A Thin Language Model for growth. A Gemma 2 9B lens for SEO and paid-platform marketing: audits, Search Console and Google Ads framing, on-page and backlink analysis, RAG-grounded, with deterministic guardrails that refuse black-hat tactics by design.
FR-Blaze sits in front of your SEO and paid-marketing workflow — scoping, auditing, and routing queries before they reach an expensive general-purpose model or API.
FR-Blaze has deterministic guardrails built into its weights. Black-hat SEO tactics — cloaking, keyword stuffing, link schemes, hidden text — are refused at the model level, not filtered after the fact. No prompt can override this.
FR-Blaze covers the reasoning tasks that matter in SEO and paid-platform marketing — grounded in real data, without cloud egress.
Analyse on-page factors, surface technical issues, flag thin content, and prioritise fixes ranked by estimated impact — grounded in your actual site data.
Interpret GSC exports to surface CTR opportunities, impression-rank gaps, and cannibalisation patterns with structured recommendations.
Frame campaign briefs, audit ad copy alignment with landing pages, and flag Quality Score risk factors — all via the lens before the expensive API call.
Classify link profiles by authority and risk, flag toxic patterns, and surface anchor-text distribution anomalies from structured export data.
Pair FR-Blaze with a retrieval layer over your own SEO data, historical reports, or knowledge base — answers stay grounded in your sources, not hallucinated.
Cloaking, keyword stuffing, link schemes, hidden text, doorway pages — refused at the weight level. Deterministic. Not a post-hoc filter.
Distilled from frontier SEO and growth reasoning into a Gemma 2 9B base, running locally with no cloud dependency.
Trained on a proprietary corpus of SEO documentation, Search Console exports, Google Ads policy, backlink datasets, on-page audits, and growth case studies — curated and reviewed in-house by SEO and paid-media specialists.
Built on Gemma 2 9B — a capable open base model from Google DeepMind. The larger parameter count relative to our 1.7B models gives FR-Blaze stronger multi-step reasoning for complex SEO and paid-media analysis tasks.
Frontier models with strong marketing and SEO reasoning serve as teachers. LoRA fine-tuning on Apple Silicon via MLX locks in domain judgment — jurisdiction of intent, keyword classification, bid strategy framing — not just surface vocabulary.
Black-hat refusal is built into the fine-tuning targets, not a runtime filter. The model was trained to refuse these outputs unconditionally — no system prompt modification can override it.
Run FR-Blaze as a fully on-device model for growth queries, or wire it as a lens in front of a frontier model API. In Lens mode it scopes intent, classifies query type, and routes — cutting frontier calls to only what genuinely needs them.
Evaluated on SEO and growth-domain reasoning tasks. FR-Blaze outperforms general-purpose models significantly larger on its target domain.
Download the weights on Hugging Face, run it on-device, or wire it as a lens in front of your existing marketing toolchain.