Every release, improvement, and bug fix for PQ Intel. We ship continuously — subscribe to our RSS feed or follow @productquant for real-time updates between changelog entries.
Published 13 core marketing pages for app.productquant.dev including homepage, pricing, features, comparison, security, status, changelog, and documentation pages. All pages built on the unified APP design system with consistent navigation, OG tags, and JSON-LD structured data.
Added cursor-based keyset pagination to the signal history feed. Supports filters by platform (LinkedIn, Reddit, X, Hacker News, Product Hunt), score tier (A/B/C/D), and date range. Intersection Observer triggers the next page load automatically — no pagination buttons required.
Rewrote the overnight GTM pipeline scheduler to reduce agent spin-up overhead. Agents now share a common orchestrator session instead of spawning individual processes. Reduced total pipeline runtime by approximately 40%.
Generate blog posts, social media assets, and lead magnet PDFs directly from your signal feed. Turn market conversations into content with one click. Supports multi-language output with platform-specific formatting.
Improved the composite scoring engine with keyword matching weighted by signal type. Scores are recalculated nightly using the latest signal data. The scoring model now accounts for recency — recent signals carry more weight.
Financial filing analysis for procurement intelligence. Supports automated extraction and analysis of financial reports from publicly traded companies, surfacing procurement signals and budget allocations before RFQs are published.
Pre-send deliverability checks, multi-provider routing, and automatic warmup for self-serve email sending. The pipeline now checks MX records, SPF/DKIM/DMARC configurations, and bounce rates before sending.
Released API v2 with cursor-based pagination, composite filter support, and reduced response payloads. The /signals endpoint now supports filter-by-platform, filter-by-score-range, and sort-by-date or score.
Outreach sequence management, reply classification, pipeline tracking, and team collaboration for B2B outbound teams. PQ Desk connects signal intelligence directly to multi-step outreach sequences.
Fixed a rare deduplication bug where signals from different platforms referencing the same company could create duplicate prospect profiles. Deduplication now checks normalized website URLs and LinkedIn profile URLs in addition to company name.
Monitor abandoned mobile app signals across mobile marketplaces. Detect apps with low ratings, no recent updates, or missing key features as buying signals for mobile development agencies and SaaS companies expanding their mobile presence.
Run separate pipelines for different clients or business units. Each tenant has its own ICP configurations, signal history, enrichment settings, and email templates. Tenant isolation is enforced at the database level.
Optimised ICP scoring queries for faster dashboard load times. Reduced average page load from 4.2 seconds to 1.1 seconds by adding composite indexes on (tenant_id, signal_type, score) and implementing a 5-minute score cache. Dashboard heatmap rendering also switched from synchronous to deferred loading.
Built an automated lead magnet generation pipeline with Jinja2 template engine, A4 PDF export, and dual design system support. Generates branded lead magnets from ICP data — cover pages, stat cards, callout sections, comparison tables, and closing CTAs. Templates available for both dark (EN) and light (RU) design systems.
Added competitive intelligence signal monitoring. When a competitor publishes new content, launches a feature, or changes pricing, PQ Intel flags it as an opportunity signal. Combined with the outreach pipeline, teams get actionable angles for prospect conversations based on competitor moves.
Complete rewrite of the contact enrichment pipeline. New architecture supports parallel enrichment requests, automatic retry with exponential backoff, and per-tenant rate limiting. Enrichment throughput increased from ~50 contacts per hour to ~500 contacts per hour. Added TinyFish API integration for email verification and professional data enrichment.
Generate personalized cold outreach sequences based on signal intelligence. Each email is crafted from prospect-specific signal data — recent LinkedIn activity, funding announcements, hiring changes, or published content. Supports multi-step sequences with A/B testing, timing optimization, and reply detection.
Fixed a deduplication bug where the same company could create separate prospect profiles when discovered through different signal sources (e.g., LinkedIn vs Reddit vs Hacker News). Deduplication now uses a composite key of normalized domain + LinkedIn URL + Crunchbase ID before creating a new prospect record.
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