Dropped
pgvector latency from 4.2s to 18ms (SaaS)
•
Reduced OpenAI
API costs by 68% (LegalTech)
•
Fixed ReAct
loop dropping 34% of context (FinTech)
•
Scaled
Python MVP to 5k concurrent users (AI Marketing)
•
Eliminated 98%
of RAG hallucinations with hybrid search (HealthTech)
•
Automated
15,000 monthly support tickets using AI agents (E-commerce)
•
Slashed
multi-agent execution time by 80% via parallel processing (Logistics)
•
Migrated undocumented legacy monolith to AI-generated Next.js (PropTech)
•
Cut
token usage
by 50% via prompt compression algorithms (EdTech)
•
Diagnosed and
patched catastrophic memory leaks in node containers (GovTech)
•
Deployed
zero-shot product classification system mapping 2M products (Retail)
•
Rescued stranded MVP by integrating resilient vector database (BioTech)
•
Resolved
concurrent websocket latency for live AI translations (Media)
•
Built
dynamic CI/CD test generation with local LLMs reducing QA queue (DevTools)
•
Dropped
pgvector latency from 4.2s to 18ms (SaaS)
•
Reduced OpenAI
API costs by 68% (LegalTech)
•
Fixed ReAct
loop dropping 34% of context (FinTech)
•
Scaled
Python MVP to 5k concurrent users (AI Marketing)
•
Eliminated 98%
of RAG hallucinations with hybrid search (HealthTech)
•
Automated
15,000 monthly support tickets using AI agents (E-commerce)
•
Slashed
multi-agent execution time by 80% via parallel processing (Logistics)
•
Migrated undocumented legacy monolith to AI-generated Next.js (PropTech)
•
Cut
token usage
by 50% via prompt compression algorithms (EdTech)
•
Diagnosed and
patched catastrophic memory leaks in node containers (GovTech)
•
Deployed
zero-shot product classification system mapping 2M products (Retail)
•
Rescued stranded MVP by integrating resilient vector database (BioTech)
•
Resolved
concurrent websocket latency for live AI translations (Media)
•
Built
dynamic CI/CD test generation with local LLMs reducing QA queue (DevTools)
•
Dropped
pgvector latency from 4.2s to 18ms (SaaS)
•
Reduced OpenAI
API costs by 68% (LegalTech)
•
Fixed ReAct
loop dropping 34% of context (FinTech)
•
Scaled
Python MVP to 5k concurrent users (AI Marketing)
•
Eliminated 98%
of RAG hallucinations with hybrid search (HealthTech)
•
Automated
15,000 monthly support tickets using AI agents (E-commerce)
•
Slashed
multi-agent execution time by 80% via parallel processing (Logistics)
•
Migrated undocumented legacy monolith to AI-generated Next.js (PropTech)
•
Cut
token usage
by 50% via prompt compression algorithms (EdTech)
•
Diagnosed and
patched catastrophic memory leaks in node containers (GovTech)
•
Deployed
zero-shot product classification system mapping 2M products (Retail)
•
Rescued stranded MVP by integrating resilient vector database (BioTech)
•
Resolved
concurrent websocket latency for live AI translations (Media)
•
Built
dynamic CI/CD test generation with local LLMs reducing QA queue (DevTools)
•
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