate legacy systems, maintaining continuity and steady delivery through the transition.

CAPABILITY 05

Migration
Under the hood

Open-source intelligence, cloud applied selectively

The pipeline combines open-source models with selective cloud services, running primarily on on-prem GPU infrastructure to deliver production-grade accuracy while avoiding cost, lock-in, and unpredictability.

Tech name

Description

Open Source

Tech name

Description

Open Source

Tech name

Description

Open Source

Tech name

Description

Open Source

This hybrid approach delivers production-grade accuracy while avoiding the cost, lock-in, and unpredictability of cloud-only architectures — making large-scale video intelligence economically sustainable at 2.5M+ video scale.

Project recap
AI-powered video intelligence at massive scale
the outcome

A searchable, time-addressable catalogue spanning millions of hours

What was previously locked behind sparse tagging and manual review is now discoverable at scale — while balancing cost efficiency with production-grade reliability.

before

Hours per batch of manual tagging

Hours per batch of manual tagging

Hours per batch of manual tagging

Hours per batch of manual tagging

after

Minutes of spot-check per batch

Minutes of spot-check per batch

Minutes of spot-check per batch

Minutes of spot-check per batch

The catalogue is now discoverable by:

Title

Description

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Description

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Description

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Description

partners

Expanding the Partnership

As the early AI and UI initiatives gained traction, the collaboration expanded. AccelOne is now contributing not only to delivery, but to roadmap planning across AI, machine learning, and user experience initiatives.

Austin describes the ongoing dialogue as productive and forward-looking:

"As we work through these initial projects, having the teams weigh in on what the roadmap should look like next has been very productive. We've gained a great set of partners and feedback voices inside of AccelOne."

The partnership now operates at two levels:

  1. Accelerating current initiatives
  2. Accelerating current initiatives

For a platform operating at this level of complexity, that alignment matters.

output

A Model Built on Leverage and Trust

Monetate's goal was simple: Accelerate AI and UI initiatives without slowing the organization down.

What ultimately emerged was a partnership that increased leverage across the entire business.

Senior engineers were embedded quickly. Roadmaps moved faster. AI became part of daily workflows. Collaboration tightened across teams and time zones.

The engagement reduced friction and increased output.

And the relationship continues to expand, with AccelOne contributing to long-term direction beyond the initial delivery.

At this level of complexity, sustainable acceleration is not about adding more people. It's about embedding partners who raise the standard of how the work gets done.

The Outcome

Results & Impact

01
All major milestones delivered on time across features, bug fixes, subsystem work, and the legacy migration.
02
Legacy systems migrated with all existing functionality maintained.
03
A team that scaled between 2 and 5 engineers without renegotiating a statement of work for each change.
04
Higher satisfaction among internal stakeholders and end customers.
05
No areas for improvement identified by the client.
Impact

Business Impact

The client raised its bottom line while meeting the quality and security standards and delivery timelines its customers expected. Development capacity matched each phase of the work, from lower-intensity periods to critical delivery.

The client highlighted AccelOne’s transparency, openness to feedback, attentiveness, and helpfulness. In a long-term embedded engagement, these qualities matter as much as technical capability.

“They’ve always been very attentive and helpful in our relationship.”

Client, Cybersecurity Training Company

Technical Architecture

A Production-Ready Approach to Legacy Migration

AccelOne held the legacy migration to the same standards as feature work. The migration was sequenced so new development could continue in parallel.

Principle How it was applied Outcome
Production-ready code Same quality, security review, and testing standards as feature work No throwaway transition scaffolding
Continuity first All existing functionality maintained through the transition No loss of capability during or after the migration
Scalability as the target Migration designed as a foundation for future development More than a copy of the legacy system
Parallel delivery Migration sequenced alongside other development Features and bug fixes continued during the migration
Strategic Value

Embedded Engineering for Work That Never Stops

This engagement shows when embedded engineering fits better than project-based outsourcing. Continuous product work, shifting requirements, and legacy migrations need a team inside the organization. Security-sensitive software also needs engineers who treat code quality and vulnerability prevention as their own responsibility.

Factor Project-based outsourcing Embedded engineering
Team structure Vendor delivers a defined scope Engineers join the client’s team directly
Ways of working Separate workflows and channels Same standups, workflows, and communication
Requirements Defined upfront Adapts to shifting requirements
Staffing Fixed to the project Scales up or down with project phases
Knowledge Leaves with the vendor Stays in the team

Frequently asked questions

What web development services did AccelOne provide for the cybersecurity training company?
AccelOne provided ongoing embedded web development. The scope covered new feature development, bug fixes, customer support issue resolution, subsystem design and implementation, and production-ready legacy system migration. A team of 2 to 5 nearshore engineers worked as an extension of the client’s internal development organization.
How is embedded engineering different from project-based outsourcing?
In embedded engineering, external engineers join the client’s team and work in the same standups, workflows, and communication channels. Project-based outsourcing delivers a fixed scope through a separate vendor. The key differences are integration, adaptability, and knowledge retention.
How does AccelOne approach legacy system migration in a web application?
AccelOne treats migration code as production code, with the same quality, security review, and testing standards as other delivered work. For this client, the migration maintained all existing functionality, built a scalable foundation for future development, and ran in parallel with other development work.
How did AccelOne handle changing requirements and staffing needs?
When requirements shifted, AccelOne listened, adjusted quickly, and delivered solutions that addressed the client’s actual needs rather than the original specification. The team scaled between 2 and 5 engineers as project phases required, without a contract renegotiation for each change.
What results did the cybersecurity training company see?
All major milestones were delivered on time. The client raised its bottom line while maintaining its standards for quality, security, and delivery timelines. Internal stakeholders and end customers saw increased satisfaction, and the client identified no areas for improvement.
What should teams look for in an engineering partner for security-sensitive software?
Look for engineers who treat vulnerability prevention as part of implementation, not a separate audit step. Based on this engagement, the qualities that matter most are security-aware engineering, transparency about status and risks, real integration with the internal team, flexible staffing, production-ready delivery, and quick response to feedback.
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