SaaS & Technology
We help SaaS and technology companies turn scattered product, customer, and billing data into faster decisions, higher retention, and a roadmap that ships on time.
OVERVIEW
Building software is faster than ever. Building the data behind it isn't.
In a SaaS or technology business, every product release creates more data, and more expectations that the team will put it to work. Meanwhile, the data work that supports the product moves at a different speed from engineering, and the gap only widens as the business scales.
We help SaaS and technology companies close that gap. By connecting product, billing, and customer data into a usable foundation, we enable clearer visibility into performance, earlier churn and expansion signals, and in‑product analytics and AI features that actually ship. We work as an extension of your engineering and data teams, moving at their pace and integrating into the systems and workflows you already use.
WHAT WE HEAR FROM SAAS & TECHNOLOGY LEADERS
Data piling up faster than it gets used
0.1
Every action, transaction, and interaction inside your product generates data, but it accumulates across databases, billing systems, and GTM tools faster than the team can turn it into decisions. Leaders spend time reconciling metrics instead of acting on a single, trusted view.
Product and revenue don’t connect
0.2
Teams can see what customers do in the product and what they pay, but not how one drives the other. Without a clear link between feature usage, conversion, and expansion, pricing, packaging, and roadmap bets are made on instinct. It's hard to say which work actually moves revenue.
Churn surprises
0.3
Retention and renewal risk shows up late because signals are scattered across usage events, tickets, billing history, and account notes. By the time a churn risk is obvious, it’s harder to recover the relationship or save the revenue.
Data bottlenecks holding back the roadmap
0.4
New product and AI features sit in the backlog waiting on data foundations that aren’t ready: event tracking, schemas, integrations, and governance. Roadmap priorities are pushed out, and engineering time gets diverted into one‑off data plumbing instead of shipping what customers see.
COMMON SOLUTIONS
What we typically build for SaaS and technology clients.
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Operational & Financial Visibility
Most SaaS and technology companies have more than enough data, but assembling a reliable picture of how the business is actually performing still takes manual work and one‑off pulls. Different teams track different versions of ARR, churn, and product usage, and leadership spends time reconciling spreadsheets instead of making decisions.
We replace that with a unified view across revenue, customer, product, and operational metrics, refreshed daily and built on consistent definitions. Finance, sales, customer success, and product teams work from the same numbers, so conversations shift from “What’s the right metric?” to “What do we do about it?”
✓ARR, MRR, expansion, contraction, and churn metrics
✓Sales pipeline, conversion, and customer success performance
✓Product usage and engagement metrics
✓Drill-down from executive summary to individual customer or feature
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Churn Prediction
Churn surprises happen because the signals are spread across product usage, support tickets, billing history, and account interactions, and no one has time to look at all of them at once. We build churn prediction models that bring those signals together and score accounts on their risk and expansion potential. Customer success, sales, and account teams get a prioritized list of where to focus, directly in the tools they already use. Renewal risk is surfaced early enough to act, and expansion opportunities can be captured before competitors do.
✓Predictive churn scoring at the account and user level
✓Risk indicators surfaced inside existing customer success workflows
✓Expansion opportunity scoring alongside churn risk
✓Continuous model improvement as new signals emerge
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In-Product Analytics
Customers increasingly expect high‑quality dashboards, reports, and analytics inside the products they buy, but building and maintaining them pulls scarce engineering time away from the core roadmap. We design and build in‑product analytics that fit seamlessly into your existing UX and data model, scaling as your customer base and feature set grow.
Your product team gets a partner who can own the analytics surface, from design through production, without slowing the rest of the roadmap or introducing a separate, hard‑to‑maintain stack.
✓Customer-facing dashboards built into the product
✓Self-serve reporting and exports for end users
✓Scalable analytics infrastructure that grows with the product
✓Tight integration with existing product engineering workflows
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Software Built on the Data Foundation
When custom tools are built on their own islands of data, every new feature or integration adds complexity. Data is copied into separate stores, behavior diverges, and the engineering team ends up maintaining a patchwork of one‑off systems.
We build differently. Every application we deliver sits on top of your centralized data platform, so internal tools and customer‑facing features can read from and write to the same source of truth. The result is fewer integration headaches, faster iteration, and a software stack that can support future analytics and AI work without another round of re‑platforming.
✓Internal applications integrated with the data platform
✓Role-based access and governance built in
✓Tight integration with billing, CRM, and core systems
✓Foundation that supports future analytics and AI work
Q&A
Things we hear from SaaS and technology leaders.
The questions every CEO, CTO, and head of data asks us in their first meeting.
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Yes, and this is some of the highest‑leverage work we do for SaaS and technology clients. We help product and engineering teams build customer‑facing analytics, dashboards, and AI capabilities from design through production deployment. Because the work sits on top of a clean data foundation, these features ship faster than they otherwise would and scale with the product as new use cases emerge.
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Yes. We set up your data so that what customers do in your product shows up in your dashboards and systems as it happens, not the next day. Your teams can spot issues sooner, act on usage while it matters, and build features that respond to customers in the moment. We'll also help you decide where real time is worth it and where it isn't, so you only pay for speed you'll use.
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We start from your product and go‑to‑market strategy, not from a specific model or vendor. Together we identify the highest‑impact use cases, whether that’s in‑product features, internal copilots, or better decision support, and map them to the data you already have. From there, we design solutions that can be explained, monitored, and evolved by your team, so AI becomes a durable capability rather than a one‑off experiment.
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Off‑the‑shelf tools come with built‑in assumptions about how a SaaS business should operate, and those assumptions rarely match the reality of any one product or business model. SaaS and technology companies run on unique pricing models, product structures, customer segments, and engineering workflows that generic software struggles to accommodate without significant workarounds. We build solutions that fit how your business actually works, using your real data, your real metrics, and the language your team already uses.
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No. Whatever data stack you’ve already invested in stays in place. We design our work to extend what you already have rather than replace it, pulling data from existing warehouses, integrating with the tools your engineering and data teams already use, and adding capability without duplicating infrastructure. System changes only come into play when there’s a clear business case for them.
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We design every engagement to ship something useful early. Most of our clients see their first solution in their hands within four to eight weeks, with more following close behind. We don’t spend six months building in the background before anyone sees value. Software companies move fast, and we work on the same cadence.
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You do. The data, the code, and everything we build sit inside your own environment and remain yours throughout the engagement. That’s a deliberate contrast to black‑box solutions that take your data off‑site and return answers with no insight into how they got there. Every model, pipeline, and dashboard we build is transparent and fully documented, and if we ever part ways, everything stays with you with no lock‑in.
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The solutions we build are designed to run with limited ongoing support. If you want to iterate on them, improve them, or build new solutions over time, we offer two paths: we can help you build the internal capability to do that work yourselves, or we can partner with you on an ongoing basis as a fractional extension of your team.
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Software engineering teams set the pace at most SaaS and technology companies, and we design our engagements to fit how those teams already work. That means following existing development practices, deployment patterns, and code review standards, working inside the same repos where it makes sense, and adopting the tooling the team already uses. We also work closely with product managers to align on requirements and roadmaps. The goal is to extend your capacity rather than create a parallel system the team later has to absorb.
Curious what your product and customer data could do for your business?
Set up a 30-minute conversation with one of our partners to talk through your business, where your data sits today, and where it could be working harder for you.