Verifiable Systems Engineering

Architectural Teardowns & Technical Case Studies

We believe in complete architectural transparency. Below are technical system teardowns detailing operational challenges, pipeline diagrams, and verified performance metrics.

Logistics & Supply ChainAI-Powered Workflow Automation
Global Freight Forwarder & 3PL Operator

Autonomous Freight Manifest & Customs Processing Pipeline

The Operational Bottleneck:

The client received over 1,400 daily multi-page Bills of Lading, packing slips, and customs declarations in erratic non-standard PDF scans. Over 22 full-time operations coordinators were required solely to manually type shipping details into the internal TMS, causing constant port clearance delays.

Engineered Solution:

Autonova engineered an automated document processing pipeline with vision models, confidence-scored JSON extraction, automated HS-code classification, and webhook push into the client's legacy AS400/EDI dispatch system.

92%Reduction in manual manifest data entry time
<14sAverage document ingestion to TMS synchronization
99.4%Field extraction accuracy on degraded scans
Stack:PythonFastAPIAWS TextractClaude 3.5 SonnetPostgreSQLDockerRabbitMQ
Read Deep Architectural Teardown
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Financial ServicesAI Integration with Existing Systems
Multi-Family Office & Wealth Advisory Firm

Secure Zero-Retention Institutional Knowledge Engine

The Operational Bottleneck:

Senior wealth advisors managed over 15 years of proprietary investment memos, tax strategies, and fiduciary legal opinions across disparate network drives. Junior analysts struggled to find historical guidance, resulting in redundant research and delayed client reporting.

Engineered Solution:

Autonova deployed an on-premise hybrid retrieval-augmented generation (RAG) platform with strict zero-data-retention, encrypted semantic chunking, and role-based document access controls.

1.4sAverage query response time with verified citations
100%Zero data leakage - private VPC isolated deployment
12hrs+Weekly research time saved per advisory partner
Stack:TypeScriptNext.jsLangGraphQdrant Vector DBLlama-3 (Self-Hosted)PostgreSQLTailwind CSS
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RecruitmentAI Agent Development
Boutique Executive Search & Technical Staffing Firm

Multi-Agent Executive Talent Sourcing & Matching Architecture

The Operational Bottleneck:

Executive headhunters spent up to 35 hours per mandate manually combing developer repositories, patent databases, and social networks to identify specialized AI research engineers, leading to slow placement cycles.

Engineered Solution:

Autonova built a multi-agent orchestration swarm that continuously evaluates public technical contributions, scores candidate relevance against complex role criteria, and synthesizes hyper-personalized outreach briefs.

4.2xIncrease in qualified candidate discovery volume
48%Higher candidate response rate to customized outreach
3 wksAverage placement cycle reduction
Stack:PythonLangChainCrewAIOpenAI GPT-4oPineconeFastAPISupabase
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