AI Powered Resume Intelligence and Candidate Matching Platform for resume parsing, candidate normalization, ranking workflows, external submissions, secure authentication, and recruitment operations.
AdiGaze is an AI powered resume intelligence and candidate matching platform designed to transform unstructured resumes into structured candidate records, searchable profiles, ranking workflows, and recruitment decision support.
The project focuses on the complete recruitment data journey: resume ingestion, file storage, AI extraction, candidate normalization, external submission processing, profile matching, ranking, operational feedback, authentication support, and backup aware workflows.
AdiGaze is positioned as a recruitment intelligence foundation rather than a simple resume upload tool. The system connects the frontend recruitment workspace with backend records, storage, serverless processing, AI services, external intake flows, and operational monitoring into one coherent hiring workflow.
The vision behind AdiGaze is to reduce manual effort in recruitment workflows by converting static resume files into structured and useful candidate intelligence.
Recruitment teams often spend significant time reading resumes, extracting candidate details, comparing profiles, tracking submissions, and identifying relevant candidates. AdiGaze addresses this problem by creating an architecture where resumes are processed through AI powered extraction, normalized into candidate records, and made available for matching and review.
The project is built around four primary ideas:
- Convert unstructured resumes into structured candidate data
- Support candidate discovery through matching and ranking workflows
- Allow multiple intake paths through direct uploads and external submissions
- Keep recruitment operations observable through processing state and telemetry
AdiGaze supports resume ingestion through direct upload and external submission workflows. Resume files become the starting point for candidate intelligence rather than remaining static documents.
The AI parsing layer extracts structured information from resumes. This can include candidate identity fields, skills, experience, qualifications, professional history, and other recruitment relevant details.
Parsed resume data is normalized into consistent candidate records. This makes the system more useful for searching, filtering, ranking, and comparing applicants.
The candidate matching workflow supports ranking candidates against hiring needs or search criteria. This allows recruiters to move from manual screening toward structured candidate review.
AdiGaze supports external resume intake flows. External submissions can be received, processed, normalized, and moved into the same candidate intelligence pipeline as directly uploaded resumes.
The platform contains operational widgets and processing state concepts so that users can understand what is happening during file ingestion, parsing, matching, and external submission workflows.
AdiGaze includes authentication workflows and MFA support concepts. Recruitment platforms often handle sensitive candidate data, so authentication and access awareness are important parts of the system design.
The platform includes a backup integration concept for preserving operational metadata and supporting recovery style workflows.
AdiGaze follows a structured recruitment intelligence workflow.
- A resume enters the system through direct upload or external submission.
- The resume file is stored and submitted for processing.
- AI parsing extracts structured candidate information.
- Candidate data is normalized into a searchable profile.
- Candidate records are stored with associated metadata.
- Matching logic compares candidate profiles against hiring criteria.
- Recruiters review ranked candidates inside the workspace.
- Operational widgets show processing state, status, and workflow feedback.
flowchart LR
A[Resume Upload] -->|stores file| B[Resume Storage]
B -->|invokes| C[AI Parsing]
C -->|extracts| D[Candidate Data]
D -->|normalizes| E[Candidate Profile]
E -->|indexes| F[Candidate Records]
F -->|scores| G[Matching Engine]
G -->|ranks| H[Recruiter Workspace]
A2[External Submission] -->|ingests| I[External Intake]
I -->|processes| D
H --> J[Operational Monitoring]
AdiGaze follows a modular architecture where the user interface, candidate workflows, backend records, file storage, serverless functions, AI services, and external integrations remain separated by responsibility.
The frontend workspace provides the recruiter facing experience. This layer includes authentication screens, dashboard layout, resume upload interfaces, candidate hunting screens, external submission views, processing state, and operational widgets.
The candidate data layer stores normalized candidate records, submission metadata, processing status, and recruitment related records.
The storage layer preserves resume files and uploaded documents so that parsing functions can read and process candidate source material.
The serverless processing layer contains function based workflows for resume parsing, candidate matching, external submission ingestion, external submission processing, and MFA related authentication flows.
The AI service layer supports extraction and matching intelligence. This layer is responsible for converting resume content into structured information and supporting scoring workflows.
The integration layer includes external submission intake and backup related workflows. These allow AdiGaze to work with multiple input sources and operational recovery processes.
flowchart TD
subgraph Frontend[Frontend Workspace Layer]
AppShell[Application Shell]
AuthPage[Authentication Page]
ResumeUpload[Resume Upload Interface]
CandidateHunt[Candidate Hunting Workspace]
ExternalSubmissions[External Submissions View]
OpsWidgets[Operational Widgets]
ProcessingState[Processing State]
end
subgraph Backend[Candidate Data and Storage Layer]
Database[Candidate Database]
Storage[Resume File Storage]
Runtime[Serverless Runtime]
end
subgraph Functions[Serverless Processing Layer]
ParseResume[Parse Resume Function]
MatchCandidates[Match Candidates Function]
ExternalIngest[Receive External Resume]
ProcessSubmission[Process External Submission]
TOTP[TOTP Authentication Function]
end
subgraph AI[AI Service Layer]
Gemini[AI Extraction and Scoring]
end
subgraph Integrations[Integration Layer]
DriveBackup[Backup Integration]
ExternalForms[External Submission Sources]
end
AppShell --> AuthPage
AppShell --> ResumeUpload
AppShell --> CandidateHunt
AppShell --> ExternalSubmissions
AppShell --> OpsWidgets
OpsWidgets --> ProcessingState
ResumeUpload --> Storage
ResumeUpload --> Runtime
CandidateHunt --> Database
CandidateHunt --> Runtime
ExternalSubmissions --> Runtime
Runtime --> ParseResume
Runtime --> MatchCandidates
Runtime --> ExternalIngest
Runtime --> ProcessSubmission
Runtime --> TOTP
Runtime --> DriveBackup
ParseResume --> Storage
ParseResume --> Database
ParseResume --> Gemini
MatchCandidates --> Database
MatchCandidates --> Gemini
ExternalIngest --> ProcessSubmission
ProcessSubmission --> Database
DriveBackup --> Database
ExternalForms --> ExternalIngest
The resume intelligence pipeline is the core of AdiGaze. It transforms a resume from a static document into a structured candidate profile that can be searched, ranked, reviewed, and used in recruitment workflows.
flowchart LR
A[Resume File] --> B[File Storage]
B --> C[Parsing Function]
C --> D[AI Extraction]
D --> E[Structured Candidate Data]
E --> F[Normalization]
F --> G[Candidate Profile]
G --> H[Candidate Database]
H --> I[Search and Matching]
I --> J[Ranked Candidates]
J --> K[Recruiter Review]
The workflow begins with a resume file or external candidate submission.
The uploaded document is preserved so that serverless processing can read and analyze the source material.
The parsing function handles the resume processing workflow and prepares the document for AI extraction.
The AI layer extracts structured candidate information from unstructured resume content.
Extracted data is transformed into fields that can support search, filtering, review, and matching.
Candidate information is normalized so records remain consistent even when resumes follow different formats.
The normalized candidate profile becomes a searchable and reviewable object inside the recruitment workspace.
Candidate records can be compared with job requirements or recruiter defined criteria.
The recruiter workspace presents ranked profiles and supports recruitment decision making.
The matching workflow allows AdiGaze to move beyond resume parsing. Candidate records can be scored, ranked, and reviewed against hiring requirements.
flowchart TD
A[Hiring Requirement] --> B[Matching Function]
C[Candidate Records] --> B
B --> D[AI Assisted Scoring]
D --> E[Ranked Candidate List]
E --> F[Recruiter Review]
C --> G[Skills]
C --> H[Experience]
C --> I[Education]
C --> J[Profile Metadata]
G --> B
H --> B
I --> B
J --> B
AdiGaze includes a dedicated external submission flow. This makes the platform flexible enough to receive candidates from outside the main dashboard while still processing all records through the same normalization and matching pipeline.
sequenceDiagram
participant Source as External Source
participant Ingest as External Ingest Function
participant Processor as Submission Processor
participant Database as Candidate Database
participant Workspace as Recruiter Workspace
Source->>Ingest: Submit candidate resume
Ingest->>Processor: Forward submission payload
Processor->>Processor: Normalize candidate information
Processor->>Database: Store candidate record
Database-->>Workspace: Make candidate available for review
sequenceDiagram
participant User as User
participant Upload as Resume Upload UI
participant Storage as Resume Storage
participant Function as Parse Resume Function
participant AI as AI Extraction Service
participant Database as Candidate Database
participant Workspace as Recruiter Workspace
User->>Upload: Upload resume file
Upload->>Storage: Store resume file
Upload->>Function: Invoke parsing workflow
Function->>Storage: Read resume source
Function->>AI: Extract structured candidate data
AI-->>Function: Return extracted fields
Function->>Database: Write candidate profile
Database-->>Workspace: Make candidate searchable
The application shell organizes the recruitment workspace, navigation, routing, dashboard layout, and protected experience.
The authentication area manages user access and login related flows.
The resume upload interface provides the entry point for direct candidate document ingestion.
The candidate hunting workspace supports searching, matching, ranking, and reviewing candidate profiles.
The external submissions area supports candidate intake from outside the main recruiter workflow.
Operational widgets provide visibility into processing state, status, and platform activity.
The candidate database stores normalized candidate profiles, submission records, and recruitment metadata.
Resume storage preserves uploaded documents and allows processing workflows to read resume files when needed.
Edge functions support parsing, matching, external ingestion, external processing, MFA, and backup related workflows.
The AI service layer supports structured data extraction and candidate scoring.
AdiGaze can support multiple recruitment and HR technology workflows:
- Resume parsing
- Candidate profile extraction
- Candidate normalization
- Recruitment automation
- Candidate matching
- Candidate ranking
- External resume submission intake
- Applicant screening support
- Resume database creation
- Recruiter workflow automation
- Hiring intelligence dashboards
- Operational monitoring for processing workflows
- MFA supported recruitment applications
- Candidate backup and recovery workflows
- AI assisted resume parsing
- Structured candidate profile generation
- Candidate matching and ranking workflow
- Serverless processing architecture
- Supabase backed candidate records
- Resume file storage workflow
- External submission pipeline
- Operational processing state
- MFA support concept
- Backup integration workflow
- Clear separation between frontend, backend, AI, and integration layers
- Recruitment SaaS foundation
AdiGaze is designed around sensitive candidate information, resumes, recruitment metadata, authentication state, and hiring workflows. A system like AdiGaze should treat access control, candidate privacy, resume storage, secure processing, and auditability as important engineering concerns.
Sensitive credentials, private endpoints, API keys, service tokens, model access tokens, and environment specific configuration should not be exposed in public documentation or committed to a public repository.
AdiGaze includes architecture choices that support operational performance and maintainability:
- Resume parsing is separated from the user interface.
- Candidate matching is handled as a dedicated backend workflow.
- File storage and candidate records are separated by responsibility.
- Processing state can provide visibility into asynchronous workflows.
- External submission workflows can be normalized before entering the main database.
- AI extraction and scoring can evolve independently from the frontend workspace.
- Advanced candidate search
- Hiring pipeline stages
- Job requirement profiles
- Recruiter notes and review states
- Candidate shortlisting workflows
- Batch resume processing
- Candidate comparison interface
- Improved extraction accuracy
- Candidate fit summaries
- Skill gap analysis
- Experience relevance scoring
- Role specific ranking profiles
- Explainable candidate scoring
- Duplicate candidate detection
- Role based access concepts
- Audit trails for candidate actions
- Team based recruiter workspaces
- Resume processing history
- Notification workflows
- Analytics dashboard for recruitment operations
- Backup and restore maturity
AdiGaze demonstrates the intersection of full stack development, AI assisted document processing, recruitment automation, serverless workflows, candidate data normalization, operational monitoring, and SaaS style product architecture.
The project is structured as a recruitment intelligence foundation where resume ingestion, AI extraction, candidate records, matching logic, external submissions, authentication, and backup workflows are connected into one maintainable system.
Adil Munawar
Web Developer, SaaS Architect, and Project Lead at Nexus Orbits Pakistan
- Portfolio:
https://adilmunawar.vercel.app - GitHub:
https://github.com/adilmunawar - LinkedIn:
https://pk.linkedin.com/in/adilmunawar
AdiGaze
AI powered resume intelligence and candidate matching platform.