3 posts tagged with "lead-gen"

In a B2B lead generation pipeline, every email that arrives costs compute. Scoring it for buyer intent, extracting entities, predicting reply probability, matching it against your ideal customer profile — each module is a DeBERTa forward pass. If 40% of inbound email is template spam, AI-generated slop, or mass-sent campaigns, you are burning 40% of your GPU budget on garbage.

The solution is a gating module: a spam classifier that sits at stage 2 of the pipeline and filters junk before anything else runs. But a binary spam/not-spam classifier is too blunt. You need to know why something is spam (template? AI-generated? role account?), how confident you are (is it ambiguous, or have you never seen this pattern before?), and which provider will block it (Gmail is stricter than Yahoo on link density).

This article documents a hierarchical Bayesian spam gating system with 4 aspect-specific attention probes, information-theoretic AI detection features, uncertainty decomposition, and a full Rust distillation path. The Python model trains on DeBERTa-v3-base. The Rust classifier runs at batch speed with 24 features and zero ML dependencies.

An AI cold email engine does one thing: it reads what you know about a company and writes a personalized outreach email — automatically, at scale. If you've ever spent an afternoon manually tweaking 50 nearly-identical emails, you understand the problem. If you've paid for Instantly, Smartlead, or Apollo, you've already solved it — just not on your own terms.

Those SaaS tools charge $30-200/month, send your prospect list to their servers, and give you a black-box model you can't touch. You can't train it on your best-performing emails. You can't add custom quality gates. You can't run it offline. For engineers and technical founders, that's a bad deal.

This system is the alternative: a locally-run pipeline where you own every layer — model weights, scoring logic, and approval gates. The core is Qwen3-1.7B, fine-tuned with LoRA adapters on MLX (Apple's framework for M1/M2 Metal acceleration). A Rust orchestration layer drives the full batch loop: pulling company records, invoking the model, running quality filters, and surfacing emails for human review before anything sends.

The result is not a toy. On a single M1 MacBook Pro, the pipeline generates 200+ personalized emails per batch in under 10 seconds — no GPU cloud, no API latency, no per-email cost. Fine-tuning converges in under 30 minutes on the same machine.

Leading cloud extraction APIs are orders of magnitude larger than the model that just beat them at structured web extraction. This isn't a marginal win — it's a 3.4 percentage point lead on the de facto standard SWDE benchmark. The secret isn't a novel architecture; it's domain-specific fine-tuning on a 100,000-example dataset of real scraping trajectories. The ScrapeGraphAI team's release of a fine-tuned Qwen3-1.7B model flips the conventional scaling law on its head and delivers a complete open-source stack (model and dataset under Apache 2.0, library under MIT) for production. This is a blueprint for how narrow, expert models will outperform generalist giants — if you have the right data.