Business Case
The company needed real-time insight across AWS and partner tools without risking live services
- Live Operations View Move from batch reports to live dashboards so leaders see issues early and act faster
- Hands-free Data Flow Send results straight to monitoring and an audit store to cut manual work and keep records clean
- Faster Incident Response Route priority alerts to the right teams in Slack to shrink detection and fix times
Our Solution
We built a cloud warehouse and price-elasticity engine in 100 days that consisted of the following components:
- Automated Pipelines S3, Glue Crawler, Athena, Step Functions, and Lambda run end to end with zero manual hops
- Comprehensive Monitoring & Alerts Metrics and logs flow to CloudWatch, ELK, and Datadog. CloudWatch Alarms notify Slack
- Integrated Notifications EventBridge, SNS, and SQS coordinate retries and decoupled alert handling
- Dynamic Lambda Functions Transform Athena outputs and stream to Datadog and S3 in near real time
- CI/CD and Reusability GitHub manages generic payload templates and Actions for consistent deploys
Business Case
When two leading park operators merged, each of the 34 venues still ran its own SQL Server. Data teams faced these problems
- Source Fragmentation 2,700 tables lived on separate park servers, so teams pulled numbers from different snapshots
- Schema Drift New or dropped columns crashed hard-coded jobs, triggering late-night fixes
- Zero Audit Trail No run-level log showed row counts or errors, so failures stayed hidden until reports looked wrong
Our Solution
We built a config-driven pipeline that gathers data from all 34 park servers into one Snowflake warehouse.
- Central Config Table All mappings, S3 paths, and load flags now sit in a Snowflake table that analysts can edit
- Object-Level Refresh Tracking Start time, end time, status, and duration are captured for every table, every run
- Row-Count Verification Source and target totals are compared on the fly. Any mismatch triggers an alert
- Dynamic COPY Procedure A stored procedure builds COPY INTO commands for each table and writes outcomes to an audit log
- Airflow-Orchestrated Loads Python tasks move files from SQL Server to S3, call the dynamic COPY, then archive each batch
Business Case
The client’s public URLs exposed critical apps, manual scripts slowed every change, and audits lacked a clear trail. Top pain points
- Security Web Apps, Functions, Storage, and SQL ran with open access, breaching policy
- Compliance No gated approvals or role-based controls left gaps in traceability
- Efficiency Hand-built servers created drift across teams and stretched release windows
Our Solution
We deployed a secure Azure stack with one-command builds, guarded CI/CD, policy-driven governance, and verified data quality.
- Infrastructure Design & Provisioning Secure azure stack with VNets and private endpoints. One-command builds using CAF-aligned Terraform
- CI/CD with GitHub Actions Branch rules, environment gates, rollback pipelines. Releases completed in minutes
- Governance & Compliance Azure policies, Blueprints, RBAC applied subscription-wide. Signed commits ensure complete traceability
- Cloud Migration Workloads moved to private network with cutovers. Parity checks maintained 100% uptime
- Shift-Left Security Azure defender, static scans, and code checks run at pull request and block merges until issues are fixed
Monitoring & Observability Azure monitor, Log analytics, custom dashboards. Alerts trigger before SLA breaches
Our Solution
Three layers deliver the service through the platform that we developed
- User Apps (React & Flutter)
- Chatbot answers and creates documents in Arabic or English
- In-app Google Play and Apple IAP for plan upgrades
- Push alerts via Firebase Cloud Messaging
- Backend (FastAPI & PostgreSQL)
- JWT-secured REST APIs with async SQLAlchemy
- RAG engine using OpenSearch plus OpenAI embeddings
- PII redaction for PDF and DOCX in both languages
- Admin Panel (React Web)
- Role-based access to users, plans, and content
- CMS for FAQs, policies, and country-specific rules
- Broadcast notifications for new features or legal updates
Business Case
Leadership wanted quicker insight and a single source of truth. Here were some of the existing issues
- Data silos Sales, stock, and promo files lived in separate systems—making every report a hunt.
- Long waits Teams queued nightly SaS jobs and still relied on third-party extracts for ad-hoc questions.
- Pricing blind spots No clear view of how discounts pushed or hurt demand and margin.
Our Solution
We built a cloud warehouse and price-elasticity engine in 100 days that consisted of the following components:
- Cloud warehouse Python and Airflow landed 50 billion SaS rows in Snowflake, ready for near-real-time queries.
- Price elasticity modelling Python models test price moves and highlight margin risk before campaigns launch.
- Unified reporting Tableau pulls Hyperion data directly, aligning reports across departments.
- Reusable ingestion framework New data sets plug in fast; no rewrite needed.
Business Case
The computer networking client, active in 150+ markets, logs clicks, alerts, and pings in clashing formats. The mismatch blocked campaign-to-sale links, stalled support updates, and left churn models outdated.
- Data volume Tens of millions of raw JSON events a day, little structure
- Speed to market New releases needed fresh event tags; dev cycles slowed
- Customer Insight Sparse retention models; no single view across apps, routers, and cloud services
- Inventory management Separate pipelines for each project drove compute and license fees up
Our Solution
Inferenz unified all event data, applied a common rule engine, and delivered real-time predictive insight.
- Tool & stack review assessed current collectors, queues, and stores for scale and cost.
- Rule Engine low-code rules let analysts add or change event logic in hours, not sprints.
- Real-time Dashboards Kafka → Snowflake → Tableau gave product, support, and marketing teams one live view.
- Common Event Framework defined one schema for campaign, app, device, and support events; enforced with JSON schema and measured campaign success
- Predictive Models gradient-boosted trees flagged churn risk days earlier; scores streamed back to CRM and push-notification systems.
