From unclear intent to implementation context
Define what to build before you start coding.
Ellygent guides teams from unclear intent to connected system and software definition, giving developers and AI the context to implement with fewer assumptions and less avoidable rework.
When important decisions stay unresolved, assumptions become code. Discovering the mismatch later means more clarification and unnecessary iterations.
Create an account, then set up your project and choose the definition steps it needs.
What is Ellygent?
A 10-second introduction.
“Let users unlock the door from the app.”
Questions to resolve
Who may unlock it? What if the device is offline? How does the user know the door actually unlocked?
System intent
Only authorized users may request access. Success depends on confirmation from the door.
Software behavior to define
Check authorization, handle missing confirmation, and show an outcome the user can trust.
Illustrative definition to refine with stakeholders, connect to requirements, and share with developers and AI.
Missing intent becomes implementation decisions.
Teams often start with incomplete input. Ellygent helps make the missing questions visible before developers and AI have to fill the gaps.
A feature request arrives. The expected behavior does not.
Delivery pressure and fragmented stakeholder input leave scope, users and failure paths unresolved.
Software requirements exist. Their system origin is unclear.
The team can verify individual requirements while still missing the intended system behavior.
The same questions keep returning during implementation.
Developers reconstruct intent from tickets and conversations. Different interpretations become different behavior.
AI generates code quickly, from incomplete intent.
A coding assistant fills missing decisions with assumptions too. The mismatch may surface only at integration.
From request to definition
Move clarification out of coding and into guided definition.
Start with a new feature request, or bring existing requirements and work back to the missing system intent. Ellygent’s guided steps help you follow essential definition checkpoints.
Frame the problem
Who needs a change, and why? Capture the problem, objectives, system boundary and operating context.
Explore the behavior
Describe scenarios, alternative and failure paths, constraints and interfaces. Identify capabilities and functions where the project needs them.
Connect the requirements
Define expected system behavior, then what the software must contribute. Connect requirements to their origin and make the expected behavior verifiable.
Prepare the handoff
Review the definition, resolve or record open questions, and export selected context for developers and coding assistants.
Use the structure your project needs and revisit decisions as you learn. Steps vary by project mode and enabled modules; requirements editing and context export are connected parts of the workflow. Exploration and prototyping still belong in delivery.
Developer and AI handoff
Give implementation the reasoning behind the requirements.
Connect system intent, scenarios and constraints to system and software requirements. Traceability preserves why the expected behavior exists, so the next developer does not have to reconstruct it.
Review AI proposals before accepting them into your project. Then select the context you want to share: download a definition summary, export specifications, or pull a context package into your development workspace.
See context export and CLI accessReview the definition
Confirm intent with the people who know the system. Accepted AI proposals still need engineering judgment.
Choose what to share
Export the relevant specifications and system context; use a live version or a selected baseline through the Context API and CLI.
Use and refresh the context
Supply the downloaded context to developers and coding assistants. Pull it again when decisions change.
A clearer definition makes the next step easier.
Fewer unresolved decisions entering implementation
Make scope, expected behavior and open questions visible before they become architecture and code.
Less repeated reconstruction of intent
Keep the reasoning behind system and software requirements available to the people building and testing them.
More useful context for coding assistants
Supply selected requirements and system context alongside the implementation task, then review the result against that intent.
A clearer starting point for change
Follow maintained relationships to see what a requirement supports and which connected artifacts may need another look.
Engineering depth when your work needs it.
Keep requirements connected across changes, exchange them with other tools, and add quality or safety analysis where relevant.
AI-Assisted System Context
Use guided definition steps and AI proposals to refine available context. Review and edit proposals before accepting them into your project.
Enterprise Interoperability
Import and export ReqIF requirements to exchange structured specifications with other requirements tools. Check the exchanged content against your toolchain needs.
Traceability Matrix
Create and inspect relationships across specifications. Use the traceability matrix to review coverage and preserve the reasoning behind requirements.
Baseline and Change Control
Save named project snapshots and compare versions to understand what changed. Select a live version or baseline when sharing engineering context.
Requirements Quality Score & AI Review
Request AI feedback on clarity, atomicity and verifiability. Use the findings to refine requirements, with engineers deciding which changes to accept.
Review Workflow with Comments
Collaborative review loops built into the authoring context. Threaded comments link directly to requirements and specifications — keeping systems, software, and QA teams aligned without leaving the tool.
Safety Analysis — ISO 26262 HARA
Use Functional Safety modules to document malfunctions, hazards and safety goals, with severity, exposure and controllability classifications. Engineers remain responsible for the safety analysis.
Context API & CLI Access
Inspect and download selected project context through the Context API and CLI. Supply the files to developers and coding assistants, and refresh them when the definition changes.
Before you start
Ellygent stores structured definition artifacts and relationships so you can see why requirements exist and reuse their context. The goal is to resolve engineering questions and support implementation, not simply produce a longer document.
No. Define enough to address the important uncertainties for the work ahead. Revisit decisions as you learn, and continue exploring and prototyping. The target is avoidable iteration caused by unresolved intent, not useful learning.
Yes. Create or import specifications through supported ReqIF workflows, then identify and document missing objectives, scenarios and system behavior. Engineers recover and connect that upstream context; importing requirements does not recover stakeholder intent automatically.
Keep planning and tracking delivery in your existing tools. Use Ellygent to define expected behavior and the reasoning behind it, then share links or exported context with the people implementing the work. This does not depend on an automatic Jira synchronization.
Review your engineering definition, then use the CLI or Context API to pull selected project context into a local workspace. Supply that context to your coding assistant with the task. Refresh it when the definition changes; the assistant still needs your direction and its output still needs review.
Start with the questions that affect scope and behavior. Lite mode focuses on capabilities and requirements. Engineering mode adds problem framing, objectives, operational context and decomposition when those are needed. Available steps depend on the project mode and modules.
Engineers clarify stakeholder intent, resolve tradeoffs, check proposed artifacts, accept changes and verify the resulting system. AI can help propose or refine content and highlight potential gaps; it cannot know unspoken intent or guarantee correct requirements or code.
Explore the engineering behind the workflow
The True Cost of a Vague Requirement
Vague requirements do not just create confusion. They create measurable cost through clarification meetings, rework, defects, delays, traceability gaps, and AI-generated outputs that lack context.
What Your Subscription Billing Feature Is Missing: Risk Requirements
Happy-path billing requirements let you build the feature. Risk requirements help prevent duplicate charges, wrong refunds, missing audit trails, and silent payment failures.
The NFRs That Will Kill Your SaaS If You Ignore Them
Non-functional requirements are not nice-to-have. Performance, security, retention, availability, and abuse protection are the difference between a SaaS product that scales and one that gets rewritten under pressure.
Give your next feature a clearer starting point.
Bring a request or an incomplete specification. Define the missing intent and build connected context for developers and AI.
Create an account, then set up your project and choose the definition steps it needs.