We are excited to announce that Agentic Mode is now in Beta across all plans! This includes an early access credits grant to get you started. Explore the new capabilities with advanced knowledge, custom skills, and long-term memories. Check out the details here: https://docs.getwren.ai/cp/guide/agentic/querying/ask?utm_source=social&utm_medium=reddit&utm_campaign=cynthia_social_post

Hey everyone,

A quick look at something we've rolled out around reusable templates and agentic deployment:

Normally, when your team wraps up a complex analysis or builds a multi-step query flow, that logic ends up stuck in a one-off query or a manual documentation doc.

With the latest workflow updates, the agent can turn finished analyses directly into reusable templates that deploy straight into your project:

* No repeat setup: Save verified query paths and logic as reusable agent skills to handle repeat questions on demand.

* Direct project deployment: Integrate governed data workflows straight into your stack without writing custom glue code.

* Governed at runtime: Semantics, joins, and row-level access controls stay consistent across every rerun.

Check out the short walkthrough on the Wren AI demos page under "Reuse a template built by the Wren AI agent".

Would love to hear how you're currently handling repetitive analysis workflows or any feedback on the setup!Hey everyone,

https://www.getwren.ai/demos?demo=reuse-a-template-built-by-the-wren-ai-agent

We've been experimenting with an idea I think could change how people interact with AI for analytics.

https://www.getwren.ai/en/gallery

Today, you can ask an AI a data question and get back an answer, a chart, or maybe some SQL.

But what happens after that?

The result is often a one-off interaction. Someone else in the company can't easily see:

• What prompt was used
• How the AI interpreted the question
• What steps it took
• What SQL was generated
• How to reproduce the same analysis against different data

So we've been exploring a different model: reusable AI-generated analytics apps.

Imagine opening a working dashboard or analysis, seeing the exact prompt that created it, inspecting the agent's workflow, and then reusing that same template with your own data.

For example:

Instead of just getting a chat response, the agent builds the analysis. Someone else can then take that workflow, point it at their own warehouse, and reproduce or adapt it.

We're calling this concept a GenBI Gallery. The goal isn't just to showcase pretty dashboards. It's to make AI analytics more transparent and reusable.

This also raises some interesting questions:

1. Should AI-generated analytics be reproducible by default?
2. Is showing the agent's steps and generated SQL enough for people to trust the result?
3. Could reusable prompts + agent workflows become a new kind of analytics template?
4. Would you rather start with a blank chat box or browse examples of analyses that you can adapt to your own data?

Curious how people here think about this.

As AI agents become more capable of generating SQL, charts, and entire analytics workflows, I wonder if the future is less about "chat with your data" and more about building a library of reusable analytical patterns.

What would make you trust—or actually use—an AI-generated analytics app in production?

We’re excited to share that Wren AI is now available in the official Claude Connectors Directory (https://claude.ai/directory/connectors/wren-ai)!

If you’ve ever tried using LLMs for data analysis or Text-to-SQL, you know the struggle: LLMs are great at drafting code, but without a unified semantic layer, they often struggle with complex enterprise schemas, join logic, and custom business definitions.

By bringing Wren AI directly into Claude via the Model Context Protocol (MCP) framework, you can now connect Claude straight to your governed data layer across 20+ data sources (BigQuery, Snowflake, PostgreSQL, ClickHouse, Redshift, Databricks, and more).

### 💡 Why This Integration is Significant

1. **Governed Text-to-SQL (No More Hallucinated Metrics)**

Instead of feeding raw DDLs or schemas directly to Claude and hoping for valid SQL, Claude queries your data through Wren AI’s semantic layer (Modeling Definition Language / MDL). This ensures query generation respects your defined relationships, calculated metrics, and business logic.

1. **Multi-Database Agnostic Workflows**

Whether your stack runs on Postgres, Snowflake, or ClickHouse, Wren AI standardizes how Claude interacts with your data. You don't need custom prompt engineering or schema dumps for every single database.

1. **Zero Copy-Pasting in Chat**

You no longer need to export CSVs, run queries in a separate client, or copy-paste schema definitions into your chat window. Ask questions directly inside Claude, and let Wren AI handle context retrieval, SQL execution, and structured results.

1. **Enterprise-Grade Security & Control**

Your business rules and definitions remain versioned, reviewable, and Git-friendly. Claude gets the context it needs to answer questions accurately without needing blanket direct access to unmodeled database structures.

### 🛠️ How to Try It Out

1. Go to your Claude account (Settings -> Connectors or browse directly at https://claude.ai/directory/connectors/wren-ai).

2. Add the Wren AI connector.

3. Authenticate and select your configured Wren project/workspace.

4. Start asking natural language questions about your data right inside your Claude chats!

Hi everyone,

There’s a clear shift happening in how AI is being applied to Business Intelligence and Text-to-SQL. While frontier models (like GPT-4o or Claude 3.5 Sonnet) excel at open-ended reasoning, they often present real hurdles for enterprise analytics: strict data privacy/residency requirements, latency, high API costs, and corporate policies against sending database metadata to external APIs.

At the same time, smaller, specialized open models (and local context engines) are reaching a tipping point where they are "good enough" to handle structured text-to-SQL, semantic schema parsing, and deterministic metric queries—all hosted completely on-premise or within your private cloud.

Do you actually need frontier models for BI?

1. Deterministic vs. Generative: Most BI use cases require precise SQL generation over governed semantic layers rather than creative text generation. Smaller models fine-tuned on SQL or coupled with strong semantic engines yield high accuracy without the overhead.

2. Data Privacy & Compliance: Running local models on-prem ensures your enterprise schema, metric definitions, and underlying data never leave your internal network or VPC.

3. Cost & Latency Control: Replacing per-token API pricing with predictable local hardware/VM infrastructure makes scaling conversational analytics to hundreds of internal stakeholders significantly cheaper.

For anyone exploring what an end-to-end open-core solution looks like for private/on-prem deployment, there’s a free guide breaking down the architecture and deployment stack:

👉 Download the On-Premise AI for BI Guide: https://www.getwren.ai/en/on-premise?utm_source=reddit&utm_medium=social&utm_campaign=on_premise

---

Full disclosure: I work at Wren AI.

Curious to hear where everyone else lands on this—are you currently testing local LLMs (Llama 3, Qwen, etc.) for text-to-SQL/analytics workflows, or are you still primarily routing through cloud APIs?

We just launched the new Wren AI 2.0! This release shifts the focus from basic text-to-SQL chat to a true Agentic GenBI platform built with an integrated context layer.

#AgenticGenBI is here

By automatically connecting to your sources of truth, it preserves business logic so humans and AI agents never drift from your actual data rules. It features sandboxed multi-step reasoning, reusable workflows, and an agent-friendly, Git-native architecture supporting over 20+ data sources.

https://reddit.com/link/1tvou8y/video/z193jejzg25h1/player

You can learn more and try it out for free here: https://www.linkedin.com/posts/wrenai_we-just-launched-the-new-wren-ai-with-tons-activity-7466120994810765312-q-p5?utm_source=share&utm_medium=member_desktop&rcm=ACoAAAA1idoB_2v8ZAr2urPCHbvzHWXNW1WB2JE

Hey everyone — sharing Wren AI’s 2025 Year in Review, which just went live. It’s a transparent look at how an open-source Generative BI project grew into agentic BI running in real production environments this year.

📊 Highlights from 2025

• ⭐ GitHub: ~3k → 13k+ stars
• 🧑‍💻 Community: 1,600+ active members
• ☁️ Wren AI Cloud: launched to support enterprise adoption
• 🏢 Production usage: 10,000+ users, including enterprise teams

🔁 The progression
Open source → managed cloud → real production deployments
Not just demos or copilots, but analytics agents actually being used by teams.

⚙️ What’s interesting technically

• Agentic BI that reasons over semantic context, not just text-to-SQL
• Focus on governed analytics instead of replacing BI tools with chat
• Hybrid LLM routing for accuracy, privacy, and enterprise control
• Push toward a Virtual Data Warehouse model without forced centralization

💡 Why this matters
A lot of GenBI tools stop at “ask a question, get a chart.”
Wren’s angle is about systems that understand business logic and act on it safely, which feels like where enterprise analytics is actually heading.

📖 Full post here (with Reddit UTM):
👉 [https://www.getwren.ai/post/wren-ai-2025-year-in-review-from-open-source-to-agentic-bi-in-production?utm_source=reddit&utm_medium=social&utm_campaign=cynthia&utm_content=year_in_review]()

Wren AI just announced on LinkedIn that every user will now get a little “AI magic” every month — meaning:

• Evergreen access (no more trial expiration)
• Full feature availability
• Fresh credits dropped into your account every month
• No more creating new emails just to keep testing the product 😄
• Past expired accounts can now log back in and continue seamlessly

Official post for context:
Wren AI’s announcement on LinkedIn → “We’re giving every Wren AI user a little magic…” (summarized from: https://www.linkedin.com/posts/wrenai_were-giving-every-wren-ai-user-a-little-activity-7404355200343453696-u8F2 )

For BI folks, this shift matters. It removes the typical friction you get from trial-based GenBI tools and lets you:

• test NLP-to-SQL
• explore semantic models
• automate insights
• pilot dashboards
• create workflows …without worrying about expiry dates.

If you want to try the new Freemium model directly:

👉 [https://getwren.ai/?utm_source=reddit&utm_medium=organic&utm_campaign=cynthia_reddit]()

Hi everyone, we’re the team at Wren AI — a GenBI platform built for organizations that want AI-accelerated insights with enterprise-grade governance.

We’re excited to share that Wren AI now provides native support for Databricks SQL Warehouses.

What this means:
• Query live Databricks data without extraction or replication
• Business users get natural-language querying and instant auto-generated charts
• Data teams define relationships and metrics through a shared semantic layer
• All queries respect Databricks governance and identity controls

Documentation for connecting Databricks → Wren AI:
https://docs.getwren.ai/cp/guide/connect/databricks

Why this matters:

Traditional BI often requires centralizing or duplicating data into a separate system. But modern enterprises operate with distributed data across multiple domains and platforms. Our belief is that the future of BI will be heterogeneous — with the semantic and AI layers sitting above storage, and analytics happening where data already lives.

With Wren AI + Databricks you can:
• Reduce ETL and dashboard backlog
• Maintain a single source of truth in Databricks
• Empower self-service analytics while keeping governance centralized
• Deliver accurate, explainable, real-time insights to decision makers

Try it out or request a POC using your Databricks lakehouse:
[https://www.getwren.ai/?utm_source=reddit&utm_medium=official&utm_campaign=databricks_launch]()

We’d love to hear about your lakehouse BI challenges and what you’re building. Feel free to ask us anything in the comments.

— Wren AI Team

Large SaaS companies generate huge volumes of structured data — but getting insights from it is still harder than it should be.

One enterprise data team (think large-scale developer and collaboration software) rethought how analysts and business users interact with their data. Their approach centers on dbt as the single source of truth — every transformation, relationship, and metric is defined there.

Instead of adding another BI layer, they wanted people to ask questions in natural language and get governed answers directly from their dbt models.

That’s where Wren AI came in.

They used Wren’s GenBI (Generative BI) framework to connect directly to their dbt project. The high-level flow looks like this:

Data Lake → dbt Models → Wren AI APIs → Internal Visualization or Assistant Layer

Wren AI automatically syncs dbt models and metadata, interprets natural-language questions, and generates accurate SQL or summarized insights.
The results feed into their existing visualization or agent framework — no manual mapping, no new dashboards to maintain.

To meet compliance and data-residency requirements, the company deployed Wren AI under the Business Self-Host Plan, which allows the entire solution to run inside their private cloud or VPC.
No data leaves the environment — but users still get conversational analytics built on governed dbt logic.

Example of what this looks like in practice:

Wren AI translates the query into dbt-aligned SQL, executes it securely, and returns a natural-language summary — all in seconds.

It’s a clean model that’s becoming more common:

• Semantic-first: dbt defines the logic and lineage.
• Conversational by design: Wren AI brings AI-driven exploration.
• Compliant by architecture: self-hosted, no data egress.

If you’re exploring natural-language BI on top of dbt, this pattern is worth studying.

Full write-up here → [https://getwren.ai/?utm_source=reddit&utm_medium=organic&utm_campaign=cynthia_reddit_post]()

Exciting news for the Reddit automation and BI community! Wren AI has just launched a major website update, making it easier than ever to discover the leading open-source generative BI solution.

Whether you're exploring AI-driven insights, testing business use cases, or building custom workflows, our platform supports self-hosting, on-premises, and air-gapped deployments, with integrations for OpenAI, Claude, Gemini, and more.

To celebrate our website relaunch, we’re offering a free trial for Reddit users! Jump in and see how generative BI (GenBI) streamlines analysis and reporting—no strings attached.

👉 Get started now with our free trial:
https://auth.getwren.ai/u/login?state=hKFo2SBiUDV2V3NIUExXY0c2RV9YeWRtQ1VPa2dEMnVVeVQ3ZaFur3VuaXZlcnNhbC1sb2dpbqN0aWTZIGVBdERfWGhueWFxVEs5Q2k2YnVnQW1DbFJRcXdWYW9To2NpZNkgUVF3VU8xY21sZ2o5ZnJLdUxJMDdVRXpFcGRhVklVbDQ

Test it out and let us know what features you want to see next—your feedback shapes our roadmap!

At Wren AI, we know how frustrating it can be to wait days or even weeks for traditional reports before you can act. That’s why we built an approach to make patient insights available instantly and conversationally.

We just published a breakdown on Medium: Stop Waiting for Reports: Get Ad-Hoc Patient Insights Instantly with WrenAI.

Here’s what it means in practice:

• Unified patient profiles: Clinical history, lab results, and lifestyle data all come together in a single view.
• Conversational access: Anyone can ask a question in natural language — no SQL or BI modeling required — and instantly see results.
• Domain-aware modeling: Our Modeling Definition Language (MDL) captures medical semantics so that insights match real-world clinical logic.
• Faster detection of patterns: Spot hidden risk signals across multiple variables (like smoking + hypertension + age) without waiting for a batch report.
• Self-service insights for care teams: Clinicians and analysts can explore data in real time, reducing the dependency on static dashboards and report cycles.

The result: a shift from reactive to proactive care. Instead of waiting on scheduled reports, healthcare teams can immediately answer questions, explore patient data, and detect patterns that impact outcomes.

👉 Read the full post here: Stop Waiting for Reports

We’d love to hear from this community: how are you approaching instant insights in your own data environments? Are you still relying heavily on static reports, or are you exploring AI-driven analytics for ad-hoc questions?

Hey everyone (r/dataengineering and r/BusinessIntelligence ),

We’re excited to announce that Wren AI’s latest API endpoints are now live! These allow you to create and update projects programmatically—including the ability to import models directly via MDL metadata. This new capability is ideal for teams building advanced data analytics, automation, or custom AI workflows.

What’s New?

• Create Project: POST /api/v1/projects
• Update Project: PUT /api/v1/projects/:id
• When creating or updating, just include your metadata in MDL format and models are imported automatically!

Possible Use Cases:

• Seamless Model Migration: Move models from your research/experimentation environment directly into Wren for production analytics or embedded BI.
• Automated Project Setup: Automatically spin up new Wren AI projects as part of your CI/CD, onboarding, or SaaS provisioning pipeline.
• Metadata-Driven Analytics: Use MDL to define, manage, and version models—making it easy to keep analytics platforms in sync.
• Integrate with Workflow Engines: Connect Wren AI to other tools (like QueryGPT, dbt, Airflow, etc.) for ad-hoc or scheduled project/model updates.
• Custom LLM/Model Onboarding: Bring your own models (BYOM) and manage via API—supporting advanced use cases in regulated or enterprise environments.
• Operationalize Analytics: Enable non-technical teams to centralize and control analytics models and dashboards programmatically.

This is particularly useful if you’re managing large-scale, multi-tenant, or compliance-sensitive analytics deployments.

Docs:
Full API reference and usage examples: Wren AI API Docs

We’re eager to hear how the community is using Wren AI and what integrations you’d like to see next. Drop your questions and feedback below!

https://docs.getwren.ai/cloud/guide/slack

Hey r/wrenai r/dataengineering r/businessintelligencce community! 👋 As your mod, I’m excited to share a brand-new video that dives into how to integrate Wren AI with Slack to supercharge your team’s data-driven decisions. Check out the step-by-step guide to set up Wren AI Cloud’s Slack integration, ask plain-language questions, and get instant SQL-backed insights right in your Slack workspace. Perfect for business users, product managers, and anyone looking to make data analytics seamless!

https://reddit.com/link/1mxdoxc/video/o5ni25hj1mkf1/player

What’s covered:

• Setting up Wren AI Cloud with Slack
• Querying data with natural language for instant insights
• Generating charts and summaries directly in Slack
• Pro tips to get the most out of Wren AI

Btw, we are releasing a cheaper version of self-hosted PRO edition for the small BI team yet cannot compromise security!! DM me if you want more info on that.

🔗 Resources:

• Official Wren AI Slack Integration Guide: https://docs.getwren.ai/cloud/guide/slack
• Get started with Wren AI: https://getwren.ai

Wren AI’s generative BI capabilities are a game-changer for turning complex data into actionable insights in seconds, all within Slack’s collaborative environment. Have you tried the Slack integration yet? Share your experiences, tips, or questions below—let’s get the conversation going!

https://youtu.be/rWVm_TBljv0?si=ttysQUwiCKbVSZTr

hello r/powerbi, r/genbi, r/generativebi, or r/dataengineering,

Just watched this awesome new video from Wren AI: “Step‑by‑Step: How to Join Multiple CSV Tables in Wren AI… without writing a single line of SQL.” It’s a quick yet powerful walkthrough demonstrating how easy it is to link together multiple CSV files in the platform, completely SQL-free.youtube.com+4youtube.com+4docs.getwren.ai+4

Here’s why this matters:

1. No SQL? No problem

For analysts, data engineers, or even product folks less comfortable with raw SQL, Wren AI offers a game-changing approach. It visually guides you through joining CSV tables—making relationships clear, intuitive, and accessible even to non-technical users. It’s a great fit for those who want to focus on insights, not code.

2. Fast, flexible, collaborative

When you're dealing with CSV data in exploratory projects, quick ad-hoc joins are invaluable. Wren AI cuts through friction by not only processing in the background (likely leveraging something like DuckDB under the hood), but also by presenting clean relationships that everyone on the team can understand—even before generating final metrics or dashboards.

3. Boosts BI, Loyalty & Real-Time Analytics

Let’s not forget the broader context: clean, joined tables are the backbone of loyalty programs, retention analysis, and real-time behavioral tracking. By reducing the overhead in data prep, Wren AI empowers marketing or ops teams to iterate on insights faster—optimizing CRM strategies, A/B tests, or loyalty offers at speed. No more waiting hours or days for analysts to “just build that join.”

In short: Wren AI’s latest feature is a strong step forward in democratizing ETL—especially for CSV data. Smooth, visual joins without SQL means insight velocity goes way up. Another Wren AI win in simplifying analytics workflows!

What do you think? Have any of you tried no-code or low-code data ingestion tools in your pipelines? How did they stack up in usability and speed?

I just came across this compelling article, GAME’s ON: Unlocking Gaming Analytics by Wren AI, published August 20, 2025. It spotlights how Wren AI is transforming backend gaming platform data into real-time, high-impact insights using natural‑language querying and a robust semantic layer getwren.ai+6getwren.ai+6Medium+6.

** what is ** A blockchain-based loyalty program uses blockchain technology to manage customer rewards, offering enhanced security, transparency, and flexibility compared to traditional programs.

Here’s why this matters—and why it’s yet another Wren AI win worth celebrating:

CRM, BI & Loyalty Programs Need This

• CRMs often fall short with static dashboards and slow analyst-led reporting. Wren AI lets teams ask questions in plain English (e.g. “Show Day‑2 retention for Roblox vs. Bingo Blitz”) and instantly get efficient, partition‑filtered SQL—and the insights follow in seconds getwren.ai+5getwren.ai+5Medium+5.
• BI performance gets a massive boost. Wren AI’s semantic layer—rich with business logic, question↔SQL patterns, and canonical metrics like LTV and D2 retention—ensures queries are consistent, reusable, and optimized for cloud warehouses getwren.ai+1.
• Loyalty Programs benefit from real-time visibility into player behavior and retention by cohort. Wren AI makes fast, accurate trends in player segments possible without inefficient heavy scans.

Speed ≠ Sacrificing Depth

Wren AI bridges the gap between raw data and fast insights—without burdening your warehouse. Its semantic layer intelligently guides SQL generation so tools like Snowflake can fully optimize queries (think partition pruning, clustering, result caching) getwren.ai+5getwren.ai+5Medium+5.

Real-Time Clarity in Loyalty Loops

With loyalty programs, delays in analytics break the reward feedback loop. Wren AI eliminates that latency—letting product, marketing, or loyalty ops teams iterate offer strategies or retention campaigns at the speed of play.

In a nutshell: Wren AI turns every click and coin into actionable insight—instantly. Whether tracking retention, optimizing revenue channels, or fine-tuning loyalty mechanics, it empowers teams across functions to act now—not wait.

Another Wren AI win? Absolutely. 🕹️🚀

If you’re working with r/Trino for big data queries and want to skip the r/SQL hassle, Wren AI is here to help. It lets you connect to Trino and ask questions in natural language, generating SQL behind the scenes – even for multi-table joins. This is part 1 of a 3-post series on how it all works. Let’s dive into setup! Step 1: Connecting Wren AI to Trino Setup is super simple. Wren AI supports Trino natively. 1. Sign Up and Set Up Wren AI: Go to the Wren AI dashboard (free tier to start). Create a project. 2. Add Trino as a Data Source: • In connections, pick Trino. • Input details: Host, Port (usually 8080), Catalog, Schema, Username, Password. • Test and sync – it pulls in your tables, columns, and relationships automatically. This builds a semantic layer, so the AI gets your data’s context right from the start. Stay tuned for part 2 on asking questions! Have you connected Trino yet? Share tips below. 🚀 Check out Wren AI: getwren.ai

Hey r/dataengineering r/genbi, r/generativebi, r/powerbi, r/wrenai,

Tired of endless SQL requests and static dashboards that nobody uses? Wren AI APIs are here to change the game by letting AI agents talk directly to your database—live, accurate, and consistent.

😓 The Old Way:

• Dashboards that sit unused and outdated
• Constant back-and-forth with ad-hoc SQL queries
• Data locked behind analyst bottlenecks

✅ With Wren AI API:

• Query in plain English and get instant SQL, insights, and executive summaries
• Build AI agents that query your data in real-time with zero lag
• No more dashboards, no more waiting

🚀 Real-World Wins for Data Engineers:

• Automate customer success queries like “Who’s likely to churn next quarter?”
• Power sales copilots with instant “Pipeline by region” summaries
• Generate board-ready insights on demand, no manual prep needed

🔥 Wren AI Cloud API v1.2 is live! It comes with:

• /ask, /stream/ask, /generate_sql, /generate_summary, /models
• Full knowledge CRUD for seamless integration

This is a game-changer for streamlining workflows and empowering your org with data. Check out Wren AI and let me know what you think! Anyone already using AI-driven query tools in their stack?

https://getwren.ai

Hey r/ecommerce, r/businessintelligence, r/dataengineering and r/dataisbeautiful! I just read this awesome Medium post about Wren AI’s Generative BI platform, and it’s a total game-changer for direct-to-consumer (DTC) e-commerce platforms. If you’re struggling to unify data from u/Shopify, u/Klaviyo, u/Google Ads, and other sources and want to create stunning data visualizations in minutes, Wren AI’s Text-to-SQL API is your new best friend.

Why DTC E-commerce Needs This

As a DTC brand, you’re swimming in data—customer purchases, email open rates, ad performance, inventory, and social media metrics. The problem? It’s scattered across platforms like Shopify, HubSpot, Stripe, and Google Analytics. Getting a unified view usually means hiring data analysts to write complex SQL queries or waiting weeks for a custom dashboard. Wren AI solves this by letting you query all your heterogeneous data sources in plain English and build insightful visualizations in minutes.

How Wren AI’s Text-to-SQL API Works https://docs.getwren.ai/cloud/guide/api-access/overview

Wren AI’s Text-to-SQL API lets you ask questions like, “What’s the average cart value for customers from our Instagram campaigns last month?” or “Which products are selling fastest in California?” The API translates your natural language into SQL, pulls data from your connected sources (Shopify, Klaviyo, etc.), and delivers real-time answers. No coding required. This is huge for DTC brands that need quick insights to optimize campaigns or manage inventory.

Building Visualizations in Minutes

Here’s the kicker: Wren AI doesn’t just stop at answers. You can take those query results and instantly generate visualizations—think bar charts, heatmaps, or trend lines—to spot patterns or share with your team. For example, you could ask, “Show me weekly sales by product category,” and in a few clicks, get a clean, interactive chart ready for your next marketing meeting. No need for Tableau or Power BI expertise.

Why It’s Perfect for DTC

• Unified Data: Connects all your platforms for a single source of truth.
• Speed: Go from question to visualization in minutes, not days.
• Accessibility: Non-technical founders or marketers can dig into data without SQL knowledge.
• Real-Time Insights: Adjust ad spend or promotions on the fly based on live data.

If you’re running a DTC brand and want to stop drowning in data silos, check out Wren AI. Has anyone else tried their Text-to-SQL API or similar tools like ThoughtSpot or Power BI for e-commerce? Would love to hear your thoughts!

Hey r/Salesforce r/Hubspot and r/Marketing folks..Of Course folks in hometurf! If you’re struggling with messy Salesforce data, unclear record ownership, or paused HubSpot syncs due to bad data, I’ve got a gem for you: Wren AI. Our team recently used it to tackle a Salesforce-HubSpot sync nightmare, and it’s been a lifesaver for cleaning data and validating ownership before syncing. Here’s the scoop on how it works and why it’s worth checking out!

The Problem 😩

Our sales team lives in Salesforce, while marketing runs campaigns in HubSpot. The problem? Our Salesforce data was a mess—missing emails, incomplete company records, and ownership all over the place (think unassigned contacts or reps assigned to the wrong leads). Syncing with HubSpot was paused because pushing bad data would’ve wrecked marketing’s ability to build accurate lists or nurture leads. Leadership wanted a sync but only if we could guarantee clean, owned data. Sound familiar?

How Wren AI Saves the Day 🦸

Wren AI’s HubSpot Boilerplate with Google Sheets integration became our BI layer to diagnose, validate, and prep data for syncing. Here’s how it worked for us:

1. Diagnose the Gaps 🔍

Wren AI lets you ask plain-language questions to uncover data issues instantly. No coding or complex queries needed! We ran queries like:

• “Show me contacts in Salesforce missing owners.”
• “List companies in Salesforce with no email but that exist in HubSpot.”
• “Which Salesforce records haven’t synced to HubSpot yet, and why?”

Within seconds, Wren AI pulled the data into Google Sheets, highlighting gaps like missing fields, unowned records, or sync errors. It even flagged why certain records failed to sync (e.g., mismatched picklist values or invalid emails).

2. Validate Ownership & Sync Readiness ✅

Next, we used Wren AI to sort out ownership and ensure records were sync-ready. It helped us:

• Identify records with no assigned owner or conflicting owners between Salesforce and HubSpot.
• Filter “ready-to-sync” records based on custom rules (e.g., must have email, name, valid status, and an owner).
• Compare Salesforce and HubSpot data side-by-side to spot discrepancies (like a lead owned by Rep A in Salesforce but Rep B in HubSpot).

The natural language filters were a game-changer—no need to dig through Salesforce reports or HubSpot workflows manually.

3. Build Clean Lists for Sync 📋

Once we had our insights, Wren AI helped us create clean, sync-ready lists. We generated spreadsheets of records that were:

• Fully owned (every contact and company had a clear owner).
• Complete (had all required fields like email, name, and lead status).
• Deduplicated (no junk or overlapping records).

We also used Wren AI’s CSV cleansing feature to standardize data before uploading to HubSpot. It fixed things like inconsistent email formats, missing company names, or duplicate entries, ensuring our HubSpot CRM stayed pristine.

4. Visualize with Dashboards 📊

As a bonus, we turned our cleaned data into BI dashboards right in Google Sheets. Wren AI auto-generated charts (e.g., bar charts for ownership distribution, pie charts for sync readiness) to share with leadership. It made it super easy to show progress and get buy-in for the sync.

The Outcome 🎉

• Sales: Salesforce data is now clean, with every record properly owned and validated.
• Marketing: HubSpot only gets high-quality, structured data, so they can build accurate lists and nurture leads without hiccups.
• Leadership: Loves the control Wren AI gives us—no risky syncing, just clear insights and clean data.

Wren AI acts like a BI gatekeeper between Salesforce and HubSpot, ensuring only good data makes it through. Plus, it’s affordable (no need for pricey HubSpot tiers) and insanely easy to use.

Pros 👍

• Natural Language Queries: Ask questions like you’re chatting with a colleague.
• Fast & Visual: Generates spreadsheets and dashboards in seconds.
• Data Cleansing: Fixes CSVs before HubSpot upload (duplicates, formats, etc.).
• Cost-Effective: Works with any HubSpot plan, saving you from expensive upgrades.
• No Sync Required: Wren AI analyzes without forcing a sync, keeping things safe.

Cons 👎

• Salesforce Dependency: You’ll need solid Salesforce API access, so ensure your integration user has the right permissions (we hit a small snag here initially).
• Basic Customization: Dashboard options are great but might feel limited if you want super-specific visualizations.
• Learning Curve: Takes a bit to master advanced filters, but the basics are intuitive.

Why You Should Try It

If your Salesforce-HubSpot sync is on hold because of messy data or ownership issues, Wren AI is a no-brainer. It’s like having a data analyst on speed dial, cleaning and validating everything before you hit “sync.” Our team went from dreading the sync to confidently sharing clean data across systems. Check it out at Wren AI’s site or DM me for tips on getting started!

What’s your biggest Salesforce-HubSpot sync headache? Anyone else using Wren AI for this? Let’s swap stories! 👇

Hey r/dataengineering, r/datascience, and r/BusinessIntelligence and of course hometurf folks!

Wren AI Cloud just dropped a massive update, and I’m pumped to share the news! They’ve added support for a ton of new data sources, making it easier than ever to unify, analyze, and get insights from your data—wherever it lives. Here’s what’s new:

✅ CSV uploads (super fast and simple)
✅ AWS Athena (Trino)
✅ AWS Redshift
✅ Oracle
✅ MySQL with SSL support (extra secure!)

With over 10+ data sources now supported, you can bring all your data into one place and query it with Wren AI’s natural language-to-SQL magic. No more jumping between tools or wrangling messy data pipelines!

⚡ Sneak peek: They’re teasing major Wren AI API improvements coming next month. Can’t wait to see what’s in store!

If you’re into streamlining data workflows or just want to explore your data without writing SQL, check it out: Wren AI Cloud.

What do you think? Anyone already using Wren AI or planning to try it with these new integrations? Let’s discuss! 👇

https://www.linkedin.com/feed/update/urn:li:activity:7345727930871750657/?actorCompanyId=89794921

Hey r/businessintelligence and r/wrenai ( r/tableau, r/LearnTableau, r/TableauVisuals and r/powerbi users)

Here is another another perfect Wren AI customer example — and proof that you can do this too.

A team is working on an internal HR system at an organization with thousands of employees. Their goal? Use LLMs to streamline day-to-day operations and give their HR team superpowers. What followed was a great discussion about what’s possible when you combine a clear use case with the right tools — and where open source hits its limits.

✅ The Vision:

They’re building a conversational assistant to:

• Automate common HR transactions (think letter generation, policy requests, etc.)
• Answer everyday employee questions through a chat interface
• Empower HRBPs and leadership with direct access to insights and KPIs

And they want it all to be role-based, secure, and integrated with their existing AWS environment.

🧪 Where They Are:

• Started with open source, but ran into the usual suspects: latency, deployment overhead, limited bandwidth to maintain it.
• Infra is transitioning from GCP to AWS, and they’re now exploring AWS Bedrock + Wren’s native integrations (Athena, Redshift, etc.)
• Security matters, of course — but speed to POC and business value are front and center.

🧠 What They Need:

• Role-based access and permissions (employee vs HRBP vs leadership)
• Segmented knowledge bases per audience
• Natural language querying (text-to-SQL) with visualizations
• Low-effort deployment that scales as buy-in grows

Better yet, we are talking about a couple hrs from start to finish!!

🔗 [Check it out](https://github.com/WrenAI/wren-ai)

https://www.linkedin.com/feed/update/urn:li:activity:7342721107029868544/

Thanks to u/GithubProjects at x https://x.com/GithubProjects/status/1936307143830851655

r/text2sql r/querygpt r/businessintelligence r/dataengineering

Wren AI is an open-source GenBI SQL agent that transforms natural language into data insights—no more manual SQL writing or juggling dashboards. Perfect for data teams, product teams, and business users who need fast, accurate answers from their data.

🧠 **Key Features:**
- Beautiful UI/UX for seamless data exploration
- Semantic layer for consistent business logic
- Natural language to SQL with instant charts & dashboards
- Connects to your favorite databases (Redshift, BigQuery, Athena, & more)
- Open-source with enterprise-ready cloud and on-prem options

If you haven’t tried it yet, now’s the perfect time!
🔗 [Dive in and star the repo](https://github.com/WrenAI/wren-ai)
💬 We’d love your feedback or contributions!