Overview
ElevAIyt — AI powered risk intelligence and governance
ElevAIyt is an AI powered risk intelligence and governance platform designed to help businesses identify operational risks, security concerns, compliance gaps, and other problematic activities before they become critical issues.
The platform combines structured governance workflows with AI-assisted analysis pipelines to generate contextual risks, severity estimates, recommended controls, mitigation actions, and audit-ready reports tailored to an organization’s environment.
As a backend engineer I helped design the modular service architecture, AI orchestration, assessment management APIs, and report generation pipelines.
100+
Risk scenarios modeled
12
Governance domains supported
30+
Compliance frameworks mapped
99%
Audit traceability
Core features
- AI Powered Risk Discovery — contextual analysis producing risks, classifications, and controls
- Assessment Management — create, scope, track, and archive assessments
- Issue Tracking & Remediation — automatic issue generation and full remediation lifecycle
- AI Assisted Guidance — actionable remediation steps and governance guidance
- PDF Reporting Engine — exportable, audit-ready reports with AI summaries
Platform capabilities
- Context ingestion and structured prompting with schema validation
- Dynamic risk modeling tailored by industry, workflows, and controls
- Scalable assessment store with pagination and background processing
- Secure evidence storage with signed S3 uploads and audit trails
- Async report generation with template-driven rendering
Technical architecture
Python · FastAPI
Primary service framework for APIs and AI orchestration.
PostgreSQL
Primary data store for assessments, risks and audit logs.
AI providers
GPT-based risk generation and guidance orchestration.
AWS S3
Secure evidence storage with signed uploads and lifecycle policies.
SQLAlchemy
ORM layer with optimized queries and indexed schemas.
Docker + EC2
Containerized deployment and infrastructure on AWS.
Challenges
- AI response consistency and malformed outputs
- Dynamic risk modeling across industries
- Large assessment datasets and heavy queries
- Formatting and stability of large PDF reports
- Secure and auditable file uploads
Solutions
- Structured prompting, schema validation, and post-processing
- Flexible contextual risk engine driven by inputs and controls
- Indexed queries, pagination, and background processing
- Template-driven async report renderer with standardized components
- S3 signed URLs, permission checks, and audit logging
Code spotlight
from typing import Dict, List
class AIRiskAnalysisService:
"""
Handles AI powered business risk analysis.
Responsibilities:
- Process business context
- Generate potential risks
- Recommend controls
- Structure AI responses
- Validate AI generated content
"""
async def analyze_business_context(
self,
organization_id: str,
business_description: str,
existing_risks: List[str]
) -> Dict:
"""
Analyze business information and generate risks.
Args:
organization_id:
Unique organization identifier.
business_description:
Detailed business workflow and operational description.
existing_risks:
User provided known risks.
Returns:
Structured AI risk analysis response.
"""
pass
async def generate_risk_controls(
self,
risk_id: str
) -> Dict:
"""
Generate mitigation controls for a risk.
Args:
risk_id:
Risk identifier.
Returns:
Recommended controls and governance guidance.
"""
pass
from typing import Dict
class AssessmentService:
"""
Manages assessment lifecycle operations.
Responsibilities:
- Create assessments
- Track progress
- Maintain assessment history
- Generate issue records
"""
async def create_assessment(
self,
organization_id: str,
assessment_payload: Dict
) -> Dict:
"""
Create a new business assessment.
Args:
organization_id:
Organization identifier.
assessment_payload:
Assessment creation payload.
Returns:
Created assessment object.
"""
pass
async def close_assessment(
self,
assessment_id: str
) -> Dict:
"""
Close an assessment after review completion.
Args:
assessment_id:
Assessment identifier.
Returns:
Finalized assessment details.
"""
pass
from typing import Dict
class IssueTrackingService:
"""
Handles issue lifecycle management.
Responsibilities:
- Create issues
- Assign remediation owners
- Track issue status
- Verify issue closure
"""
async def create_issue(
self,
risk_id: str,
severity: str
) -> Dict:
"""
Create a remediation issue.
Args:
risk_id:
Associated risk identifier.
severity:
Risk severity level.
Returns:
Newly created issue object.
"""
pass
async def resolve_issue(
self,
issue_id: str
) -> Dict:
"""
Mark issue as resolved.
Args:
issue_id:
Issue identifier.
Returns:
Updated issue status.
"""
pass
Build timeline
Week 1–2 · Discovery
Architecture design + ADRs
Identified core workflows, drafted ADRs, and finalized AI orchestration strategy.
Week 3–5 · Foundation
Core platform + AI pipeline
FastAPI scaffolding, Together AI integration, and SQLAlchemy models.
Week 6–10 · Core Services
Assessments, risks & issue tracking
Assessment workflows, issue lifecycle, and AI validation systems.
Week 11–14 · Infrastructure
Deployment & observability
Dockerized services, EC2 deployment, monitoring and backups.
Outcomes
Smarter
AI-driven risk discovery earlier in the lifecycle
IntelligenceFaster
Streamlined assessment and remediation workflows
EfficiencyAudit Ready
Professional PDF reports and full traceability
Compliance