Operator doesn't just query your tables. It understands how your business thinks about its data — your definitions, your canonical sources, your logic. Built in from day one, not bolted on later.
"Realistically sped up that analysis by probably 99% if I did it myself in Looker. Our ability to analyze our hypotheses are going to increase dramatically."
David Siegel — UCAN · 3 weeks in
From operators who've used every tool in the stack.
Operator is sooo smart. scary. Like its ability to interpret what I mean, is remarkable.
Realistically sped up that analysis by probably 99% if I did it myself in Looker. Our ability to analyze our hypotheses are going to increase dramatically.
David is a sophisticated operator — not new to data tools. He knew Looker. He switched anyway. The difference is context: Operator understood what he meant, not just what he typed. That's what 8 years of consulting methodology buys you.
Most agents can query. Operator builds. Give it a business question and it handles everything downstream: ingestion, transformation, modeling, visualization. Already knowing what "revenue" means to you. Already knowing which source is canonical.
Through the Orchard onboarding process, Operator learns how your business defines every metric. Not scraped from YAML — built through conversation and validated by your team.
ETL pipelines. SQL models. Dashboards. Operator constructs the full stack — autonomously, grounded in context, so every output reflects how your business actually works.
Business definitions change. Operator monitors for drift, flags inconsistencies, and updates the context layer as you evolve. No stale semantic models. No silent errors.
Orchard Analytics was built in the trenches of DTC brands, fintech startups, and scaling companies. We weren't building BI tools. We were making data make sense for businesses that had to ship.
After building the same infrastructure dozens of times, a pattern became impossible to ignore. The hard part was never the technology. It was capturing how the business thought about its own data. What "revenue" meant. Which source was canonical. Why the two dashboards disagreed.
That institutional knowledge — the context layer — used to live in a consultant's head. We encoded it into Operator. Now it arrives with every Switchboard implementation, built through our onboarding process, and evolves as your business does.
Looker launched in 2012. Tableau in 2003. Omni in 2022. Every major BI tool was designed for a world without LLMs — and now they're all scrambling to bolt context layers onto architectures that were never meant to hold them.
Switchboard was designed for AI from day one. The context layer isn't a feature we added. It's the foundation everything else is built on.
Most platforms connect data first, ask questions second. We get the context right first. That's the entire difference.
We run a hands-on onboarding — the same process we've done for 30+ companies. Every metric definition, every canonical source, every business rule gets encoded into Operator before anything else starts.
Connect Stripe, HubSpot, Shopify — whatever you use. Operator builds pipelines, models, and dashboards that reflect how your business actually works, not how the raw schema looks.
Definitions change. Products launch. Segments split. Operator monitors your data for drift, flags inconsistencies, and keeps the context layer current. It's not a one-time setup.
Snowflake has the warehouse. Omni has a UI. Looker has LookML. None of them have a living context layer built by people who've run it in production for 8 years.
| Capability | Switchboard | Omni | Looker | Basedash | Mozart Data |
|---|---|---|---|---|---|
| Living context layer | ✓ Built & maintained | Manual semantic layer | LookML (manual, brittle) | ✕ None | ✕ None |
| Autonomous AI agent | ✓ Operator | ✕ NL queries only | ✕ Gemini (basic) | ✕ Chat only | ✕ None |
| Built-in data ingestion | ✓ | ✕ | ✕ | ✕ | ✓ 140+ sources |
| Data warehouse included | ✓ | ✕ Direct-query only | ✕ | ✕ | ✓ Snowflake |
| Data modeling layer | ✓ AI-built | ✓ Manual required | ✓ LookML experts needed | ✕ | ✕ Needs dbt |
| No engineer required | ✓ | ✕ Needs analyst | ✕ Needs LookML expert | ✓ | ✕ Needs SQL |
| Starting price | $5,000/mo | ~$2,500/mo | $5,000/mo+ | $200/mo | Free + usage |
Based on public pricing and documentation as of March 2026. Switchboard is the only platform combining a living context layer with autonomous AI and a complete data stack.
If you're paying for Fivetran, Snowflake, dbt, and Looker separately, you have infrastructure but not intelligence. Switchboard consolidates everything and adds Operator on top.
Plus the $150K+ you'd spend hiring an engineer to maintain it — and the context that walks out the door when they leave.
What teams actually ask before signing.
No legacy definitions to untangle. Orchard builds your context layer from scratch — correctly. We've done this for 30+ companies. The onboarding process extracts how your business actually thinks about its data, before a single pipeline runs.
The context layer is built through a hands-on Orchard process — not inferred from metadata. Operator shows its work. You review metric definitions before they go live. After that, Operator monitors for drift and flags conflicts. Your team stays in control of what "correct" means.
Switchboard can connect to your existing Snowflake warehouse. What you gain is the Operator context layer on top — the canonical business definitions that make your dashboards actually answer questions the way your team thinks. Start there, expand from there.
A data engineer costs $120–150K/yr — and they leave, taking context with them. A fragmented stack (Fivetran + Snowflake + dbt + Looker) runs $6K+/mo, with no agent on top. Switchboard is cheaper than the stack, cheaper than the hire, and the context doesn't walk out the door.
We'll walk through how Orchard would build your context layer and what Operator does with it. Show up with a business question you've been trying to answer.
No commitment · White-glove onboarding included · $5K/mo after you're convinced