Privacy-first web analytics for SaaS teams that refuse to guess

Mission

Removing the trade-off between insight and privacy

Wolf Analytics exists because the analytics industry created a false choice. On one side, platforms that vacuum up every click, scroll, and keystroke, generating compliance liability that scales faster than the insights they deliver. On the other, minimal counters that strip so much context that product teams are left staring at page-view totals with no path to action. Neither model serves teams that need to ship better software while respecting the people who use it. We started Wolf Analytics to prove that rigorous privacy controls and meaningful product intelligence are not mutually exclusive. The architecture was designed from day one around the premise that data minimization and analytical depth can coexist when the system is intentional about what it collects, how it stores that data, and who can access it.

Our mission is to give every SaaS team a single analytics platform that answers real product questions without generating regulatory exposure. We want teams to stop maintaining two parallel stacks, one for compliance and one for insight. Wolf Analytics is built so that a single deployment handles both concerns through explicit configuration rather than afterthought bolt-ons. When privacy is a system-level property instead of a policy document, the entire organization benefits: engineers spend less time on data plumbing, legal teams face fewer ambiguous data flows, and product managers get the behavioral evidence they need to prioritize with confidence.

The problem

Most analytics platforms get the fundamentals wrong

The standard enterprise analytics stack collects too much data by default. Raw IP addresses are logged indefinitely. Full user-agent strings are stored without reduction. Third-party cookies follow visitors across domains. Session recordings capture form inputs and personal identifiers unless engineers manually configure masking rules that vary by page. The result is a data warehouse that grows into a compliance liability, one that legal and security teams discover only when a regulation changes or a breach occurs. Teams that depend on this data are building their product roadmaps on a foundation that could be restricted or deleted at any point by a policy review they did not anticipate.

The counter-movement toward minimal analytics solves the compliance problem by eliminating context. Page-view counters and referrer summaries are safe from a privacy standpoint, but they answer almost none of the questions that drive product decisions. Where do users drop off in a multi-step workflow? Which traffic sources produce sessions that convert versus sessions that bounce? Is a retention improvement driven by a feature change or a seasonal pattern? Minimal tools cannot answer these questions because the data required to answer them was never collected. SaaS teams stuck between these two extremes end up cobbling together multiple tools, each with its own data model, consent flow, and access control surface. The operational cost is high, the data is fragmented, and the privacy posture is only as strong as the weakest integration.

Our approach

Two-mode privacy architecture, one unified platform

Wolf Analytics solves the privacy-versus-insight problem with a dual-mode architecture that makes data collection posture an explicit, per-deployment decision. In GDPR mode, IP addresses are hashed with SHA-256 and a rotating salt before any storage occurs. User-agent strings are reduced to browser and operating system family. Session replay is disabled entirely. Geographic data is limited to country and region, with no city-level coordinates stored or transmitted. This mode is designed for teams operating under the General Data Protection Regulation, ePrivacy requirements, or any organizational policy that demands strict data minimization without sacrificing traffic analysis.

In International mode, the platform enables full behavioral analysis for teams whose legal and consent frameworks permit it. Full IP and user-agent data are stored. Session replay captures mouse movement, click sequences, scroll depth, and masked form inputs. Geographic data includes city-level resolution with latitude and longitude for map-based visualization. Both modes share the same dashboard, the same API surface, and the same multi-tenant access controls. The difference is entirely in what the tracking layer collects and what the backend persists. There are no third-party cookies in either mode. The tracking snippet is a self-contained vanilla TypeScript module served from your own domain. Data isolation is enforced at the tenant level through project-scoped API keys and account-authenticated dashboard access, so one customer's data never leaks into another customer's queries.

Technology

A modern stack built for async throughput and operational clarity

The Wolf Analytics backend is a Python FastAPI application running fully asynchronous request handling. Data is stored in PostgreSQL, accessed through async SQLAlchemy 2.0, which gives us connection pooling, typed query construction, and migration management without sacrificing the concurrency model that high-volume event ingestion requires. The API serves both the tracking ingest path, where project API keys authenticate incoming events, and the dashboard query path, where JWT-authenticated account sessions retrieve aggregated metrics, session lists, funnel analyses, and retention cohorts. Redis backs real-time features including active visitor counts and the Server-Sent Events stream that powers live dashboard updates.

The frontend dashboard is a Next.js application using server-side rendering for fast initial loads and TanStack Query for client-side data synchronization. Visualizations are built with Recharts for time-series and aggregate charts, and Leaflet for geographic map views. The tracking snippet is a lightweight vanilla TypeScript module bundled with esbuild, designed to load asynchronously and operate without framework dependencies on the host page. Geographic enrichment uses MaxMind GeoIP databases evaluated server-side, with Cloudflare geo headers as a fallback for edge deployments. Billing and subscription management are handled through Stripe webhooks, keeping payment state synchronized with account tier limits and usage metering. Every component in this stack was chosen for operational predictability: well-documented, actively maintained, and straightforward to debug in production.

Values

Principles that shape every decision we make

Contact

Get in touch with the Wolf Analytics team

Wolf Analytics LLC is incorporated in New Mexico and operates as a fully remote team. Whether you are evaluating the platform for a new project, planning a migration from an existing analytics provider, or have questions about our privacy architecture, we are available to help. We respond to all inquiries within one business day and can schedule technical walkthroughs for teams that need a deeper look at deployment options, data handling, or integration patterns before committing.