Data Analytics
SaaS Company Reduces Report Time by 80% with Wren AI
Wren AI Boosts SaaS Data Reporting
Wren AI helped a SaaS company reduce report time by 80%, enabling teams to access data in plain English, improving self-service, and boosting productivity.
A fast-growing SaaS company had a solid data infrastructure but struggled to use it effectively. The marketing and product teams frequently waited days for basic reports, while the analytics team was swamped with ad hoc requests. Dashboards helped, but they weren’t flexible enough for follow-up questions.
To solve this, the company introduced Wren AI, a natural language analytics tool that allows teams to ask questions in plain English and get fast, accurate answers. The result? Reporting turnaround times dropped from days to hours, analyst workloads lightened, and business teams gained direct access to data-driven insights.
SaaS Company Faced Data Bottlenecks
Despite having PostgreSQL and BigQuery set up, the company couldn’t turn data into decisions fast enough. Common questions — like “What’s our churn rate for Q2?” or “Which channels convert the best?” — got buried in Slack threads or support queues. Dashboards were too rigid, and the analytics team burned hours each week answering repetitive queries.
Why the SaaS Team Chose Wren AI
The company explored several self-service BI tools, but most added complexity without solving the core issue. Wren AI stood out because of its semantic layer — which allowed business terms like “active users” or “plan churn” to be defined clearly — and its natural language processing capabilities. This enabled anyone to ask questions in plain English and get fast, accurate responses. Wren AI also integrated seamlessly with their existing PostgreSQL and BigQuery databases, worked within the company’s existing toolset, and supported both open-source and cloud deployment, making it a scalable and cost-effective choice for an SME-focused SaaS company.
Project Details
Wren AI connected to marketing and product databases. Key definitions were set up by the data team. After going live, users could ask questions like “How many paying users did we lose in Asia last month?” and get clear answers — with the SQL behind it — in under a minute. Teams stopped relying on analysts for routine metrics. They started using Wren AI to build slide decks, plan campaigns, and guide sprint discussions.
Aspect | Details |
Service | Web Application AI-Driven Reporting |
Technology | Wren AI, PostgreSQL |
Period | July 2023 to August 2023 |
Budget | Cost-effective solution tailored for SMEs, focused on scalable analytics and improved data access |
AI Reporting Setup for SaaS Business
Wren’s /ask and /generate_summary API endpoints were integrated into the internal dashboard. Slack was used for sharing responses. Role-based access ensured each department only saw relevant data. The semantic layer translated business language into SQL queries, reducing misunderstandings and building trust in the data. Wren AI fit seamlessly into the existing stack without requiring a major overhaul.
Secure Self-Service Analytics in SaaS
The team integrated Wren’s /ask
and /generate_summary
API endpoints directly into their internal dashboard. They used Slack to share answers across teams efficiently. Role-based access ensured each department saw only the data relevant to them. The semantic layer translated casual business language into precise queries, reducing misunderstandings and building trust in the results. Most importantly, the company didn’t need to replace or rebuild any existing tools — Wren fit seamlessly into their current tech stack.
Results: Faster Data for SaaS Teams
The average turnaround time for data requests dropped from 2.6 days to just under 2 hours. More than 70% of marketing’s repeat questions were handled through Wren AI, without analyst involvement. Product managers increased their use of live user behavior data by nearly 40%. This led to better decision-making during roadmap reviews. A satisfaction survey revealed a significant increase in confidence in data access, from 5.1 to 8.3. The analytics team shifted focus to more strategic tasks like forecasting and experimentation.
SaaS AI Rollout Challenges
Some users initially struggled to phrase their queries correctly, leading to incomplete answers. This was addressed with quick training and clear documentation. Concerns around data access and security were managed using Wren’s robust permission controls and audit trails. Though adoption was gradual, usage spread organically once teams experienced the time-saving benefits.
Lessons: Smarter SaaS Data Use
Wren AI solved one key problem: getting accurate answers without writing SQL. It didn’t replace analysts, but made their time more valuable. Teams were more confident in exploring data. The success of the project was due to a clear semantic model, minimal training, and well-defined guardrails. Most importantly, it fostered a cultural shift — moving from a request/wait model to a self-serve mindset.
Next for This SaaS Company
With proven value in product and marketing, the company is now rolling Wren AI out to customer support and finance. The next phase will focus on analysing churn patterns, billing data, and refund trends. Plans are also underway for a limited partner-facing version that provides access to anonymised insights.
Wren AI’s Impact on SaaS Growth
Wren AI wasn’t just another BI tool. It was a shift in how the company used data. Teams now had access in plain language, eliminating delays and reducing reliance on analysts. This change allowed for faster, deeper decision-making. It didn’t require rebuilding their tech stack — it fit seamlessly into their existing workflows.
Ready to unlock the full potential of your data? Get in touch today to see how Wren AI can streamline your reporting and empower your team. We’re here to help you get started!
WRITTEN BY
July 21, 2025, Product Development Team
Top Categories
- Software Development ................... 7
- Digital Transformation ................... 5
- AI in Business ................... 5
- Uncategorized ................... 3
- Product Development & AI ................... 3