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Interview vetted Python engineers with relevant project experience.
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Continue with the same developer or add more as your needs grow.
Interview vetted Python engineers with relevant project experience.
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The client’s healthcare RCM platform was facing a growing operational bottleneck. Users were spending hours manually assembling prior authorization packets from fragmented documents while trying to keep up with constantly changing payer requirements.
As the platform expanded across healthcare organizations, the problem only intensified.
The client wanted to streamline prior authorization workflows through intelligent automation—while maintaining HIPAA-compliant processing and enterprise-grade security. But they wanted prior authorization automation embedded directly into their product.
That’s when they collaborated with Infojini. Within 2 weeks, they had interviewed and onboarded an entire team of experts who worked to embed a prior authorization automation capability directly into their RCM platform.
The goal was straightforward: turn fragmented authorization packets into submission-ready requests with minimal manual effort. In short, the automation had to be accurate, auditable, and safe for healthcare environments.
Multi-tenant ingestion pipeline securely processed PDFs and scanned clinical documents, maintaining document lineage, processing history, and audit trails for compliance.
OCR and document understanding pipelines extracted key fields such as member IDs, ICD/CPT codes, provider NPIs, and clinical indicators—even from inconsistent document layouts.
Automated completeness checks ensured required documentation was present before submission, preventing avoidable payer rejections.
Low-confidence extractions were routed through a review workflow, allowing staff to quickly validate or correct data without slowing the process.
A configurable rules engine validated submissions against payer-specific requirements and managed routing and attachment checks.
APIs returned submission-ready authorization packets directly into the ISV platform interface, allowing users to review and submit requests seamlessly.
The system was built using a modern Python-based architecture designed for reliability and scalability.
Built high-performance APIs to power the prior authorization automation services and integrate with the RCM platform.
Managed workflow orchestration for document processing pipelines, validation steps, and automation workflows.
Extracted structured healthcare data from PDFs, scanned documents, and clinical records.
Enabled interoperability with healthcare systems and standardized clinical data exchange.
Stored structured extracted data, workflow states, and authorization packet metadata.
Provided containerized deployment for scalable and consistent environments across development and production.
Enabled automated build, testing, and deployment for continuous product updates.
Ensured code quality and reliability through automated testing frameworks.
Within 8 weeks of deploying the embedded prior authorization automation capability, the client’s RCM platform delivered measurable improvements across operational efficiency, processing speed, and revenue cycle performance.
Behind the solution was a focused team of specialists who collaborated across architecture, AI, backend engineering, infrastructure, and quality assurance.
Designed the overall system architecture, ensuring the automation platform remained scalable, secure, and compliant with healthcare interoperability standards.
Built the document AI and OCR pipelines to extract structured clinical data from complex healthcare documents.
Developed the backend services, automation workflows, and APIs powering the prior authorization processing system.
Managed infrastructure, containerization, and CI/CD pipelines to support reliable deployments and system performance.
Implemented automated testing and validation processes to ensure system accuracy, reliability, and compliance.
Their Python engineers understood our clinical workflows and compliance constraints from day one. We had a production-ready system in weeks — not months.
Prior authorization delays rarely happen because healthcare data is unavailable. They occur because critical information is scattered across documents, formats, and systems that were never designed to work together.
By embedding intelligent automation directly into the RCM platform, the client transformed prior authorization from a manual bottleneck into a scalable product capability.
What once required hours of manual document review and preparation could now be completed in minutes — with higher accuracy, better compliance, and significantly improved operational efficiency.
For the client, this capability became more than just a feature.
We are here to help.
Our client is a specialty retailer operating multiple store locations and product categories across several regions in the US. As store expansion accelerated, merchandising teams struggled to track real-time demand across locations, making inventory planning slow and reactive.
Demand planning relied on spreadsheets built from delayed POS data, often 24–48 hours behind real sales activity. By the time merchandising teams identified trends, stores were already facing stockouts or excess inventory across 15–25% of SKUs.
Promotions introduced unpredictable demand spikes — often 2–3x baseline sales — with no centralized system to forecast impact or adjust replenishment decisions in real time.
Because...
The client needed real-time inventory intelligence but hit a roadblock with hiring delays and limited access to specialized data engineering talent locally.
With a pre-vetted pool of 50+ in-house Python developers, Infojini helped the client onboard a full-stack team (Python + React) within just a couple of weeks, turning months of hiring lag into immediate build momentum.
This enabled the merchandising team to move from delayed, spreadsheet-driven planning to real-time, data-driven decision making.
Working in daily stand-ups with the client’s product and merchandising teams, Infojini engineers built a full-stack inventory command center to unify demand signals, forecast at SKU × location level, and drive real-time replenishment decisions.
Python-based ingestion pipelines consolidated POS, inventory, supplier lead times, promotion calendars, and external signals (e.g., weather) into Snowflake. Airflow-orchestrated workflows enabled reliable, near real-time data availability across the platform.
LightGBM ensemble models generated forecasts at SKU × location granularity, with weekly retraining and drift detection.
Models incorporated seasonality, promotions, and regional demand variability to improve forecast accuracy.
Dynamic safety stock and reorder points computed using forecast variance and supplier lead times.
Enabled precision replenishment across fast- and slow-moving SKUs.
Statistical + ML models identified sudden demand shifts (viral trends, competitor stockouts, weather events).
Triggered real-time alerts for rapid intervention.
Approval workflows validated or overrode reorder recommendations before ERP submission.
Maintained human-in-the-loop control without slowing execution.
React-based interface delivering:Demand visibility by region, store, category, SKUForecasts with confidence intervalsPromotion overlays and YoY comparisonsEnabled teams to act on live demand signals instead of delayed reports.
The platform combined a modern front-end analytics interface with scalable Python data pipelines and machine learning models designed for retail demand forecasting.
Interactive dashboard for demand visibility and replenishment workflows
Visualizing demand trends, forecasts, and promotion overlays
Backend APIs powering forecasting services and inventory analytics
Workflow orchestration for forecasting pipelines and data ingestion
Large-scale retail data processing and transformation
Central analytics warehouse for POS, inventory, and forecasting data
ML-based demand forecasting and anomaly detection
Operational data storage for application workflows
Real-time caching for dashboard performance
Containerized deployment of backend services
Automated testing and deployment of platform updates
Backend and frontend testing for system reliability
The new command center gave merchandising teams real-time demand intelligence, allowing them to react faster to changing retail conditions.
Behind the solution was a focused team of specialists who collaborated across architecture, AI, backend engineering, infrastructure, and quality assurance.
Experience: 10+ years
Built the document AI and OCR pipelines to extract structured clinical data from complex healthcare documents.
Built the document AI and OCR pipelines to extract structured clinical data from complex healthcare documents.
Experience: 9+ years
Python data engineering, demand forecasting pipelines, anomaly detection, ML workflows
Built large-scale data pipelines and forecasting systems, implementing SKU-level demand prediction, safety stock optimization, and automated model retraining.
Experience: 9+ years
React dashboards, FastAPI services, real-time data visualization, workflow automation
Developed high-performance frontends and APIs for data-driven platforms, enabling real-time inventory visibility, replenishment workflows, and operational dashboards.
For the first time, we’re not reacting to demand; we’re ahead of it. The real-time command center has given us confidence in our replenishment decisions. What impressed us most was how quickly the remote team onboarded and delivered a robust, scalable system. Within weeks, we had a production-ready platform that integrated seamlessly with our ERP and scaled effortlessly across multiple regions."
Retail inventory problems are rarely about demand; they’re about visibility and timing.
When demand signals, forecasting, and replenishment decisions are connected, retailers can respond faster and reduce both stockouts and excess inventory.
By combining Python data pipelines, ML forecasting, and a React analytics dashboard, Infojini helped the retailer move from spreadsheet planning to real-time inventory intelligence.
The result: fewer stockouts, lower excess inventory, and faster, more confident merchandising decisions.
We are here to help.
