# BooleanMaths vs Flame Analytics

BooleanMaths records 30,692 monthly visits with -9.5% month-over-month growth and Free Trial, Contact for Pricing pricing. Flame Analytics records 22,598 monthly visits with -5.6% growth and Contact for Pricing pricing.

| Metric | [BooleanMaths](https://openaitools.app/tool/booleanmaths-pulse) | [Flame Analytics](https://openaitools.app/tool/flameanalytics-com) |
| :--- | :--- | :--- |
| Website URL | https://booleanmaths.com | https://flameanalytics.com |
| Categories | AI Marketing, AI For Data Analytics, AI Advertising | AI For Data Analytics, AI Marketing, AI Data Mining, AI Lead Generation |
| Pricing Model | Free Trial, Contact for Pricing | Contact for Pricing |
| Monthly Visits | 30,692 | 22,598 |
| MoM Growth Rate | -9.5% | -5.6% |
| Primary Market | Global | Spain |
| Direct Visits | 24,592 | 5,946 |
| Review Score | 4.1 | 4 |
| Saved Count | 0 | 0 |
| Supported Platforms | Website | Website |

## BooleanMaths

BooleanMaths positions itself as an "AI marketing engine" whose value is trustworthy data first. Rather than another dashboard that reads whatever the pixel captured, it emphasises server-side event collection: enriched purchase events are sent directly to Meta and Google Ads conversion APIs so ad platforms receive cleaner signals and attribution is less dependent on browser cookies.

The platform layers several analytics products on top of that data layer. Multi-channel attribution untangles overlapping journeys across Meta, Google, and email. User journey enrichment recovers sessions lost to third-party checkouts, cash-on-delivery orders, return-to-origin shipments, and cross-browser paths. A live P&L brings contribution margin, COGS, shipping, and ad spend into one view, and post-purchase surveys capture stated customer intent to validate the modelled attribution.

More recently BooleanMaths has leaned into agentic workflows. Its MCP Labs exposes the marketing data layer through a Model Context Protocol server, so teams can run prompts in Claude, ChatGPT, or Gemini to decompose ROAS, diagnose creatives, or plan seasonal demand. A newer BooleanVision feature tracks creative assets, extracts hooks and messaging, and recommends combinations. Pricing is quoted after a demo, and the company publishes benchmark case studies across dozens of D2C brands.

**Pricing & Plans:** Pricing is not published transparently on the main site; plans are quoted after a demo or sales conversation, with a free trial and promotional offers such as a discount on the first three months. A free walkthrough/demo is the usual entry point.

- **Server-side CAPI tracking:** Sends enriched purchase events to Meta and Google Ads conversion APIs to improve match quality and reduce dependence on browser-side pixels.
- **Multi-channel attribution:** Resolves overlapping journeys across Meta, Google, email, and other channels with multi-touch modelling for first-touch and channel comparisons.
- **Live P&L tracking:** Puts contribution margin, COGS, shipping, RTO, and ad spend in one live view so scaling decisions are made on profit, not just ROAS.
- **Journey enrichment and surveys:** Recovers orders lost to third-party checkouts, COD, RTO, and cross-browser journeys, and adds post-purchase surveys to capture customer intent.
- **MCP Labs for AI agents:** Exposes the data layer via an MCP server so teams can run prebuilt or custom prompts in Claude, ChatGPT, or Gemini for ROAS decomposition and creative diagnostics.

**Strengths:** Server-side CAPI and journey enrichment directly target common D2C attribution blind spots; Combines attribution, P&L, creative intelligence, and surveys in one data layer; MCP integration lets existing AI assistants query first-party marketing data; Publishes benchmark case studies with concrete ROAS and visibility deltas

**Limitations:** No transparent public pricing; you must book a demo to get numbers; Attribution quality still depends on correct event implementation and product-level data hygiene; Heavier setup than a simple pixel installer for stores with complex checkout flows

[Profile (Markdown)](https://openaitools.app/tool/booleanmaths-pulse.md)

## Flame Analytics

Flame Analytics targets anyone who runs a physical venue and needs to know what actually happens inside it. Retailers, shopping centres, supermarkets, hotels, banks, transport hubs and public venues connect their existing camera estate to the platform, and Flame's proprietary AI models turn that footage into real-time metrics. The core product is organised around three pillars: Traffic, which measures inside and outside footfall, monitors occupancy live and computes genuine conversion; Customer Journey, which tracks routes, dwell times and zone interactions to expose in-store behaviour; and Connect, which uses guest WiFi captive portals to capture, segment and market to visitors based on location, profile and behaviour.

Privacy is the defining design constraint rather than an afterthought. Flame's video technology is explicitly built without biometrics, so it does not identify individuals or store facial data, and the platform is positioned as GDPR-compliant by design. The company also emphasises hardware efficiency and reuse of existing cameras, which keeps deployment cost and effort down compared with rip-and-replace sensor rollouts. The reporting layer is aimed at non-technical operators as much as analysts: drag-and-drop dashboards, pre-built templates, dynamic reports, automated scheduling and more than 200 KPIs tailored to different venue types and use cases.

By design the platform is integration-friendly. It connects with leading CCTV and people-counting systems and plugs into cloud platforms, data lakes and enterprise analytics environments, so measurement can flow into the wider data ecosystem instead of living in isolation. Typical use cases span people counting, conversion analytics cross-checked with POS, heatmaps and dwell analysis, occupancy management with capacity alerts, queue and checkout-abandonment analytics, restroom cleaning schedules based on real usage, DOOH audience measurement for retail media sales, and guest or corporate WiFi access. The company is headquartered in Spain, publishes case studies and a partner programme, and sells mainly through a personalised demonstration with the customer's own data.

**Pricing & Plans:** Flame Analytics does not publish public list pricing. The site routes buyers to a "Request a demo" flow and a personalised 20-minute session using the customer's own data and use case, so pricing is contact-for-pricing and typically depends on venue count, modules selected and camera infrastructure.

- **Traffic and conversion measurement:** Measure indoor and outdoor footfall, monitor occupancy in real time and calculate true conversion by cross-checking visitor data against POS transactions.
- **Customer journey and behaviour analysis:** Follow routes through the space, analyse dwell time and zone interactions, and read heatmaps and visit patterns to optimise each touchpoint.
- **Connect with guest WiFi:** Turn guest WiFi into capture, segmentation and marketing activation, launching personalised campaigns based on location, profile and behaviour.
- **Privacy-first, non-biometric AI:** Video analytics built without biometrics and positioned as GDPR-compliant by design, reusing existing CCTV hardware rather than requiring new sensor deployments.
- **200+ KPIs and automated reporting:** Drag-and-drop dashboards, pre-built templates, dynamic reports, scheduled delivery and more than 200 KPIs tailored to retail, malls, hospitality and transit.

**Strengths:** Works on existing CCTV and people-counting hardware, limiting new capex and installation effort; Non-biometric approach makes privacy and GDPR review notably easier for legal teams; More than 200 KPIs with drag-and-drop dashboards aimed at non-technical venue managers; Integrations with cloud platforms, data lakes and enterprise analytics avoid a data silo

**Limitations:** No transparent public pricing; budgeting requires a sales conversation and a tailored quote; Accuracy of insights depends on camera placement and quality of the existing estate; Full value typically requires multiple sites and POS or WiFi integration to be in place

[Profile (Markdown)](https://openaitools.app/tool/flameanalytics-com.md)

## Frequently Asked Questions

### How do BooleanMaths and Flame Analytics compare in monthly traffic?

BooleanMaths has 30,692 monthly visits, while Flame Analytics has 22,598. Traffic estimates describe audience size, not product quality.

### How do their growth rates compare?

BooleanMaths has a month-over-month growth rate of -9.5%; Flame Analytics has -5.6%.

### How does pricing compare?

BooleanMaths: Free Trial, Contact for Pricing. Pricing is not published transparently on the main site; plans are quoted after a demo or sales conversation, with a free trial and promotional offers such as a discount on the first three months. A free walkthrough/demo is the usual entry point. Flame Analytics: Contact for Pricing. Flame Analytics does not publish public list pricing. The site routes buyers to a "Request a demo" flow and a personalised 20-minute session using the customer's own data and use case, so pricing is contact-for-pricing and typically depends on venue count, modules selected and camera infrastructure.

## Alternatives

| # | Tool Name | Monthly Visits | MoM Growth Rate | Pricing Model | Summary | Profile (Markdown) |
| :--- | :--- | :--- | :--- | :--- | :--- | :--- |
| 1 | [Triple Whale](https://openaitools.app/tool/triple-whale) | 1,594,461 | +1.1% | Freemium, Paid | Know what's actually working in your ecommerce business — and act on it instantly. | [.md](https://openaitools.app/tool/triple-whale.md) |
| 2 | [HypeAuditor](https://openaitools.app/tool/hypeauditor-com) | 917,615 | +0.5% | Contact for Pricing | AI influencer marketing platform to find, vet, and run creator campaigns | [.md](https://openaitools.app/tool/hypeauditor-com.md) |
| 3 | [Peec AI](https://openaitools.app/tool/peec-ai) | 262,088 | +0.8% | Free Trial, Paid, Contact for Pricing | AI search visibility analytics for marketing teams | [.md](https://openaitools.app/tool/peec-ai.md) |
| 4 | [Simple Analytics](https://openaitools.app/tool/simpleanalytics-com) | 106,690 | -9.3% | Freemium, Paid | Cookie-free web analytics you can run without consent banners or personal data | [.md](https://openaitools.app/tool/simpleanalytics-com.md) |
| 5 | [viral.app](https://openaitools.app/tool/viral-app) | 89,176 | +14.3% | Free Trial, Paid | The operating system for UGC marketing and short-form analytics | [.md](https://openaitools.app/tool/viral-app.md) |
| 6 | [Scale Insights™](https://openaitools.app/tool/scaleinsights-com) | 84,404 | -33.4% | Free Trial, Paid | Automate Amazon PPC with rules you fully control | [.md](https://openaitools.app/tool/scaleinsights-com.md) |
| 7 | [Polar Analytics](https://openaitools.app/tool/polaranalytics-com) | 44,209 | -23.6% | Free Trial, Paid | The AI data platform that gives ecommerce teams and their agents one trusted source of truth | [.md](https://openaitools.app/tool/polaranalytics-com.md) |
| 8 | [Shortimize](https://openaitools.app/tool/shortimize) | 34,262 | -7.8% | Free Trial, Paid | Track and analyze short-form video performance across every major platform | [.md](https://openaitools.app/tool/shortimize.md) |
| 9 | [Lebesgue](https://openaitools.app/tool/lebesgue) | 23,737 | -24.3% | Freemium, Free Trial | An AI CMO that explains your e-commerce marketing data and tells you what to do next | [.md](https://openaitools.app/tool/lebesgue.md) |
| 10 | [WorkMagic](https://openaitools.app/tool/workmagic-io) | 14,786 | -40.2% | Contact for Pricing | Triangulated measurement: incrementality, MMM and causal attribution | [.md](https://openaitools.app/tool/workmagic-io.md) |

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Source: https://openaitools.app/compare/booleanmaths-pulse-vs-flameanalytics-com

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