Challenge
IWConnect’s sales team faced a scaling problem. Hundreds of job postings appear on LinkedIn every day, each a possible opportunity, but reviewing them by hand and writing personalized outreach didn’t scale.
Large language models could read the unstructured posts, yet their raw output wasn’t reliable enough to put in front of prospects. At 2,000 postings a month, LLM inference costs climbed fast.
We needed personalized outreach from day one, trustworthy classifications, and a cost curve that didn’t punish growth.
Solution
We designed a hybrid AI architecture built for speed now and low cost later. Large language models bootstrapped production from day one, extracting industries, technologies, and business needs from each post into PostgreSQL and making them searchable with PGVector to match opportunities to the right case studies.
An AI Jury of multiple LLLM families, scored every classification before it reached a prospect, with Langfuse for observability.
As validated data accumulated, we trained lightweight ML models with MLflow to take over once they matched LLM accuracy.
Business Value
IWConnect launched a fully automated outreach engine without waiting months to collect training data. The AI Jury kept low-confidence classifications away from prospects, protecting our reputation while outreach scaled.
Most important, a recurring AI expense became a proprietary asset: classification costs dropped up to 90% after local ML replaced LLM inference, while accuracy held. Every posting processed now makes the system a little better and cheaper.
Download the full case study to see the architecture decisions and the LLM-to-ML transition in detail.
