📄 Feature Proposal: "Esdim Auto-Scope" Project: Esdim Platform Feature Name: Intelligent Scope Estimation (Auto-Scope) Type: Productivity / AI Analytics 1. Concept "Esdim" (derived from Estimation + Dimension ) represents the core ability to predict the size, complexity, and timeline of a task automatically. The Esdim Auto-Scope feature analyzes project requirements written in natural language and outputs a "dimensioned" estimate—including time, budget, and risk probability. 2. Problem Statement Project managers and freelancers often struggle with "scope creep" and inaccurate initial estimates. Traditional estimation relies on historical data that is often outdated or difficult to apply to new, unique contexts. Users need a way to instantly gauge the feasibility of a request before committing resources. 3. The Solution (The "Esdim" Feature) The Esdim Auto-Scope feature uses NLP (Natural Language Processing) to parse a project brief and compare it against a global database of similar completed tasks. Core Capabilities:
Complexity Scoring: Assigns a numeric "Esdim Score" (1–100) based on the technical difficulty described. Time Box Prediction: Suggests a range (e.g., "2–3 weeks with a team of 2") rather than a single flat number, accounting for uncertainty. Resource Mapping: Automatically highlights specific skills required (e.g., "Requires Senior DevOps" or "Needs UI Specialist").
4. User Stories
As a Project Manager: I want to paste a client email into Esdim and immediately receive a draft timeline so I can reply faster. As a Developer: I want to see the "Esdim Score" of a ticket to understand if it is a quick fix or a deep refactor. As a Stakeholder: I want to see the confidence interval of the estimate to understand the risk margin. esdima
5. Functional Requirements | ID | Requirement | Priority | | :--- | :--- | :--- | | E-01 | Input Interface: User can input text (min 50 words) or upload a PDF/Docx file. | High | | E-02 | Dimensioning Engine: System identifies keywords related to complexity, volume, and dependencies. | High | | E-03 | Comparative Analysis: System queries the internal database for similar historical projects. | Medium | | E-04 | Output Dashboard: Display the "Esdim Score," predicted timeline, and a "Risk of Delay" percentage. | High | | E-05 | Adjustment Sliders: Allow users to adjust variables (e.g., team size, seniority) to see how the estimate changes dynamically. | Medium | 6. UI/UX Design Mockup (Text Description) Screen: The Estimator
Left Panel (Input):
A large text area labeled "Describe the Task." Example Input: "Build a login system with social auth and 2FA." Users need a way to instantly gauge the
Right Panel (The Esdim Card):
Header: Esdim Score: 34/100 (Low Complexity). Timeline: Estimated 14–18 hours . Confidence: 85% Match (Based on 450 similar tasks). Risk Tag: 🟡 Minor Risk: Third-party API dependency.
7. Technical Implementation Details
Backend: Python (Flask/FastAPI). ML Model: Fine-tuned BERT model for text classification to detect complexity sentiment. Data: Utilizes anonymized historical project data (Jira exports, Git commits frequency). Algorithm:
Tokenize input text. Match tokens against a "Complexity Dictionary" (e.g., "integration" = +5 points, "legacy" = +10 points, "simple" = -2 points). Calculate Gaussian distribution for time estimation.