How to set up AI analytics: Contextflo vs other approaches

We've helped teams set up AI analytics across different stacks and company sizes. There are a few ways to approach it today: use an LLM directly, build something in-house, adopt a semantic layer tool, use your warehouse's built-in AI, or use a purpose-built platform. Each gets you started, but they differ significantly in what happens after the demo.

We evaluate each option across the criteria that matter most in production: setup effort, access controls, vendor lock-in, audit trail, context gap detection, token analytics, data warehouse optimization recommendations, and ongoing maintenance.

Using an LLM directly

Connect your database to Claude, ChatGPT, or Gemini and start asking questions. This is the fastest way to get started and works well for a single person exploring data. The problems show up when you try to scale it to a team.

Everyone sees everything. Two people asking “what's our revenue?” can get different numbers because there's no shared definition. You're tied to one provider, and if you want to switch, you start over. Nobody knows what questions are being asked, what queries are running, or where the answers are wrong.

Building in-house

Full control, built for your exact needs. No vendor lock-in. But you're committing your data team to building and maintaining an internal product. Context management, access controls, query validation, user monitoring, gap detection: all of it falls on you to build and maintain.

Most teams underestimate the maintenance. The initial build takes a quarter. Keeping it working takes indefinitely. Every new data source, every new team member, every new metric requires your team to update the system. For a 1-2 person data team, this means you're spending more time maintaining the tool than doing actual analysis.

Semantic layer and BI tools

Tools like Cube, dbt Semantic Layer, and AtScale are genuinely good at what they do: standardizing metric definitions and structured reporting. If your team has data engineers who can maintain the YAML and your questions fit neatly into predefined metrics, these tools work well. The challenge is everything outside the predefined layer.

New metrics, new data sources, new team members asking questions nobody anticipated. The layer falls behind and you don't know what's missing until someone gets a wrong answer. Access controls vary by tool. Token analytics and warehouse optimization recommendations don't exist. The context layer is entirely manual to build and maintain.

Warehouse-native AI (Cortex, Genie, Amazon Q)

Snowflake has Cortex Analyst, Databricks has AI/BI Genie, and AWS has Amazon Q for Redshift. These are built into your warehouse and use your existing permissions model.

The upside is everything stays inside your warehouse ecosystem. The downside is everything stays inside your warehouse ecosystem. You're locked to one vendor. Your context, definitions, and saved queries don't carry over if you switch. You need to write and maintain YAML semantic models (Cortex) or configure Genie spaces manually. And your team has to use the warehouse's interface, not the AI tool they already work in.

If your entire company lives in one warehouse and you have data engineers to maintain the semantic model, these work. If you use multiple data sources, want LLM flexibility, or need your non-technical team to ask questions in Claude or ChatGPT, they don't.

Contextflo

Contextflo is built for teams that want AI analytics in production without the overhead of building or maintaining it themselves.

Connect your data source, start with a few tables, and build context from real usage. Business concepts, metrics, and reference queries grow from actual questions your team asks, not a massive upfront project. Access controls are built in from day one: scope tables by user group so marketing sees marketing data and sales sees theirs.

Contextflo is LLM-agnostic. It works with Claude, ChatGPT, or any model your team uses. Switch anytime and your context carries over. Every question, every query, and every step the agent takes is logged.

The system flags where business definitions are missing or where queries are failing, so your data team fixes what matters instead of guessing. Token analytics show where tokens are being spent at each step. And it recommends aggregate tables to reduce query cost and complexity.

Comparison

Contextflo might not be the right fit if your data is strictly on-prem with regulatory requirements that prevent cloud connections (we're working toward an on-prem option). But for teams with cloud-hosted data who want self-serve analytics without the overhead, we're already helping teams run thousands of questions per month without adding headcount. If you're thinking about setting up conversational analytics for your team, we'd love to chat.