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AI Solution Accelerator™

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  • OUTPACE AI-POWERED COMPETITORS

    Move AI from experiments into real software delivery and accelerate your SDLC before competitors turn AI adoption into market advantage.

  • ESTABLISH A GOVERNED AI CORE

    Build a governed, standardized operating core where every system speaks the same language and AI runs only on verified, controlled data.

  • SHIP AI-ASSISTED WORK SAFELY

    Protect critical workflows before AI touches production and keep sensitive logic behind engineering review.

Our Execution Model

AI Solution Accelerator™ by Devox Software

AI Solution Accelerator™ by Devox Software is a governed execution model. Our approach centers on engineering a delivery harness around the LLM, so it works within your context. The value comes from the layer we build to enforce governance controls and apply your engineering standards.

We work inside your engineering organization with AI-powered development services that remove AI talent bottlenecks and modernize high-risk systems in controlled slices. The delivery harness gives you control over multiple focused coding-agent sessions, so complex work can be planned, executed, and validated in smaller steps.

Modernizing unstable systems? Launching new products?

We build development environments that deliver enterprise-grade scalability, compliance-driven security, and control baked in from day one.

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The Harness

The Harness Layer Behind AI Delivery

Context and Memory

We use LSP-based code search and knowledge graphs to give the LLM structured context about your codebase and architecture.

Guardrails and Permissions

AI receives only the access it needs. It cannot change protected workflows, financial logic, or regulated data paths without human approval.

Tool-Controlled Execution

Deterministic validation hooks require agents to revise code and tests until the work meets defined acceptance criteria.

Observability and Audit Trail

Every AI-assisted action is logged, reviewed, and assigned to a human owner before it enters the production path.

How the Harness Works

The Four Harness Principles

AI generates code fast. It does not know your product, your constraints, or your blast radius. The missing layer is the delivery harness around the model. Below is our methodology that governs every engagement: four principles that hold across all phases, and six delivery phases that apply them in sequence.

These principles apply across every phase. They are defined once here and enforced everywhere after.

  • Context & Memory. The LLM operates against a structured model of your system — LSP-based code search and knowledge graphs supply architecture, conventions, and dependency context, so agents reason from your codebase, not from guesswork.
  • Guardrails and Permissions. Agents receive only the access a task requires. Protected workflows, secrets, financial logic, and regulated data paths sit behind human approval gates. Meanwhile, execution guardrails block destructive actions before they reach critical systems.
  • Tool-Controlled Execution. Work runs as multiple focused agent sessions, not one oversized prompt. Deterministic validation hooks force revision until acceptance criteria are met; planning artifacts (markdown) are the only handoff between sessions, keeping context and tokens under control.
  • Observability & Audit. Every AI-assisted action is logged, time-stamped, traced to the requirement it supports, and assigned a human owner before it enters the production path.

Every phase below applies these four principles, so we focus on where each one affects delivery.

Modernizing unstable systems? Launching new products?

We build development environments that deliver enterprise-grade scalability, compliance-driven security, and control baked in from day one.

Check Our Portfolio
Learn More about Devox Software
How the Harness Guides Delivery

Our Six-Phase AI Delivery Model

01

Ground. Context-Grounded Discovery

Everything starts with context, not code. Before any implementation begins, we establish a reliable, reviewed picture of how the system really works so AI can contribute from a solid foundation rather than assumptions. At this stage, we identify technical limitations early and define where human oversight must remain in place. We look closely at the system’s structure, the bottlenecks that slow delivery, and the dependencies that make change risky. We also assess technical debt using tools that help reveal architectural weak points and anti-patterns, while reviewing the security surface to understand access boundaries and potential exposure. With that baseline in place, AI can work with confidence and precision instead of guesswork.

02

Scope. Validated Delivery Backlog

Once we understand the system, we translate that knowledge and stakeholder input into a clear, practical delivery scope before engineering begins. Here, we clarify the business rules that will shape implementation, including key decisions, exceptions, and workflow logic. We define each delivery slice as a real unit of work, grounded in product behavior, data dependencies, and release risk. Larger initiatives are then broken into smaller, testable increments with clear validation criteria, creating a backlog that is both realistic for the team and safe for AI to support.

03

Model. Pre-Code Validation

Before development starts, we validate how each slice should behave so the team can move forward with fewer assumptions and fewer surprises. We model the expected experience before writing code, turning prioritized requirements into reviewable user journeys. We also map APIs, data flows, and integration points in advance so teams can see how everything connects before implementation begins. New components are then checked against existing system constraints and release dependencies, helping the team make sound design decisions early.

04

Build. Human-Governed Development

At this stage, AI supports development, but always under engineering control. In legacy environments especially, hidden logic and undocumented behavior can make even small changes risky, so every recommendation is reviewed by engineers before it becomes part of delivery. We analyze the system beneath the surface to uncover hidden coupling, brittle dependencies, dead paths, and critical business logic that must be protected. From there, we create refactoring plans that show what can be safely separated and what requires deeper architectural attention. AI can then help generate code in line with your existing conventions and patterns, while rollout is prepared carefully through feature flags, CI/CD checks, and rollback planning to reduce release risk.

05

Release. Audit-Ready Deployment

Before AI-assisted work reaches production, we make sure there is clear evidence that it is ready. Speed matters, but what matters more is being able to review, trust, and explain every release. Each delivery slice passes through quality, testing, security, and pipeline checks before deployment. We shape the CI/CD process around your technology stack and approval requirements, while also watching for environment drift across development, staging, and production. Releases are prepared with rollback readiness in mind, and engineers remain responsible for final production decisions. As a result, every increment can be traced back to clear evidence such as passed checks, reviewer input, and deployment signals that leadership, security, and compliance teams can inspect.

06

Learn. Post-Release Loop

The work does not stop at release. Once changes are live, we use what the system tells us to make smarter decisions about what should happen next. We connect observability and telemetry tools so performance, errors, and usage patterns can guide the next delivery cycle. AI helps surface meaningful signals such as engagement drivers, drop-off points, and areas that need attention, so the team no longer has to rely on instinct alone. We also support continuous refinement through experimentation frameworks and hypothesis-driven learning, helping the product improve with every release.

Built for Compliance

Compliance Built Into the AI Delivery Harness

We translate compliance requirements into engineering controls:

[AI Governance]

  • GDPR

  • CCPA

  • SOC 2

  • ISO/IEC 27001

  • NIST CSF 2.0

  • OWASP Top 10 for LLM Apps

[Data Privacy and Cloud]

  • GDPR

  • CCPA / CPRA

  • GLBA

  • FERPA

  • DORA

[Security Standards]

  • SOC 2

  • ISO/IEC 27001

  • NIST CSF 2.0

  • OWASP Top 10 for LLM Apps

  • PCI DSS v4.0

  • Secure SDLC

  • SAST / DAST

  • ISO/IEC 27017

[Model Risk]

  • Fed/OCC SR 11-7

  • Model Cards

  • Bias Testing

  • XAI Logs

  • DORA

Case Studies

Our Latest Works

View All Case Studies
Function4 Function4
  • website
  • management platform

Function4: Event Management Platform for the Financial Services Industry

A feature-rich system for managing tickets, devices, invites, and communication at scale.

Additional Info

Core Tech:
  • Vue js
  • GSAP
  • Ruby
  • Azure
Country:

USA USA

View Case Study
Searchable Archive IA Engine for a European Legal Tech Provider Searchable Archive IA Engine for a European Legal Tech Provider

Searchable Archive IA Engine for a European Legal Tech Provider

An on-prem intelligent automation engine that transforms legacy legal scans into a searchable, audit-grade archive with sub-second full-text discovery and language-adaptive OCR.

Additional Info

Core Tech:
  • FastAPI
  • .NET 6 wrapper
  • React 18
  • Elasticsearch 8.x
  • PostgreSQL
  • Docker Swarm
  • Tesseract
  • Grafana
  • GitLab CI/CD
  • PDF/A-3b sealing
Country:

Germany Germany

View Case Study
Optimizing Migrations: Neural Networks for Column Classification and Anomaly Detection for a Tech Startup Optimizing Migrations: Neural Networks for Column Classification and Anomaly Detection for a Tech Startup

Optimizing Migrations: Neural Networks for Column Classification and Anomaly Detection for a Tech Startup

Advanced machine learning techniques streamline data table processing as part of an end-to-end product. Neural networks identify data types, detect anomalies, and classify columns for a smooth automated migration. Real-time. Accurate. Fast.

Additional Info

Core Tech:
  • Python
  • Keras
  • Pandas library
  • Scikit-learn
  • NLTK (Natural Language Toolkit)
Country:

USA USA

View Case Study
Testimonials

Testimonials

Carl-Fredrik Linné Carl-Fredrik Linné
Carl-Fredrik Linné Sweden

The solutions they’re providing is helping our business run more smoothly. We’ve been able to make quick developments with them, meeting our product vision within the timeline we set up. Listen to them because they can give strong advice about how to build good products.

Carl-Fredrik Linné
Tech Lead at CURE Media
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Darrin Lipscomb Darrin Lipscomb
Darrin Lipscomb United States

We are a software startup and using Devox allowed us to get an MVP to market faster and less cost than trying to build and fund an R&D team initially. Communication was excellent with Devox. This is a top notch firm.

Darrin Lipscomb
CEO, Founder at Ferretly
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Daniel Bertuccio Daniel Bertuccio
Daniel Bertuccio Australia

Their level of understanding, detail, and work ethic was great. We had 2 designers, 2 developers, PM and QA specialist. I am extremely satisfied with the end deliverables. Devox Software was always on time during the process.

Daniel Bertuccio
Marketing Manager at Eurolinx
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Trent Allan Trent Allan
Trent Allan Australia

We get great satisfaction working with them. They help us produce a product we’re happy with as co-founders. The feedback we got from customers was really great, too. Customers get what we do and we feel like we’re really reaching our target market.

Trent Allan
CTO, Co-founder at Active Place
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Andy Morrey Andy Morrey
Andy Morrey United Kingdom

I’m blown up with the level of professionalism that’s been shown, as well as the welcoming nature and the social aspects. Devox Software is really on the ball technically.

Andy Morrey
Managing Director at Magma Trading
Vadim Ivanenko Vadim Ivanenko
Vadim Ivanenko Switzerland

Great job! We met the deadlines and brought happiness to our customers. Communication was perfect. Quick response. No problems with anything during the project. Their experienced team and perfect communication offer the best mix of quality and rates.

Vadim Ivanenko
COO at Optherium
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Jason Leffakis Jason Leffakis
Jason Leffakis United States

The project continues to be a success. As an early-stage company, we're continuously iterating to find product success. Devox has been quick and effective at iterating alongside us. I'm happy with the team, their responsiveness, and their output.

Jason Leffakis
Founder, CEO at Function4
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John Boman John Boman
John Boman Sweden

We hired the Devox team for a complicated (unusual interaction) UX/UI assignment. The team managed the project well both for initial time estimates and also weekly follow-ups throughout delivery. Overall, efficient work with a nice professional team.

John Boman
Product Manager at Lexplore
View on Clutch
Tamas Pataky Tamas Pataky
Tamas Pataky Canada

Their intuition about the product and their willingness to try new approaches and show them to our team as alternatives to our set course were impressive. The Devox team makes it incredibly easy to work with, and their ability to manage our team and set expectations was outstanding.

Tamas Pataky
Head of Product at Stromcore
View on Clutch
Stan Sadokov Stan Sadokov
Stan Sadokov Estonia

Devox is a team of exepctional talent and responsible executives. All of the talent we outstaffed from the company were experts in their fields and delivered quality work. They also take full ownership to what they deliver to you. If you work with Devox you will get actual results and you can rest assured that the result will procude value.

Stan Sadokov
Product Lead at Multilogin
Mark Lamb Mark Lamb
Mark Lamb United Kingdom

The work that the team has done on our project has been nothing short of incredible – it has surpassed all expectations I had and really is something I could only have dreamt of finding. Team is hard working, dedicated, personable and passionate. I have worked with people literally all over the world both in business and as freelancer, and people from Devox Software are 1 in a million.

Mark Lamb
Technical Director at M3 Network Limited
Insights

Some Insights Into AI Solution Accelerator™

Manufacturing

Software Architecture for Industrial Machinery Manufacturers: Integrating ERP, MES, and IoT Platforms

Read Article
Read Article
Manufacturing

End-to-End Manufacturing Traceability: Architecture Patterns for Multi-Plant Operations

Read Article
Read Article
Manufacturing

Cheat Sheet: Cost-Effective Modernization of Production Control Systems in Machinery Manufacturing

Read Article
Read Article
View More Insights
FAQ

Frequently Asked Questions

  • What are AI accelerators, and how do they differ from traditional automation?

    AI Solution Accelerator™ is Devox Software’s proprietary delivery model for using AI in real engineering workflows.

    Traditional automation focuses on speed. The Accelerator shows how an AI-powered software development company can make AI-assisted delivery safe, useful, and governed. This happens through the delivery harness layer, which governs how the LLM uses permissions.

    On the delivery side, it launches adaptive CI/CD pipelines out of the box, whether you use GitHub Actions, GitLab, or Jenkins, so teams can move faster with safer releases when needed. Just as importantly, we embed telemetry and analytics from the beginning by integrating observability tools such as Datadog, allowing the system to monitor user behavior and application performance in real time. That means if you are launching, for example, a service that processes data from connected devices, you can identify performance bottlenecks early rather than after they start affecting users.

  • How do you handle data security and governance when AI is part of the delivery process?

    As a nearshore AI-powered software development company, we make security and governance part of the delivery system by translating compliance requirements into engineering controls. Sensitive logic, regulated data, and production releases remain under engineering control. Execution guardrails block destructive actions and keep agents within approved boundaries. Code, transcripts, credentials, and delivery artifacts stay inside controlled environments with defined access boundaries.

    Sensitive inputs are sanitized before any AI processing, and when external model APIs are used, redaction layers prevent proprietary information from leaving approved boundaries. Every Al-assisted output is traceable and requires strict human review before merging into your engineering workflows.

  • Will we need to support both the legacy system and the new system during the transition, and how will that affect costs?

    Yes, and we are fully transparent about that. With an iterative approach (like the Strangler pattern, which safely replaces legacy code piece-by-piece without system downtime), you maintain two systems for a period of time, which means parallel costs. Our AI accelerator reduces the burden of the transition by automating orchestration and equivalence testing through Dual Run, so you can retire legacy modules with confidence and as quickly as possible.

    For companies searching for an AI-powered software development company europe partner, AI Solution Accelerator™ is built on Devox Software’s internal accelerator framework to reduce development time, lower release risk, and give teams measurable verification before change reaches production.

  • Can we log and track what the AI has recommended or executed? Is it observable for audits and compliance?

    Every AI-assisted action within AI Solution Accelerator™ is logged in a structured, reviewable format. Logs include context snapshots, decision rationale, confidence signals, and references to affected code artifacts or system components.

    All AI activities can be monitored via your existing monitoring tools or integrated into GitOps workflows. Each optimization or change is captured as a reviewable diff with metadata showing what changed, why it changed, and what triggered it. 

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