Backend systems, AI-ready data, and enterprise automation

Building Backend Infrastructure for Applied AI Systems

Alrhazes designs and develops scalable backend platforms, AI-ready data operations, automation workflows, document pipelines, and enterprise software infrastructure for organizations moving from messy operations to production-grade AI systems.

Start a Conversation
Architecture-first backend developmentAI-ready data and document pipelinesHuman-in-the-loop operational reliability

Engineering the backend and data layer where AI products either scale or fail.

Architecture-first development
Production-grade systems
Enterprise integration readiness

Solutions

Services for backend infrastructure and AI-ready operations.

Alrhazes works where enterprise AI becomes dependable: clean data, integrated systems, digitized documents, governed workflows, and backend services that can operate after launch.

data.layer

Data Cleaning & Structuring

Normalize, validate, deduplicate, and document scattered CRM, ERP, spreadsheet, product, vendor, and customer data before automation or AI work begins.

api.bridge

API & System Integration

Connect ERP, CRM, spreadsheets, dashboards, workflow tools, and internal systems through reliable APIs, middleware, and backend services.

ocr.pipe

Document Digitization

Convert scanned PDFs, invoices, forms, and legacy files into searchable, structured, workflow-ready data with OCR, extraction, and review loops.

label.ops

Data Labeling & Annotation

Create high-quality labeled datasets with documented guidelines, reviewer calibration, quality checks, and audit-ready model training inputs.

model.ops

AI Platform Engineering

Build backend platforms for model routing, retrieval, tool execution, observability, evaluation, and production AI workloads.

erp.addon

ERP AI Add-ons

Add targeted AI capabilities to existing ERP workflows without forcing a full platform replacement or breaking operational controls.

review.loop

AI Operations & Human Review

Run AI-enabled processes with exception handling, feedback loops, reviewer calibration, and quality monitoring after launch.

Selected Project Tracks

Selected Project Tracks

Focused build areas where Alrhazes applies backend engineering, AI-ready data operations, and production-grade system design.

platform.track

Multi-Model AI SaaS Platform

Backend infrastructure for model routing, usage control, customer environments, evaluation hooks, and product-level orchestration.

vision.track

AI-Enabled Video Intelligence

Computer vision architecture for camera-based detection, alerting, forensic search, and operator workflow support.

data.ops

AI Data Operations Layer

Data cleaning, structuring, document extraction, labeling, and human review workflows for companies preparing for automation and AI.

workflow.track

Enterprise Workflow Automation

Backend services, API integrations, and process automation connecting ERP, CRM, dashboards, documents, and operational tools.

agent.track

Agent Infrastructure & Tooling

Permission-aware agent workflows, tool execution layers, memory boundaries, audit trails, retries, and human approval paths.

gov.system

Government & High-Accountability Systems

Documentation-ready backend architecture, role-aware workflows, secure integration, and deployment planning for regulated or institutional environments.

Operating Method

How Alrhazes Builds

A practical path from messy operations to production-grade AI systems.

01
build.sequence

Clean the data reality

Normalize, validate, and structure the records, documents, and operational data before automation begins.

02
build.sequence

Connect the systems

Integrate ERP, CRM, dashboards, spreadsheets, APIs, and workflow tools into a reliable backend layer.

03
build.sequence

Add AI where it survives

Introduce model routing, document intelligence, agent workflows, and automation only where the operating process can support them.

04
build.sequence

Keep humans in the loop

Design review queues, exception handling, calibration, and quality checks for dependable AI operations.

05
build.sequence

Move from PoC to production

Plan monitoring, cost controls, deployment ownership, and maintainable infrastructure from the beginning.

Capabilities

Designed around the systems AI products and operations depend on.

From orchestration layers to data cleanup and document workflows, the work is structured for maintainability, scale, and production ownership.

multi.model

Multi-Model AI SaaS Backend

Routing, tenancy, usage controls, evaluation hooks, and data access patterns for AI products serving multiple customer environments.

agent.runtime

AI Agent Infrastructure

Tool execution layers, permission boundaries, memory systems, retries, audit trails, and human review paths for agentic workflows.

data.quality

Data Foundation & Quality Systems

Pipelines for normalization, validation, master-data cleanup, quality rules, and trustworthy datasets for reporting, automation, and models.

pipeline.api

API and Data Pipeline Development

Typed APIs, background workers, queues, data sync jobs, warehouse-ready pipelines, dashboards, and operational data movement.

doc.intel

Document Intelligence Pipelines

OCR, extraction, classification, human review, and structured outputs that move documents into dashboards, ERPs, and AI tools.

eval.ops

Dataset Labeling & Evaluation Support

Annotation operations, reviewer guidelines, quality checks, calibration workflows, and evaluation datasets for applied AI systems.

enterprise

Government & Enterprise Systems

Secure integration patterns, documentation, role-aware workflows, governance readiness, and deployment planning for high-accountability environments.

automation

Workflow Automation

Operational automation that connects business rules, internal tools, approvals, scheduled jobs, alerts, and exception handling.

human.review

Human-in-the-Loop AI Operations

Review queues, calibration guidelines, quality checks, feedback loops, and operating processes for AI systems that need supervised reliability.

Why Alrhazes

Practical AI infrastructure, anchored in operational truth.

Alrhazes bridges strategy and execution for founders, enterprises, and government teams that need cleaner information, connected systems, and AI that can survive daily work. The operating model pairs regional governance awareness with Bangladesh engineering depth and execution capacity.

  • Backend-first engineering
  • Data quality before AI automation
  • ERP and workflow fluency
  • Secure API and document pipeline architecture
  • Human review, exception handling, and quality loops
  • Riyadh strategy with Dhaka engineering execution

Technology Stack

Practical tools for scalable backend and AI infrastructure.

PythonNode.jsFastAPIPostgreSQLRedisDockerKubernetesLLM APIsVector DatabasesOCR PipelinesERP IntegrationsData ValidationReview QueuesCloud GPU InfrastructureWorkflow Orchestration

Insights

Notes on building AI infrastructure that survives production.

Brief perspectives on backend architecture, agent systems, and the practical work of moving AI platforms into reliable operation.

field note

The Backend Problem in AI Products

AI product reliability is usually decided below the interface: state, latency, evaluation, cost controls, and integration design.

Read perspective

field note

Why Agentic Workflows Need Strong APIs

Agents become useful in production when tool access, audit trails, retries, and permission boundaries are designed into the system.

Read perspective

field note

From PoC to Production-Ready AI Infrastructure

The path from prototype to deployment requires architecture choices that support monitoring, scaling, compliance, and maintenance.

Read perspective

Contact

Bring Alrhazes into the data and backend work that needs to move.

Start with the operational problem: the records, systems, workflows, documents, or AI process that needs to become cleaner, faster, and more reliable.

hello@alrhazes.comRiyadh, Saudi Arabia - Olaya District, Vision TowerDhaka, Bangladesh - Gulshan 2, Tech Park
Start a Project