Sample engagements,
real-world patterns

Context

A mid-size professional services firm with 12 teams was spending 40+ hours per week on manual invoicing, time tracking reconciliation, and project status reporting. Data lived across spreadsheets, email threads, and a legacy accounting tool.

Constraints

• Budget-conscious — needed phased delivery
• Existing accounting system could not be replaced (vendor lock-in)
• Staff had limited technical skills
• Multiple billing rate structures across teams

Solution

We built a custom billing automation system that connected their time tracking platform to the legacy accounting tool via a middleware layer. A real-time project dashboard replaced the weekly manual status reports, pulling data from 5 source systems.

Tech Stack

Rollout Plan

• Week 1-2: Discovery and system mapping
• Week 3-4: Middleware and API bridge development
• Week 5-6: Dashboard build with team-specific views
• Week 7-8: Testing, training, and phased rollout

Context

An e-commerce brand selling across Shopify, Amazon, and wholesale channels was experiencing frequent oversells, manual stock counts, and delayed reorders. Their team spent hours daily reconciling inventory across platforms.

Constraints

• Three different sales channels with different APIs
• Products had complex variant structures (size, color, material)
• Warehouse used barcode scanning but no digital WMS
• Peak season (Q4) was approaching — tight timeline

Solution

We built a centralized inventory hub that synced bi-directionally with Shopify, Amazon Seller Central, and their wholesale order system. An automated reorder engine monitored stock levels and generated purchase orders based on configurable thresholds and lead times.

Tech Stack

Rollout Plan

• Week 1: Discovery, API access, data modeling
• Week 2-3: Core sync engine with conflict resolution
• Week 4: Reorder logic and PO generation
• Week 5: Testing with real inventory data, QA
• Week 6: Production deployment before peak season

Context

A property management company handling 200+ units was drowning in tenant communication spread across email, phone calls, and text messages. Maintenance requests were tracked on paper, leading to missed repairs and tenant frustration.

Constraints

• Tenants ranged from tech-savvy to minimal digital literacy
• Multiple property types (residential, small commercial)
• Existing vendor relationships needed to be preserved
• Budget for ongoing hosting needed to be minimal

Solution

We built a tenant-facing portal with maintenance request submission, status tracking, and communication history. On the backend, an automated routing system assigned requests to the appropriate vendor based on issue type, property location, and vendor availability. Managers received a dashboard with portfolio-wide metrics.

Tech Stack

Rollout Plan

• Week 1-2: Discovery, UX design, tenant journey mapping
• Week 3-5: Portal build with maintenance workflow engine
• Week 6-7: Vendor integration and notification system
• Week 8: Tenant onboarding, training, and soft launch
• Week 9-10: Iteration based on feedback, full rollout

Context

A network of 8 outpatient clinics processed over 1,200 patient intake forms weekly, all on paper. Scheduling was manual, leading to double-bookings, long wait times, and frustrated staff juggling phone calls with data entry.

Constraints

• HIPAA compliance required throughout the pipeline
• Staff resistance to technology changes
• Legacy EHR system with limited API capabilities
• No downtime allowed during transition

Solution

We built a digital patient intake system with AI-powered form extraction that auto-populated the EHR. A scheduling optimization engine balanced provider availability, patient preferences, and appointment types to eliminate double-bookings and reduce gaps.

Tech Stack

Rollout Plan

• Week 1-2: HIPAA audit, EHR API mapping, compliance review
• Week 3-5: Digital intake form with AI extraction pipeline
• Week 6-8: Scheduling engine and calendar integration
• Week 9-10: Pilot at 2 clinics with staff training
• Week 11-12: Full network rollout with monitoring

Context

A regional logistics company operating 85 vehicles relied on driver phone calls and end-of-day paper logs for fleet visibility. Route planning was based on driver experience rather than data, leading to fuel waste and missed delivery windows.

Constraints

• Drivers had varying levels of smartphone proficiency
• Rural areas with spotty cellular coverage
• Existing GPS hardware was outdated on 30% of the fleet
• Tight margins required quick ROI

Solution

We deployed a real-time fleet tracking dashboard with GPS integration and built a route optimization engine that factored in traffic patterns, delivery windows, and vehicle capacity. An offline-capable mobile app ensured drivers could operate in low-connectivity areas.

Tech Stack

Rollout Plan

• Week 1-2: GPS hardware audit, data pipeline design
• Week 3-4: Real-time tracking dashboard build
• Week 5-6: Route optimization algorithm development
• Week 7-8: Mobile driver app with offline sync
• Week 9-10: Fleet-wide deployment and driver training

Context

A growing B2B SaaS company with 2,000+ customers was struggling with support ticket volume. Their 6-person support team handled 400+ tickets weekly, with average resolution time of 18 hours. Knowledge was siloed in individual team members.

Constraints

• Small support team, no budget for large headcount increase
• Product knowledge was undocumented and tribal
• Existing Zendesk setup needed to stay in place
• Customers expected fast, accurate technical responses

Solution

We built an AI-powered support copilot that ingested their product docs, past tickets, and internal wikis to suggest responses in real-time. The system also auto-categorized tickets, surfaced relevant knowledge articles, and identified patterns for proactive documentation.

Tech Stack

Rollout Plan

• Week 1-2: Knowledge ingestion pipeline and embedding generation
• Week 3-4: AI copilot with suggestion engine and Zendesk integration
• Week 5: Auto-categorization and ticket routing rules
• Week 6: Agent training and feedback loop setup
• Week 7-8: Iteration, accuracy tuning, and full rollout

Context

A mid-size manufacturing plant with 40+ machines experienced an average of 3 unplanned breakdowns per month, each costing $15K-$50K in downtime and emergency repairs. Maintenance was purely reactive with no sensor data analysis.

Constraints

• Machines from 5 different manufacturers with varying sensor capabilities
• Plant floor had limited WiFi infrastructure
• Maintenance team had no data science background
• Cannot disrupt active production lines for installation

Solution

We deployed IoT sensor bridges on critical machines and built a predictive maintenance platform that analyzed vibration, temperature, and operational data to predict failures 2-3 weeks before they occurred. A production analytics dashboard gave floor managers real-time visibility into OEE metrics.

Tech Stack

Rollout Plan

• Week 1-2: Machine audit, sensor compatibility assessment
• Week 3-4: IoT bridge deployment on 10 critical machines
• Week 5-7: Data pipeline and predictive model training
• Week 8-9: Dashboard build with alerting system
• Week 10-12: Full plant rollout with maintenance team training

Context

A 30-attorney legal firm spent thousands of billable hours annually on contract review, due diligence document scanning, and clause extraction. Junior associates spent 60% of their time on repetitive document work rather than substantive legal analysis.

Constraints

• Attorney-client privilege requirements for all data handling
• Contracts in multiple formats (PDF, Word, scanned images)
• Firm used a legacy document management system (DMS)
• Partners needed confidence in AI accuracy before adoption

Solution

We built a document intelligence platform that used OCR and NLP to extract key clauses, flag risks, and generate summary reports from contracts. The system integrated with their existing DMS and provided a confidence score for each extraction, allowing attorneys to focus review time on flagged items.

Tech Stack

Rollout Plan

• Week 1-2: DMS integration, document format analysis
• Week 3-5: OCR pipeline and clause extraction model training
• Week 6-7: Risk flagging engine and summary generation
• Week 8-9: Pilot with 2 practice groups, accuracy validation
• Week 10-12: Firm-wide rollout with partner review dashboard

Context

A restaurant chain with 14 locations used different POS systems across locations, making consolidated reporting impossible. Food waste was significant due to inaccurate demand predictions, and managers spent hours weekly compiling reports manually from each location.

Constraints

• 3 different POS systems across locations (Toast, Square, Clover)
• High staff turnover required minimal training burden
• Real-time data needed despite varying internet quality at locations
• Seasonal and event-based demand fluctuations

Solution

We built a unified data layer that synced all three POS systems into a central dashboard with real-time sales, inventory, and labor metrics. A demand forecasting model analyzed historical sales data, local events, weather patterns, and seasonal trends to predict daily ingredient needs per location.

Tech Stack

Rollout Plan

• Week 1-2: POS API mapping and data normalization design
• Week 3-4: Central data pipeline with real-time sync
• Week 5-6: Unified reporting dashboard build
• Week 7-8: Demand forecasting model training and validation
• Week 9-10: Location-by-location rollout with manager training