Proof That We Deliver
We've built production AI systems and delivered measurable results for our clients. Here's the evidence.
Production Systems We Built & Operate
Real products serving real customers, not demos
WysLeap
Visitor Analytics Platform
Processing 10M+ events per month
Scalable infrastructure handling massive event volumes
Sub-100ms response times
Lightning-fast analytics queries for real-time insights
What This Proves
We've built production AI systems that scale, perform, and deliver value to customers.
PulsairSocial
Social Listening Platform
Comprehensive analytics across 7 platforms
Twitter, TikTok, Pinterest, YouTube, Facebook, Instagram, and LinkedIn
Actionable insights for strategy
Data-driven recommendations to optimize your social presence
What This Proves
We understand data analytics and building platforms that help businesses make smarter decisions.
Proprietary Technology for Precise Cost Analysis
Our Patent-Pending Unified Dependency Graph provides a systematic approach to mapping code to infrastructure costs, eliminating manual analysis and estimation.
The Unified Dependency Graph mathematically links every line of static code to the dynamic infrastructure it spawns. This enables precise cost tracing with granular accuracy, allowing us to identify optimization opportunities that standard tools cannot detect.
Technology protected by U.S. Patent Pending Application No. 63/952,353
What we've shipped for clients
Sector-anonymized engagements across enterprise and mid-market. References available under NDA.
$4M/Year Saved with Template-Less Document Automation
Leading Supply Chain Management Firm, USA
The Challenge
- Purchase order and invoice processing required heavy manual intervention with traditional OCR systems that struggled with non-standard layouts
- High customer escalation rate driven by accuracy issues in billing and document processing
- Existing template-based processing pipelines could not scale to the diversity of document types coming from the customer base
- Strict regulatory compliance requirements added overhead to every processing exception and human review
- Operations team capacity was the bottleneck on growth — not customer demand
Our Approach
- Discovery: Mapped the document landscape across PO and invoice types, classified data flows, and identified the regulatory boundaries the new system would need to operate within.
- Architecture: Designed a template-less, self-learning approach using LLM-powered reasoning instead of brittle OCR templates. Engineered for high-volume throughput with reviewer-in-the-loop checkpoints on low-confidence outputs.
- Build: Built the document intelligence pipeline with structured extraction, confidence scoring, automated routing for human review, and full audit trails on every decision the system made.
- Hardening: Integrated with existing compliance and audit infrastructure. Validated accuracy at production scale (8,000+ docs/day) and tuned the human-in-the-loop thresholds based on actual reviewer feedback.
- Handoff: Trained the operations team on system monitoring, exception handling, and the routing rules. Production deployment with rollback procedure and post-engagement check-in scheduled.
The Results
$4M / year
Operational cost reduction
Material P&L impact, sustained year-over-year
99.5%
Document accuracy at production scale
70% improvement vs. prior OCR-based system
8,000 / day
≤30 seconds per document
55–60% touchless processing; 90% reduction in customer escalations
Engagement reference and detailed metrics available under NDA.
From Zero AI to Production-Ready in 10 Weeks
Veterinary Healthcare Company
The Problem
- 4 veterinarians spending 45-60 minutes per visit writing detailed SOAP notes covering multiple species (canine, feline, exotics) with varying clinical complexity
- Processing 30-40 patient visits daily; veterinarians staying 1-2 hours after clinic hours to complete documentation backlog
- Manual note-taking during exams disrupted client interaction; transcription errors and missed clinical details requiring time-consuming corrections
- Evaluated commercial solutions (ScribbleVet, VetSOAP, CoVet) but costs of $3-5 per summary were prohibitive at their volume (900+ visits/month)
- No internal AI expertise; needed custom solution that integrated with their practice management system and handled species-specific veterinary terminology accurately
Our Approach
- Week 1-2: Conducted stakeholder interviews with 4 veterinarians and practice managers, analyzed 300+ anonymized SOAP notes across species (canine, feline, avian, small mammals) to understand documentation patterns, clinical terminology, and species-specific nuances. Mapped workflow from exam → documentation → PMS integration. Established data privacy requirements aligned with state veterinary privacy laws and AVMA ethical guidelines.
- Week 2-3: Compared 4 models (GPT-4, Claude, GPT-3.5, Gemini) on 200+ sample SOAP notes across different species and visit types. Evaluated accuracy of Subjective, Objective, Assessment, and Plan sections, veterinary terminology precision, cost per summary, and response latency. Selected GPT-4 for best balance of clinical accuracy, proper veterinary terminology, and cost-effectiveness.
- Week 3-5: Built working prototype with selected model, refined prompts to generate structured SOAP notes with proper veterinary terminology, species-specific clinical reasoning, and accurate capture of physical exam findings. Created editable templates allowing veterinarians to review and refine AI-generated notes. Tested on 300+ notes with iterative veterinarian feedback. Integrated with practice management system API for one-click export.
- Week 6-7: Implemented secure infrastructure with encryption at rest and in transit, audit logging for all data access, and role-based access controls aligned with state veterinary privacy requirements. Built quality monitoring dashboard to track accuracy metrics and flag notes requiring review. Conducted security and compliance review with practice management team.
- Week 8-10: Trained 4 veterinarians and support staff on using the system, including best practices for reviewing and editing AI-generated notes. Set up cost monitoring and alerting dashboard. Deployed to production with gradual rollout (10% → 50% → 100% of visits over 2 weeks), collecting feedback and fine-tuning prompts based on real-world usage.
The Results
50-60% Reduction
45 minutes → 18-22 minutes per SOAP note
Saving ~25-30 minutes per visit after review/editing. Veterinarians completing notes same-day instead of staying late, allowing 1-2 more patients daily per veterinarian
$0.23
vs $3-5 quoted by vendors
Processing 900+ summaries monthly at 93% lower cost than alternatives
Growing
Team building second AI feature with our guidance
Internal team gaining confidence and capability to extend AI features with ongoing support
We'd been burned before by consultants who delivered beautiful presentations but no working code. FusionLeap was different. They showed us working prototypes every week, used our actual visit notes, and by week 6 we had something our veterinarians could actually use. The cost savings alone justified the investment, but the time savings for our team—that's been transformative. Our vets are getting home on time, and patient care quality has improved because they're not rushing through documentation.
— Director of Technology, Veterinary Healthcare Organization
Open-Text Search That Halved Pre-Sales Support Cost
Industrial Sewing Threads Manufacturer, UK
The Challenge
- Customer-facing teams spending significant time on pre-sales support — manually matching open-text customer requests against a complex thread catalog (color, weight, material, application)
- Quote generation cycles slowed by manual database lookup against thousands of SKUs
- Search experience was rigid: customers needed to know exact thread codes or precise specifications
- Limited scalability — the only path to handle more volume was hiring more support staff
Our Approach
- Discovery: Mapped the existing thread catalog schema, sales workflow, and the linguistic patterns customers use when describing what they need (color names, end-use applications, fuzzy descriptions).
- Architecture: Designed a text-to-SQL system that interprets open-text queries, dynamically filters the thread database with context-aware matching, and returns precise candidate matches with color and material logic baked in.
- Build: Built the search and quote-generation interface for both customer-facing and internal sales teams. Added effortless discovery flows, time-optimized matching, and color-matching logic.
- Hardening & Deploy: Tuned ranking logic against historical sales data. Validated precision improvements on real customer queries. Production deployment with feedback loop for continuous refinement.
The Results
20–30% ↓
Reduction in support overhead
Same volume, materially lower cost-to-serve
40% Faster
Cycle time reduction
Faster quotes mean better win rates and customer experience
2× Capacity
Without adding support staff
15–20% improvement in search precision; 25–35% faster search experience
Engagement reference and detailed metrics available under NDA.
Complete Platform from Concept to Launch in 8 Weeks
Educational Services Platform
The Challenge
- Single tutor managing 50+ students across multiple subjects, scheduling conflicts and manual coordination becoming unmanageable
- Using multiple disconnected tools (Zoom, Google Calendar, WhatsApp, PayPal) creating workflow inefficiencies
- No existing technical infrastructure; needed complete platform from scratch with no internal development team
- Timeline pressure: needed to launch before academic year start to capture new student enrollments
- Required multi-currency payment processing, automated billing, and parent communication portal
Our Approach
- Week 1: Requirements gathering, workflow analysis, and architecture design. Selected tech stack (Next.js, PostgreSQL, Stripe, video API integration)
- Week 2-3: Built core user management and authentication, integrated video conferencing API with screen sharing and whiteboard capabilities
- Week 4-5: Developed scheduling system with calendar sync, automated reminders, and payment processing with subscription management
- Week 6-7: Built progress tracking, analytics dashboard, resource library, and real-time messaging. Conducted user acceptance testing with 20 beta users
- Week 8: Performance optimization, security hardening, production deployment, and go-live support
The Results
8 Weeks
From concept to production launch
Launched on schedule, capturing peak enrollment period
9 Core Systems
Fully integrated and production-ready
Replaced 5+ disconnected tools with unified platform
300+
Active users within first month
From 50 students to 300+ in 30 days
I was juggling Zoom links, calendar invites, payment reminders, and progress tracking across five different tools. It was exhausting. FusionLeap didn't just build a website—they understood my workflow and delivered a complete platform that handles everything. We went from concept to serving 300+ students in 8 weeks. The automated scheduling alone saves me 10 hours a week, and parents love the progress tracking. The system just works, and I can finally focus on teaching instead of admin tasks.
— Platform Founder, Educational Services
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