Product Tour
See how Ellygent turns system definition into engineering context.
Walk through how teams define system intent, structure engineering context, maintain traceability, and export approved context into implementation and AI-assisted delivery workflows.
Guided engineering workflow
Problem
Objectives
ConOps
Capabilities
Functions
Requirements
Traceability
Baseline
Export
Product Walkthrough
Follow the workflow before you evaluate the details.
See the guided path from system intent to structured context, AI-assisted refinement, human review, traceability, baselines, and context export.
Product walkthrough coming soon
See the workflow from system definition to traceable engineering context.
Define system intent
Structure context
Generate/refine with AI
Review and accept
Trace and export
Step 1
Define the system boundary and problem context
Start by making the system perimeter explicit: what belongs inside the solution, what remains external, what constraints matter, and why the system needs to exist.
Problem context workspace
System boundary
External actors
Constraints
Assumptions
Step 2
Capture mission objectives and success criteria
Turn high-level intent into measurable objectives so later capabilities, functions, and requirements can be reviewed against concrete success signals.
Objective set
Mission objective
Success criterion
Stakeholder value
Verification signal
Step 3
Describe operational scenarios and ConOps
Describe how the system behaves in real operational context, including nominal flows, edge cases, actors, environment, and handoffs.
ConOps scenario board
Scenario
Actor
Trigger
Outcome
Step 4
Define system capabilities and functions
Translate context into capabilities and functions that describe what the system must provide before detailed requirements or implementation tasks appear.
Capability to function map
Capability
Function
Requirement seed
Review status
Step 5
Generate or refine engineering content with AI assistance
Use the approved context as input for AI-assisted drafting, refinement, derivation, and requirement quality review instead of starting from isolated prompts.
AI proposal panel
Source context
Draft proposal
Quality notes
Suggested relations
Step 6
Review, accept, and maintain human control over AI proposals
AI output remains a proposal until a person reviews it. Teams can accept, revise, reject, and preserve decision intent before content becomes project context.
Human review queue
Pending proposal
Reviewer note
Accept action
Revision history
Step 7
Create traceability between system definition elements
Connect objectives, scenarios, capabilities, functions, requirements, hazards, and implementation context so teams can navigate why each artifact exists.
Traceability graph
Objective
Capability
Function
Requirement
Step 8
Manage versions and baselines
Capture approved system context as baselines, compare changes over time, and give downstream teams a stable reference for delivery and audit.
Baseline timeline
Live context
Baseline v1.0
Change set
Approval record
Step 9
Export context through ReqIF, CLI, or Context API
Move approved engineering context into implementation workflows, enterprise requirements ecosystems, automation, CI, and AI-assisted delivery tools.
Context export paths
ReqIF
CLI pull
Context API
AI-ready JSON
Bring system definition into the delivery workflow.
Start with system intent, keep AI proposals under review, and export approved context where engineering work actually happens.