vt

Agentic Analytics Platform

Empowering non technical teams to explore data and build stakeholder ready reports, without SQL, analysts, or a chat window as the product.

  • AI
  • Spatial UI
  • Product Design
  • B2B SaaS
Breadcrumb dashboard redesign update

Business teams live and die by data. Campaign performance, pipeline health, operational throughput. But most analytics tools weren't built for them. Tableau, Looker, and even ChatGPT style interfaces assume technical fluency, SQL literacy, or the patience to prompt engineer your way to an answer.

Breadcrumb set out to change that: a platform where marketing managers, ops leads, and sales directors could connect their sources, explore findings on a spatial canvas, and publish visual reports. No analyst in the loop, no query language required.

The problem

Across our early customer interviews, the same pattern kept surfacing. Non technical business users had the questions and the domain knowledge. They just couldn't get to the answers fast enough.

  • Report requests to internal analytics teams took 2 to 5 business days, killing momentum on time sensitive decisions
  • Existing BI tools required training most business users never completed, so adoption stalled after initial rollout
  • Chat based AI tools produced answers, but users didn't trust outputs they couldn't inspect or reorganize
  • Static dashboards couldn't adapt when the question changed. Every new angle meant starting over

The core job to be done was clear: let a regional sales manager or marketing coordinator go from "I wonder what's driving churn in Q3" to a polished, shareable report in one sitting, on their own.

Hypothesis & approach

We believed the bottleneck wasn't intelligence. Models were already capable enough. The bottleneck was interface. Conversation UI forces linear thinking; business exploration is messy, iterative, and spatial. People cluster ideas, compare scenarios side by side, and refine narratives as they go.

My design direction: embed AI into the workspace itself, not as a chat sidebar, and treat the canvas as the product. Connect data sources in plain language, generate charts as movable objects, and give users controls (not prompts) to reshape what they see. Transparency features like insight breakdowns and step by step reasoning would build the trust needed for business users to act on AI output.

The product

Breadcrumb home screen
Home starts with intent, not a blank canvas. Users describe what they need in plain language and choose how deep to go (quick answer vs. full report). We designed it this way so non technical users never face an empty BI tool on day one.
Data sources and smart connectors
Smart connectors remove the engineering bottleneck. Business users self serve data connections because the first step of every report shouldn't require filing a ticket with IT.
Connecting an Airtable source
A second connector added
Canvas built from the connected source
A finished marketing impact dashboard
From connecting a source to a finished dashboard, in the same workspace, without an analyst in the loop.
Collaborative spatial canvas with multiple dashboard groups
A spatial canvas mirrors how teams actually think, comparing scenarios side by side instead of scrolling through tabs. Live cursors and grouped dashboards let cross functional teams build shared context without passing static PDFs back and forth.
Salesforce CRM overview on spatial canvas with insight popup
Insights attach to individual widgets, not a separate chat thread. Users iterate on one chart without rebuilding the whole report, because real exploration means tweaking a single view, not starting over.
Generated event analytics report, scrolling through the published view
Published reports look presentation ready out of the box. We optimized for stakeholder consumption: business users need to walk into a meeting with something polished, not a work in progress dashboard.

Key design decisions

AI embedded in UI, not conversation UI

Rather than a chat first experience, AI capabilities live inside buttons, chart options, and widget interactions. Users switch visualization types, regenerate summaries, and explore follow ups without writing prompts, reducing cognitive load for non technical users who don't think in LLM instructions.

Canvas control bar with space rules
Canvas controls stay accessible but out of the way. Space rules and scoped knowledge live in a persistent bar so power users can configure context without cluttering the workspace non technical users need to stay focused.
Chart type selector and insight panel
Chart type switching lives in the panel, not a prompt box. Non technical users think in "show me a bar chart," not LLM instructions, so one click reformats both the visualization and its summary together.

Trust through transparency

Business users told us they wouldn't share AI generated charts with leadership unless they could explain how the number was calculated. Insight breakdowns show columns used, processing steps, and the underlying query, turning a black box into something defensible in a board meeting.

Insight breakdown showing SQL steps and columns used
Insight breakdown exposes the logic behind every number. Pilot users said they wouldn't share AI charts with leadership unless they could defend the calculation, so transparency became a first class feature, not a debug panel.
AI reasoning timeline with details panel
A reasoning timeline shows the path from question to chart. Surfacing steps in plain language builds confidence for users who don't read SQL. They can follow the logic even if they can't write it.

Spatial exploration for non linear thinking

Users cluster related widgets, filter views by goal (e.g. "increase engagement"), and let the canvas encode context through layout, similar to how teams use Miro but oriented toward analytical depth. Proximity and grouping act as implicit prompts, so exploring a new angle doesn't mean starting a new chat thread.

Results

We ran an 8 week pilot with 12 users across marketing, sales ops, and customer success at three mid market companies. Success was measured by self serve report creation, time to output, and reduction in analyst dependency.

  • 73% reduction in time to produce a stakeholder ready report (avg. 2.1 days → 28 minutes)
  • 4.2× increase in self serve report creation among non technical users vs. their prior BI tool
  • 68% of pilot users published their first report without training or analyst support
  • 41% drop in weekly ad hoc data requests to internal analytics teams across pilot orgs
  • 86% rated insight breakdown as "critical" or "very important" for trusting AI output
  • Pilot expanded from 12 to 47 seats within 8 weeks; 3 of 3 orgs converted to paid

Qualitatively, users described the shift as "finally being able to answer my own questions." The most repeated feedback: they trusted outputs because they could see the logic, rearrange findings on the canvas, and share a polished report, not a chat transcript.

Breadcrumb proved that non technical business users will explore data and ship reports on their own when the interface respects how they actually think, and earns their trust along the way.

Final thoughts

This was built before LLM providers offered native visualization and code execution as a built in capability. Once that shifted, a meaningful part of what the product solved became something the underlying model could do on its own, and the need for a dedicated canvas layer went with it. The product was discontinued, but the core insight, that the real unlock is removing friction between a question and an answer, shaped the direction of everything that came next.