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CONCEPT PROJECTS

CASE STUDIES.

Verify our system building capabilities. These cases illustrate concrete technology implementations and business metrics. Marked as illustrative validations.

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AI / Voice / AutomationILLUSTRATIVE CASE STUDY

AI Receptionist for a Service Business

Challenge: A growing multi-location service firm was losing up to 22% of inbound appointment enquiries because front-desk staff were occupied assisting in-person clients or answering basic pricing questions, resulting in long call queues and missed revenue.

Solution: We engineered a production-grade AI Voice Receptionist integrated directly into their local telephony routing and CRM scheduler, capable of holding complex natural-language phone calls, confirming availability, answering service FAQs, and booking calendar openings.

Voice AI (VAPI)GPT-4oTwilio TelephonyFastAPI / PythonNode-RED Workflow Engine
Operational Outcome

"Streamlined front-office overhead and captured after-hours bookings, translating customer interest into booked appointments without adding staff headcount."

+35%
Booking Capture Rate
78%
Repetitive Calls Diverted
0 sec
Average Queue Time
BUILD MY AI RECEPTIONIST
Generative AI / RAGILLUSTRATIVE CASE STUDY

AI Assistant for Business Documents

Challenge: Operations analysts and legal clerks spent an average of 9 hours per week searching through scattered PDFs, internal manuals, compliance briefs, and historical project files to answer customer enquiries and complete technical audits.

Solution: We built a secure, high-performance Document Intelligence platform using Retrieval-Augmented Generation (RAG). The platform indexes company knowledge bases and enables secure natural-language chat queries with accurate source citations.

Python / LangChainPostgreSQL / pgvectorLlamaIndexNext.js / TypeScriptOpenAI Embeddings
Operational Outcome

"Turned static document files into an interactive, conversational company brain, accelerating internal audit times and reducing customer onboarding cycles."

-85%
Search Resolution Time
14,000+
Document Queries / Month
99.4%
Accuracy Rating
BUILD MY AI DOCUMENT ASSISTANT
Machine Learning / E-commerceILLUSTRATIVE CASE STUDY

Personalized Product Recommendations

Challenge: A high-traffic e-commerce retailer had a catalog of 20,000+ items but was relying on manual product groupings, leading to low click-through rates on cross-sales and under-optimized average order value (AOV).

Solution: We trained and deployed a custom hybrid Recommendation System using collaborative filtering, content features, and browse histories. The system serves custom API recommendations to the front-end in under 40ms.

Scikit-learnPyTorchRedis CacheFastAPI API LayerGoogle Cloud Platform
Operational Outcome

"Created a personalized shopping experience designed to improve product discovery and conversion rates across all collection paths."

+18.4%
Average Order Value (AOV)
x2.4
Click-Through Rate (CTR)
28ms
Recommendation API Latency
BUILD MY ML RECOMMENDATION ENGINE
SEO / GrowthILLUSTRATIVE CASE STUDY

Building an Organic Growth Engine

Challenge: A high-ticket B2B service platform had high acquisition costs via paid channels but lacked search engine visibility, driving a need for a sustainable organic distribution pipeline.

Solution: We engineered a database-driven Programmatic SEO framework, generating 3,500+ structured, fast-loading service landing pages targeting long-tail intent search phrases, backed by topic clusters and technical indexing setups.

Next.js App RouterTailwind CSSJSON-LD SchemaPostgreSQLVercel Edge Network
Operational Outcome

"Created a scalable organic acquisition system designed to generate qualified inbound enquiries without ongoing ad spend."

220k+
Monthly Organic Traffic
-62%
Cost Per Acquisition (CPA)
3,400+
Pages Indexed by Google
BUILD MY SEO GROWTH ENGINE
AI / Sales / AutomationILLUSTRATIVE CASE STUDY

From Lead to Conversation Automatically

Challenge: A business consultancy received hundreds of inbound lead enquiries weekly across ads, socials, and web forms, but inconsistent follow-up delays caused a drop in lead-to-booking conversions.

Solution: We built a cognitive Sales Automation workflow that screens incoming messages, scores buy-intent, drafts contextual responses in minutes, updates the company CRM, and schedules discovery calls automatically.

Make.comOpenAI GPT-4HubSpot CRM APICal.com APINode.js
Operational Outcome

"Reduced manual lead qualification workloads, ensuring every qualified lead receives an instant, intelligent response and booking pathway."

<2 min
Lead Response Time
+44%
Meeting Booking Rate
-90%
Manual Screening Work
BUILD MY SALES AUTOMATION ENGINE
Data / Analytics / MLILLUSTRATIVE CASE STUDY

From Spreadsheets to Intelligence

Challenge: A manufacturing firm had operational metrics split across Excel sheets, legacy databases, ERP systems, and cloud tracking tools, preventing management from viewing cohesive profitability data.

Solution: We constructed an ELT (Extract, Load, Transform) data pipeline feeding into an analytical data warehouse, complete with responsive Next.js dashboards showing historical KPIs and predictive demand trends.

PostgreSQL WarehouseFastAPINext.js / Chart.jsPython / PandasDocker Containerization
Operational Outcome

"Unified company data into a secure decision control board, providing predictive analytics to prevent stock bottlenecks and material waste."

Instant
Time-to-Report Generation
14%
Inventory Waste Saved
<1 hour
Data Latency
BUILD MY BUSINESS DASHBOARD