PROOF OF WORK & CASE STUDIES

Tested in Production, Measured in Business Outcomes

Explore how we architect, benchmark, and deploy deterministic AI systems that eliminate manual bottlenecks and scale operational capacity.

Production Systems: 100% DeployedAverage ROI: > 3.8x in 90 DaysZero Retainers • Fixed Sprints

Lead Automation & CRM SyncClient: Mid-market residential brokerage (45+ agents)

Automating Real Estate Inbound Lead Qualification & Tour Booking

73% faster qualificationLead Velocity

22 hrs saved/weekAgent Admin Time

The Operational Problem

Incoming leads from Zillow and Meta ads waited an average of 4.2 hours for an agent response, resulting in a 40% lead drop-off before tour booking.

Engineered Architecture

Event-driven webhook ingest via n8n, sub-minute Claude 3.5 intent scoring, Apollo data enrichment, and automatic appointment slot generation synced to Google Calendar.

Production Outcome

Response time collapsed from 4.2 hours to 48 seconds; automated booking rate increased by 31% in first 30 days.

Data Pipeline & Trigger Sequence

1.Zillow / Meta Inbound Webhook

2.n8n Payload Normalization

3.Claude 3.5 Intent Extraction

4.Apollo Profile Enrichment

5.Cal.com Slot Booking & CRM Push

Stack:n8nClaude 3.5 SonnetHubSpot APICal.com WebhookPostgreSQL

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Vision AI & OCR ExtractionClient: Regional freight & cold-chain logistics carrier

Sub-Second Multi-Format Logistics Manifest & Invoice Parser

99.4% field accuracyExtraction Precision

85% cost reductionCost Per Document

The Operational Problem

Operations team manually keyed in data from 400+ mixed-format bills of lading and handwritten fuel receipts daily, creating billing delays and frequent audit discrepancies.

Engineered Architecture

Computer vision pipeline using OpenCV for deskewing/noise reduction, followed by targeted vision LLM parsing mapped to strict Pydantic schemas with code-level arithmetic validation.

Production Outcome

Processing cost per document dropped from $2.40 to $0.06; invoice approval cycle compressed from 5 business days to instant settlement.

Data Pipeline & Trigger Sequence

1.Raw PDF & Image Ingest

2.OpenCV Deskew & Denoise

3.GPT-4o Vision Spatial Slice

4.Pydantic V2 Math Validation

5.PostgreSQL / ERP Ledger Commit

Stack:Python / OpenCVGPT-4o VisionPydantic V2FastAPIPostgreSQL

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Enterprise RAG & Knowledge EngineClient: Boutique corporate law and compliance firm

Hallucination-Proof Legal Document Discovery & Contract Synthesis

0% hallucination rateVerified Claims Benchmark

< 1.4s retrieval p95Search Latency

The Operational Problem

Paralegals spent 15+ hours per case cross-referencing multi-hundred-page compliance filings, discovery transcripts, and regional regulatory statutes.

Engineered Architecture

Hybrid vector search combining dense embeddings with BM25 sparse keyword matching in Qdrant, featuring deterministic citation verification that requires an exact page-and-paragraph anchor link for every claim.

Production Outcome

Attorneys can verify discovery facts with 1-click citation references; research prep time reduced by 65% with zero unverified claims.

Data Pipeline & Trigger Sequence

1.Statute & Discovery PDF Chunks

2.Dense + BM25 Sparse Index

3.Qdrant Reciprocal Rank Fusion

4.Citation Integrity Verifier

5.Private VPC Answer Generation

Stack:Qdrant Vector DBLlamaIndexHybrid BM25BGE EmbeddingsPrivate VPC

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CUSTOM SYSTEMS SPRINT

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