FDE Bringing AI Engineers into the Real Enterprise Business Front Line
Forward Deployed Engineering connects enterprise business, data, people, AI models and existing systems — driving AI from idea to actual operation.
What is FDE?
FDE stands for Forward Deployed Engineering. Unlike traditional software development, FDE does not wait for requirements to be fully defined before starting work, nor does it simply complete systems according to a feature list.
FDE engineers go deep into real enterprise business, working together with management, business teams and technical staff to understand problems, discover AI opportunities within real data and real workflows, and rapidly complete design, development, deployment and iteration.
FDE requires understanding both AI technology and business processes, organisational collaboration and project outcomes.
FDE vs Traditional Services
Traditional AI Consulting
Primarily provides strategic analysis, technology recommendations and project reports. Project deliverables are typically strategy documents; subsequent development and deployment must be driven by the enterprise itself.
Traditional Software Development
Typically requires clients to provide clear requirements upfront, then the development team completes the system according to requirements. Focus is on whether features are delivered.
EvoCore FDE
The FDE team co-discovers requirements with the enterprise, starts from business outcomes, and continuously adjusts and optimises throughout the project. The focus is not just on completing system development, but on whether AI truly enters business processes and is used by staff.
FDE Service Flow
Enterprise AI Diagnosis
Understand the enterprise's current business, systems, data, staff and AI usage. Includes management interviews, department interviews, business process mapping, data and file inventories, software system reviews, AI usage analysis and problem identification.
AI Opportunity Map
Identify work across different enterprise departments where AI can be applied, ranked by value and difficulty. Assessment dimensions: labour input, work frequency, business value, data readiness, technical feasibility, risk & compliance, adoption difficulty and expansion potential.
Data & Knowledge Preparation
Help enterprises organise the data and knowledge required by AI systems, including historical file organisation, data cleansing, document classification, business rule organisation, standard process organisation, permission definitions, data interface confirmation and knowledge base structure design.
Rapid Prototype & POC
Select one high-value scenario and rapidly complete validation using real data. POC validates: whether AI can understand enterprise data, whether it can complete target tasks, whether accuracy meets requirements, human review workload, system integration feasibility and actual business value.
AI System Development
Complete formal system construction based on validation results. May include: AI Agents, enterprise Skills, knowledge bases, workflows, management consoles, user interfaces, API interfaces, permission systems, logging systems and review platforms.
Enterprise System Integration
Connect AI to existing enterprise software and communication tools: ERP, CRM, financial systems, email systems, WhatsApp, Google Workspace, enterprise databases, document management systems, websites and apps.
Organisational Deployment & Training
After the AI system goes live, whether staff truly use it determines project value. EvoCore assists enterprises in designing staff operating procedures, creating user guides, conducting department training, establishing human review processes, collecting usage feedback, setting AI usage responsibilities and building internal support mechanisms.
Continuous Optimisation & Skill Development
As enterprise business changes, continuously add new AI capabilities: new data source connections, new rule additions, new Agent development, new Skill development, model optimisation, workflow adjustments, usage effectiveness evaluation and department expansion.