AI + Automation Services

Customer Problem / Opportunity: 

• The HR team for a global IT company with 6,000 employees and headcount growing 500+ annually struggled to manage onboarding tasks, documentation, and employee support tickets across regions. 

Challenges: 

• Manual Onboarding: HR sends forms & tracks updates in Excel &/or checklists 

• Repetitive Queries: 70% of employee queries were routine & repetitive 

• Poor Documentation MGMT: Policies scattered in multiple SharePoint folders 

• No Workflow Automation: New employee access requests took days/weeks to discover & complete 

• Lack of Insights: No centralized view of employee engagement or ticket trends 

Our Solution: 

• We created a Copilot-Powered HR Automation Suite leveraging Microsoft ecosystem tools. 

• HR Copilot Assistant: 

  • • Integrated inside Teams for employees to ask policy & process questions 

    • Powered by Azure OpenAI and trained on internal HR knowledge base (SharePoint + policy PDFs)

• Power Automate Flows: 

  • • Automated onboarding checklists, system access, ID creation, payroll setup 

    • Escalation flows triggered when SLA breached 

• Employee Analytics: 

  • • Power BI dashboards showed real-time onboarding progress and HR ticket categories 

• SharePoint Document Hub: 

  • • AI tagging organized 1,200+ policy and training documents for easy retrieval 

The Results: 

• 65% faster HR query resolution 

• 40% reduction in onboarding completion time 

• 30% improvement in employee satisfaction 

• 0 (zero) SLA breaches in critical onboarding workflows 

• 50% reduction in HR workload - freeing focus for strategic tasks 

Technology Used: 

• Microsoft Copilot, Power Automate, SharePoint Online, Power BI, Power Virtual Agents, Azure, OpenAI, Microsoft Teams

Customer Problem / Opportunity: 

• A mid-sized financial services company preparing for external audits and expanding to regulated markets struggled preparing audit-ready documentation across multiple jurisdictions.  

Challenges: 

• Scattered Documents: Policies, contracts, and financial statements were spread across Google Drive, Dropbox, and ERP 

• Time Drain: Financial team spent 300 hours annually compiling compliance documents 

• Audit Risks: Missing supporting evidence increase audit penalty exposure 

• Stress on CFO: Audits disproportionately consumed valuable leadership attention  

Our Solution: 

• We deployed an AI Compliance & Audit Assistant. 

• Documentation Summarization: 

  • • LLMs auto-generated audit-ready summaries with citations to source documents 

• GAP Analysis: 

  • • AI highlighted missing records against required compliance checklists (SOX, IFRS) 

• Evidence Linking: 

  • • Automated linking of every figure to its source ledger or contract 

• Version Control: 

  • • Chain-based memory ensured consistent use of the latest financial data 

• Collaboration Workflow: 

  • • n8n automation flagged items to responsible finance staff 

The Results: 

• Faster Prep: Audit document prep time cut from 4 weeks to 5 days 

• Risk Reduction: Compliance gaps identified 95% faster 

• CFO Relief: CFO time spent on audits dropped 60% 

• Continuous Readiness: CFO maintained "always audit-ready" status 

• Investor Confidence: Improved governance perception during funding talks   

Technology Used: 

• LLMs, LangChain Memory, n8n Workflow, AI Compliance Agents, Secure Cloud APIs 

Customer Problem / Opportunity: 

• A mid-sized financial services firm in New York with 200 employees and $75M annual operating budget lacked real-time visibility into departmental spending and budget variances.  

Challenges: 

• Fragmented Expense Data: Multiple ERP systems and manual spreadsheets 

• Slow Budget Reporting: Month-end closing and variance reports took 7+ days 

• Overbudget Risks: Department heads often overspent without real-time alerts 

• Limited Predictive Insights: No ability to forecasts future spending based on trends  

Our Solution: 

• We deployed a Finance Expense Dashboard on Power BI. 

• Unified Data Model: 

  • • Aggregated ERP and spreadsheet data for consistent reporting 

• Real-Time Budget Tracking: 

  • • AI highlighted departments approaching or exceeding limits 

• Variance Analysis: 

  • • Dashboards automatically calculated and visualized planned vs actual spending 

• Trend Forecasting: 

  • • Predictive AI suggested upcoming monthly expenses using historical patterns 

• Collaboration Integration: 

  • • Power BI reports shared across teams with automated notifications 

The Results: 

• Speed: Monthly reports reduced from 7 days to 1 day 

• Cost Savings: Overspending reduced 18% annually ($1.35M) 

• Transparency: Leadership gained instant visibility into departmental budgets 

• Predictive Control: Forecasts enabled proactive adjustments before overspending 

• Efficiency: Finance team saved 80+ hours per month on manual reconciliation 

Technology Used: 

• Power BI, Python for predictive modeling, DAX, Power Automate, Azure, SQL, ERP connectors, AI-based anomaly detection 

Customer Problem / Opportunity: 

• A private equity (PE) firm managing had manual, reactive, and slow tracking of financial health for each of its 8 portfolio companies. 

Challenges: 

• Data Overload: Monthly financial reports from 8+ companies, each with unique ERP systems 

• Delayed Red Flags: Issues spotted only at quarter-end reviews 

• Manual Tracking: Analysts manually compared KPIs against benchmarks 

• Risk Blind Spots: Hidden anomalies in cash flow, AR/AP cycles, and revenue recognition 

Our Solution: 

• We deployed a Portfolio Health AI Monitoring System. 

• Automated Report Ingestion: AI read financial statements across multiple formats (Excel, PDF, ERP exports) 

• Anonaly Detection: Identified unusual revenue dops, inflated AR, or abnormal cost spikes 

• Comparative Benchmarking: Benchmarked portfolio company performance against industry averages 

• Predictive Alerts: CFOs & partners received proactive alerts before board meetings  

The Results: 

• Faster Decisions: Monthly anomaly insights became real-time 

• Loss Prevention: $4M value preserved by catching early financial stress 

• Investor Trust: Improved transparency with LPs 

• Scalable Oversight: Analysts focused on insights, not manual reconciliations 

Technology Used: 

• AI-powered anomaly detection models, OCR + NLP for financial docs, LamChain for memory across portfolio companoes, n8n workflow automation, Python ML pipelines, AWS secure cloud  

Customer Problem / Opportunity: 

• A multi-specialty healthcare network with hospitals and clinics across multiple states serving 100K+ patients annually faced inefficiencies in patient scheduling, discharge summaries, and inter-department coordination. 

Challenges: 

• Complex Scheduling: Overlapping doctor availability and appointment clashes 

• Manual Record Updates: Discharge summaries manually typed and stored in shared drives 

• Slow Coordination: Cross-department referrals delayed due to email-based communication 

• High Admin Burden: Nurses spent hours updating SharePoint trackers 

• Compliance Risks: Difficulty maintaining up-to-date records for audits 

Our Solution: 

• We developed a Healthcare Copilot Agent integrated with Microsoft 365. 

• AI Scheduling Assistant: 

  • • Copilot synced physician calendars and suggested optimal patient slotting 

    • Power Automate handles reschedules, cancellations, and notifications to patient via Teams chatbots 

• Clinical Workflow Automation: 

  • • Discharge notes automatically summarized by Copilot and uploaded to SharePoint patient records 

    • Automated audit trail maintained through versioned SharePoint files 

• Collaborative Teams Environment: 

  • • Doctors and nurses access patient summaries, referrals, and reports through Copilot chat in Teams 

The Results: 

• 45% reduction in admin clerical time 

• 33% reduction in patient wait time 

• 100% audit readiness via auto-versioned documents 

• $800K saved annually from manual data entry 

• 8-10 hours per week saved for clinical teams (increasing staff satisfaction)  

Technology Used: 

• Microsoft Copilot, Power Automate, SharePoint Document Libraries, Teams Chatboats, Outlook Integration, Microsoft Graph API

Customer Problem / Opportunity: 

• A mid-sized healthcare provider group in Texas, managing 200+ physicians and outpatient centers, struggled with slow and error-prone claim reviews, leading to delays in reimbursements and compliance risks. 

Challenges: 

• High Claim Volume: 25,000+ insurance claims per month were processed manually 

• Errors & Rejections: 12-15% of claims faced rejection due to miscoding or incomplete reviews 

• Compliance Risk: Delays in CAC (Clinical Audit & Compliance) reviews led to potential penalties 

•  Staff Burnout: Teams spent long hours verifying claims, with frequent backlogs 

Our Solution: 

• We deployed a Healthcare Claims AI Review Agent integrated with existing EHR and billing systems. 

• Automated Claim Scanning: 

  • • AI agents parsed claim documents, checking diagnosis codes against treatment records 

• LamChain Integration: 

  • • Chain-based memory allowed the bot to "remember" prior claim history for recurring patients 

• Math & Anomaly Detection: 

  • • Automated identification of unusual billing patterns (ie. high-value discretionary procedures) 

• n8n Workflow Automation: 

  • • Claims with red flags were routed automatically to compliance staff with supporting evidence 

• Language Support: 

  • •  Integrated Ollama for on-the-fly summarization and English translation of claims from Spanish-speaking patients 

The Results: 

• Faster Processing: claim turnaround reduced from 5 days to 12 hours 

• Error Reduction: claim rejection rate dropped from 15% to 3% 

• Cash Flow Improvement: $3.5M annual acceleration in reimbursements 

• Compliance Strengthened: every flagged claim carried an audit trail, lowering penalty exposure 

• Staff Productivity: the team redirected focus from manual reviews to handling exceptions  

Technology Used: 

• AI Agents, LamChain, Ollama, Python NLP Models, n8n Workflow, AWS Lambda, HIPAA-compliant RPA bots 

Customer Problem / Opportunity: 

• A large supply chain management company handling the procurement, storage, and distribution of goods across multiple regions faced challenges in managing inventory, order processing, and shipment tracking which led to delays, inaccuracies, and an increased workload on their team. 

Challenges: 

• High Volume & Complexity: The company managed a vast inventory with diverse product categories and fluctuating demand patterns 

• Manual Errors: Order entry, inventory tracking, and shipment scheduling were prone to mistakes, leading to delays and customer dissatisfaction 

• Lack of Visibility: Real-time tracking of orders and inventory levels was cumbersome, causing delays in decision-making 

• Scalability Issues: Growing operations stretched the team's capacity to handle increased orders and inventory 

• Operational Bottlenecks: The manual workflows led to delays in procurement and restocking, especially during peak demand periods 

Our Solution: 

• We implemented an AI-powered automation system designed to optimize supply chain processes through intelligent decision-making and real-time data tracking. 

• AI-Powered Order Management: 

  • • AI agents automatically processed orders, ensuring accurate product allocation, order prioritization, and vendor coordination 

• Real-Time Inventory Tracking: 

  • • Integrated sensors and APIs monitored inventory levels in real-time, updating stock counts and generating reordering alerts based on predefined thresholds 

• n8n Workflow Automation: 

  • • The system automated critical supply chain tasks, such as order routing, shipment tracking, and vendor communication 

• Predictive Analytics: 

  • • AI algorithms forecasted demand patterns and optimized inventory levels to reduce stockouts and overstock situations 

• Shipment Tracking Integrations: 

  • • Seamless integration with logistics providers allowed for real-time tracking of shipments, providing both the company and its customers with up-to-date delivery information 

• Exception Management: 

  • • The system flagged potential issues like shipment delays or inventory discrepancies for manual review and resolution 

The Results: 

• 60% reduction in order processing time 

• 85% reduction in operational errors for inventory management and order fulfillment 

• Eliminated the need for additional team member to manually handle order processing 

• Automation system easily scaled with company's growth, handling larger order volumes and inventory without additional resources 

Technology Used: 

• AI Agents, Make.com, Python, Custom APIs, Predictive Analytics, Google Sheets API, AWS Lambda, Real-Time Logistics API 

Customer Problem / Opportunity: 

• A mid-sized US manufacturing company specializing in industrial equipment parts with plants across Ohio and Michigan managed a global supply chain but faced frequent delays, rising costs, and unplanned equipment downtime that hurt delivery schedules. 

Challenges: 

• Unpredictable Downtime: Machines broke down unexpectedly, halting production lines 

• Inventory Inaccuracy: Manual tracking caused both overstock and stockouts, leading to missed orders 

• Inefficient Procurement: Buyers lacked AI insights in supplier performance and raw material pricing trends 

• Fragmented Systems: Maintenance logs, procurement data, and ERP systems were siloed 

• Rising Costs: Frequent emergency repairs and expedited shipping increased operational expenses  

Our Solution: 

• We deployed an AI-powered manufacturing optimization platform integrating supply chain + predictive maintenance. 

• AI Supply Chain Optimization: 

  • • AI agents predicted raw material demand using historical sales + seasonality trends 

    • LamChain allowed context retention of supplier performance (ie. delivery times, defect rates, cost changes) 

    • Automated procurement bots issues purchase orders when stock thresholds were breached 

• Predictive Maintenance AI Agent: 

  • • Sensors on CNC machines + IOT data fed into an AI anomaly detection model 

    • Ollama-powered reasoning flagged unusual vibration/temperature readings 

    • Maintenance tasks auto-scheduled in ERP before breakdowns occurred 

• Mathematical Cost Modeling: 

  • • AI agents simulated impact of downtime vs proactive maintenance 

    • CFO dashboards showed real-time "savings gained" from avoided downtime 

• Workflow Automation: 

  • • n8n bots connected ERP, procurement systems, and vendor communication into one automated loop 

The Results: 

• 35% reduction in unplanned outages and downtime 

• 95% on-time delivery and customer order fulfillment rate (up from 72%) 

• 90% real-time inventory accuracy (up from 65%) 

• 40% costs cut for emergency repair and expedited logistics 

• $6M annual EBITDA improvement from reduced downtime + operational efficiency 

Technology Used: 

• AI Agents, LamChain, Ollama, Python ML Models, IoT Sensor Data, n8n Workflow, ERP API Connectors, AWS Lambda 

Customer Problem / Opportunity: 

• A mid-sized property management company managing residential rental units across multiple cities with hundreds of utility bills processed monthly needed to automate its billing process to reduce manual work and improve accuracy.  

Challenges: 

• Volume & Complexity: Bills differed by vendor, property type, and tenant occupancy 

• Human Error: Manual proration and entry led to mistakes and tenant billing disputes 

• Scalability: Increasing property volume stretched the team's capacity 

• Tenant Confusion: Inconsistent charges affected tenant trust 

• Multiple Bill Formats: Bills came from 15_ vendors with varying templates  

Our Solution: 

• We built an AI-powered automation workflow combining OCR, AppFolio integration, and n8n for intelligent decision-making. 

• Google OCR Integration: Extracted accurate data from scanned bills regardless of layout 

• n8n Workflow Automation: Managed the entire process - data routing, proration logic, decision paths, and fallback handling 

• Rule-based Bill Classification: Mapped utility type, service period, and property unit automatically 

• Proration Calculator: Applied tenant move-in/move-out dates to calculate partial charges 

• Direct AppFolio Integration: Entered bills and assigned amounts to tenants or owners 

• Exception Alerts: Flagged incomplete or unmatched bills for manual review 

• Audit Logging: Maintained detailed records of all processed bills and calculations 

The Results: 

• Time Efficiency: Reduced billing time from 5 hours per week to 90 minutes 

• Accuracy Boost: Cut billing errors by over 90% using rule-based logic and validation 

• Resource Savings: Eliminated the need for an additional billing staff member 

• Tenant Trust: Accurate, transparent billing increase satisfaction 

• Scalability: Easily handles growing property counts without increase workload 

Technology Used: 

• Google OCR, n8n (AI Automation), Python, AppFolio API, AWS Lambda, Google Sheets API, Google Calendar API 

Customer Problem / Opportunity: 

• A mid-sized property management company managing over 2,000 rental units across multiple cities. The company faced operational inefficiencies in handling inquiries, scheduling tours, application tracking, and lease renewals. Their goal was to streamline tenant communication and automate repetitive leasing workflows using AI.

Challenges: 

• Manual Lead Handling: Leasing agents were overwhelmed with responding to tenant inquiries across different channels.

• Fragmented Systems: Tour scheduling, application processing, and lead management operated in silos, resulting in delays and errors.

• Limited Personalization: The team lacked intelligent automation to personalize communication based on user behavior.

• Low Visibility: Management lacked real-time insights into lead performance, tour no-shows, and lease funnel drop-offs.

• High Operational Costs: Routine follow-ups, document collection, and status tracking required constant staff involvement.

Our Solution: 

• We developed a full-stack AI-powered SaaS platform designed for multi-tenant property management companies. 

• AI Leasing Assistant: Built a GPT-4-based chatbot for lead qualification, tour scheduling, and application status via web and WhatsApp.

• Multi-Tenant Admin Dashboard: Enabled each client to configure their chatbot, view analytics, and manage AppFolio integrations.

• Calendar & AppFolio Sync: Integrated with AppFolio for listings and leads, and Google Calendar for tour booking coordination.

• Customizable SaaS Platform: Developed with C# (.NET), PostgreSQL, and React, allowing full tenant isolation and secure scaling.

• Role-Based Access & Security: Implemented JWT-based auth, audit logging, and tenant-level API key handling.

• Real-Time Insights: Delivered dashboards for lease pipeline analytics, agent productivity, and lead source effectiveness.

The Results: 

• Increased Efficiency: Reduced agent workload by 60% by automating 80%+ of lead and applicant conversations.

• Faster Tour Scheduling: Prospects could self-schedule tours in real time without needing agent intervention.

• Improved Security: Tenant-specific data storage with AES-256 encryption and role-based access safeguards.

• Better Visibility: Management gained real-time analytics on lease progress, tour bookings, and agent performance.

• Scalability: The multi-tenant model allowed the company to onboard additional regional branches seamlessly.

• Higher Lead Conversion: Personalization and real-time engagement boosted inquiry-to-application conversion by 40%.

Technology Used: 

• C# (.NET Core), React, GPT-4 (OpenAI), PostgreSQL, Redis, AWS S3, Voiceflow, Twilio, AppFolio API, Stripe, AES-256 Encryption

Customer Problem / Opportunity: 

• A US-based real estate investment group managing 120+ commercial & residential units across New Jersey was overwhelmed with tenant services, rent collection, maintenance scheduling, and tenant queries. 

Challenges: 

• Manual Rent Reminders: Property Managers manually followed up with tenants for late payments 

• Maintenance Requests Lost: Emails & calls were often missed, leading to tenant dissatisfaction 

• No Analytics: No clear view of occupancy trends or cash flow forecasting 

• Scaling Issues: Expansion to new properties increased workload disproportionately  

Our Solution: 

• We implemented a Tenant AI Assistant integrated with Yardi and QuickBooks. 

• Rent Collection Bot: 

  • • AI agents sent automated reminders via SMS/Email, escalating overdue cases 

• Maintenance AI Agent: 

  • • Logged tenant complaints, auto-scheduled vendors, and updated tenants in real time 

• Cash Flow Dashboard: 

  • • Predictive analytics projected rent inflows & vacancy risk 

• Ollama-based Chat Agent: 

  • • Provided instant responses to common tenant queries (ie. lease renewals, due dates, maintenance status) 

• Workflow Automation: 

  • • Integrated calls, emails, and WhatsApp into a single reporting system 

The Results: 

• 40% improvement in on-time rental payments within 90 days 

• Reduced tenant response turnaround from 48 hours to < 6 hours 

• Real-time transparency dashboards for CFO & property mangers 

• Scalable solution for 120+ properties without adding new staff 

• Tenant satisfaction rates improved due to quick service handling   

Technology Used: 

• AI Agents, Ollama, Power BI, Yardi RPA Connector, QuickBooks API, Make.com 

Customer Problem / Opportunity: 

• A property management company responsible for overseeing residential rental properties across multiple locations. They relied heavily on manual processes to manage property listings, handle inquiries, process rental applications, conduct screenings, and coordinate move-ins. Due to fragmented workflows and reliance on human input, the team faced delays, data inconsistencies, and frequent follow-ups. With growing volume, maintaining responsiveness and accuracy became increasingly difficult.

Challenges: 

• Many disconnected systems and manual steps. 

• No trigger to detect new guest cards or applications in AppFolio.

• Manual document review for IDs, income proofs, and employment verifications.

• Delays in sending reminders for missing information.

• Errors in calculating income-to-rent ratio from uploaded stubs or bank statements.

• Lease creation required manually checking deposit status, adding addendums, and collecting e-signatures.

• Move-in instructions and key collection coordination were done manually, leading to confusion and missed tasks.

• There was no sandbox or dummy setup for testing new workflows before live execution.

Our Solution: 

• We built an intelligent Leasing Automation Bot using Electroneek, integrated with AppFolio and ShowMojo. 

• Web Scraper Engine: To monitor AppFolio for new guest cards and rental applications.

• Document Verifier: Used OCR to extract and validate income, ID, and employment data from uploaded attachments.

•  Trigger-Based Messaging: Automated reminders sent via email/SMS to applicants with incomplete documentation.

•  Decision Engine: Calculated income-to-rent ratio, flagged exceptions, and routed borderline cases for manual review.

•  Lease Workflow Bot: Auto-generated lease drafts, attached property-specific addendums, and managed e-signature flow.

•  Move-in Coordination: Sent move-in instructions, tracked key handover schedule, and notified stakeholders of occupancy status.

The Results: 

• Transformed a multi-step manual process into an automated pipeline.

• Reduced application processing time by over 60%.

• Ensured compliance and accuracy in screening, lease creation, and document handling.

• Reduced missed follow-ups and delays with automatic reminders and notifications.

• Enabled scalable handling of applications without increasing staff.

• Improved tenant experience with faster application responses and onboarding.

• Empowered management with real-time visibility into lead conversion and leasing pipeline through bot logs and dashboards.

Technology Used: 

• RPA, AI, Eletroneek, AppFolio, ShowMojo 

Customer Problem / Opportunity: 

• A boutique commercial real estate advisory firm that represents high-profile law firms and corporate tenants across the U.S. The company managed its leasing RFP process manually using segmented Word documents and email exchanges. Each RFP was filled by multiple landlords, returned via email, and then manually reviewed and compared. The lack of automation caused delays, inconsistencies in responses, and limited visibility for internal teams.

Challenges: 

• Manual RFP response workflow, where landlords filled out Word/PDFs offline, making tracking, version control, and comparisons difficult.

• No centralized system for tracking landlord responses

• Inconsistent formatting and missing data across returned RFPs

• Manual extraction of critical deal terms into Excel for side-by-side comparison

• Inability to flag deal-breaker clauses in real-time

• Delays in response review and missed deadlines due to inefficient processes

Our Solution: 

• We developed a custom web application that allowed brokers to send structured RFPs as fillable PDF forms embedded directly within the web interface using Apryse SDK (PDFTron). 

• Editable, secure in-browser PDF forms for landlords

• Field-level validation and autosave

• Digital signature capability

• Admin dashboard for tracking submission status and timing

• Automatic extraction of key fields (rent, term, TI allowance, etc.) for comparison

• Exportable summaries and visual comparison dashboards

The Results: 

• The solution transformed the RFP process into a centralized, secure, and real-time system that ensured data integrity, improved landlord engagement, and enabled fully digital RFP lifecycle management. 

• Faster RFP response collection with in-browser editing

• Automated comparison of landlord terms across multiple proposals

• Role-based access to ensure data security and client-specific controls

• Actionable insights for internal teams and decision-makers

• Fewer errors, more complete data capture

• Improved response time and increased operational efficiency

Technology Used: 

• Apryse SDK (PDFTron) 

Cost depends on project needs, efforts, & complexity so it varies by customer. Typically it's based on results + savings so there's a clear ROI.