You built the product. Now build the data foundation that makes it grow.
Software companies generate more data per customer than almost any other industry. LakeStack unifies product, revenue, customer, and operational data into a single governed foundation so your teams can reduce churn, expand revenue, and build AI-driven product experiences on data you can trust.
Your product generates terabytes. Your team still cannot answer the questions that matter.
Product data, revenue data, and customer data exist in separate systems, updated on separate schedules, owned by separate teams. The questions that drive growth live at the intersection of all three.
Usage drops, support tickets stack up, NPS slides. Each signal is visible in a different tool. Nobody sees all three together until the customer has already submitted the cancellation. By then the conversation is defensive, not preventive.
Finance pulls ARR from billing. Sales pulls pipeline from CRM. Product pulls expansion from usage data. Three teams, three numbers, one board meeting. The first twenty minutes are spent agreeing on which number is right.
Every AI initiative in the product roadmap, churn prediction, usage-based recommendations, intelligent onboarding, hits the same wall: the data is not clean enough, not governed enough, and not connected enough to build on reliably.
A product and revenue data foundation built for how software companies grow
Every growth, retention, and AI initiative your team is working on runs better when product, revenue, and customer data share the same governed foundation.
Product usage, support ticket volume, NPS responses, billing history, and contract renewal dates are unified into a continuously updated customer health foundation. CS and account management teams see a live health score for every account, built from actual behavioral signals, not self-reported status. When a high-value account enters the risk zone, the right team knows immediately with context on why.
- Build customer health scores from unified product usage, support, and billing data
- Trigger CS workflows automatically when accounts show early churn signals
- Identify the feature usage patterns that correlate most strongly with retention
Usage trends, seat consumption, feature adoption rates, and contract data are unified so revenue and CS teams can identify accounts approaching tier limits, using high-value features without being on the right plan, or showing expansion-ready behavior patterns. ARR, NRR, and expansion metrics are calculated consistently from a single source, so finance, sales, and the board all work from the same number.
- Identify expansion-ready accounts from product usage signals before the renewal cycle
- Give CS a live view of seat utilization and feature gaps per account
- Produce one consistent ARR and NRR calculation shared by finance, sales, and the board
Event streams, session data, feature adoption metrics, and cohort behavior are unified so product teams can see exactly how different customer segments engage with the product, which activation milestones correlate with long-term retention, and which features are consistently discovered too late in the customer journey. Product decisions stop being based on intuition or loudest-customer bias.
- Map the feature adoption paths that lead to high retention across customer segments
- Identify activation bottlenecks that prevent customers from reaching their first value moment
- Measure the revenue impact of product changes, not just usage metrics
Every AI feature in your roadmap, usage-based recommendations, intelligent onboarding flows, churn prediction models, anomaly detection, requires training data that is clean, current, and governed. LakeStack ensures the data feeding SageMaker models and Bedrock applications is unified from every source, validated at ingestion, and continuously updated. AI initiatives move from prototype to production because the data layer holds.
- Train product recommendation and personalization models on governed, current behavioral data
- Deploy churn prediction models that use complete signals, not just what CRM captures
- Build NLQ features for customers powered by a structured, trusted product data layer
User consent, PII handling, data residency, and deletion workflows are governed at the data layer, not managed as a manual process each time a data request arrives. Privacy-compliant data flows into analytics and AI pipelines automatically. When a customer exercises a data deletion right or a regulator asks for an audit trail, the documentation already exists.
- Enforce GDPR and CCPA deletion workflows automatically at the data foundation layer
- Maintain SOC 2 audit trails for every data access event across product and analytics data
- Apply PII masking and anonymization before behavioral data enters any analytical environment
GDPR, CCPA, and SOC 2 compliance without slowing your product team down
Software companies carry privacy obligations that grow with every user and every new market. LakeStack governs compliance at the data layer so your product team is not blocked by it.
PII is identified and masked at ingestion. User consent signals flow from your consent management platform into the data foundation, so opted-out users are excluded from analytics pipelines automatically. Your product team accesses governed, anonymized behavioral data without having to think about what is and is not permitted.
When a user requests data deletion under GDPR or CCPA, LakeStack executes the deletion workflow across all connected data stores, not just the source system. The event is logged, timestamped, and auditable. Compliance teams handle data subject requests without filing an engineering ticket for every one.
Every data access event, pipeline transformation, and PHI or PII movement is logged with full lineage. When your SOC 2 auditor asks for evidence of data access controls, the documentation is already compiled. No manual assembly, no retrospective reconstruction.
Every source your product and revenue stack depends on, unified
LakeStack connects product telemetry, revenue platforms, customer systems, and operational tools into one governed foundation.
- Segment, Mixpanel, Amplitude
- Custom event streams and Kafka
- Feature flagging (LaunchDarkly)
- Session replay and heatmap tools
- In-app messaging platforms
- Gainsight, Totango, ChurnZero
- Zendesk, Intercom, Freshdesk
- NPS and survey platforms
- Customer communication tools
- Onboarding and activation platforms
- Stripe, Chargebee, Recurly
- Salesforce and HubSpot CRM
- CPQ and contract management
- Financial reporting (NetSuite, Xero)
- Usage-based billing platforms
- AWS CloudWatch and logs
- Datadog and observability tools
- GitHub and CI/CD pipelines
- HRIS and finance systems
- Data warehouse (Snowflake, Redshift, BigQuery)
What unified product and revenue data delivers
Results across retention, revenue efficiency, and product velocity that CEOs, CFOs, and CPOs can put in a board deck.
Proven business impact
The organizations that win in healthcare are and will be data-defined.
Applify, the team behind this AI innovation, built LakeStack as a true AWS-native data foundation that lives entirely inside your AWS account, giving you full sovereignty, governed lakehouse capabilities, and production-ready AI value in weeks, without tool sprawl or external dependencies.
- Supports Agentic AI using Bedrock and SageMaker
- Uses Apache Iceberg open table format
- Enforces Lake Formation fine-grained governance
- Handles schema drift automatically every time
- Provides built-in active metadata and lineage
- Features self-healing real-time pipelines
- Eliminates all third-party tool dependencies
- Enables query flexibility with any engine
- Ensures full data sovereignty and control
- Offers automatic sensitive data classification
The full LakeStack platform, built for software companies
Frequently asked questions
CDPs and product analytics tools are optimized for a specific function: managing customer profiles or visualizing product usage. LakeStack is a data foundation that connects and governs all of your data sources, including the systems those tools produce data from. It does not replace your product analytics tool or CDP. It makes those tools more reliable by ensuring the data feeding them is clean, complete, and governed from a single source.
Yes. LakeStack works with your existing data warehouse. It handles the connectivity, transformation, and governance layer that sits between your source systems and your warehouse or analytical environment. The clean, governed data flows into Snowflake, BigQuery, or Redshift and becomes available to your existing BI tools and data teams.
Product event streams from tools like Segment, Mixpanel, or custom Kafka pipelines can generate millions of events per day. LakeStack uses incremental and streaming ingestion patterns designed for high-volume event data, ensuring that dashboards and AI models receive current signals without the cost of reprocessing the full event history on every refresh.
Yes. One of the most common outcomes of a LakeStack implementation for software companies is a single, governed ARR calculation that all functions use. Transformation logic for ARR, NRR, churn rate, and expansion revenue is defined once, applied consistently, and maintained centrally, so every team draws from the same number.
PII masking and anonymization are applied at ingestion before behavioral data enters any analytical pipeline. User consent signals are incorporated so opted-out users are excluded automatically. Data subject deletion requests trigger automated workflows across all connected data stores. Every access event and deletion is logged and auditable.
LakeStack implementations for software companies typically start with the highest-priority use case, usually customer health scoring or revenue analytics, deliver measurable value quickly, and expand from there. For growth-stage companies, the foundation built for the first use case scales as the product and data stack grows, without requiring a rebuild when data volumes increase or new tools are added.
Your best customers are sending signals. Make sure to hear them.
LakeStack unifies product, revenue, and customer data into a governed foundation so your teams can reduce churn, expand revenue, and build AI-driven product experiences on data that is actually trustworthy.
