Benchmarking · Diagnostics · Capability improvement

The quality benchmark for AI-generated charts, slides, and reports.

AI can analyze your data. It can’t present the insights effectively. The charts, slides, and reports are correct but not usable without rework. ChartBoss measures the gap, diagnoses why, and builds the fix.

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The gap

Every AI product generates business reports. None of them are presentation-ready.

Ask any LLM, BI copilot, or AI presentation tool to make a chart or a slide. The data is correct. The chart renders. And the output blindly displays the data, without communicating to an audience — it always requires manual rework before it can be shown to a stakeholder, put in a deck, or used to drive a decision.

The gap persists because there's no way to measure it. Code has HumanEval. Math has GSM8K. For chart and slide quality — there's no benchmark, no evaluation framework, no systematic way to diagnose what's wrong or specify what would fix it.

✕Wrong chart type for the data and the question being asked
✕Titles that restate the axes instead of stating the insight
✕No storytelling — missing reference lines, emphasis, annotations
✕Slides with uniform visual weight — no hierarchy, no rhythm
✕PPTX exports that break layout and flatten to images
✕No audience awareness — same output for a board and an analyst
What we do

We measure the gap. Then we close it.

We built the first evaluation framework for AI-generated business reporting quality. Everything else follows from being able to measure.

Product 1
Benchmarking & diagnostics
We score any AI model or tool's chart and slide output. Per-dimension. With root-cause diagnosis and a specification for what would fix each gap.
  • Cross-model benchmark reports
  • Per-model deep diagnostics
  • Per-platform competitive analysis
  • Ongoing monitoring subscriptions
No fine-tuned model required. The evaluation framework is the product. Revenue starts here.
Product 2
Custom intervention
The diagnosis defines the fix. We build whatever closes the diagnosed gaps — each engagement is custom because each model fails differently.
  • Custom training datasets targeting specific weaknesses
  • Fine-tuned models with chart judgment in the weights
  • Embeddable SDK — sub-20ms chart intelligence, no LLM needed
  • Prompt and skill packages
Activated when a buyer sees their diagnostic and says "fix this." Form follows diagnosis.
The loop

Product 1 and Product 2 connect in a cycle. The diagnostic reveals gaps. The intervention closes them. Monitoring detects new gaps. The cycle repeats.

Evaluate→ Diagnose→ Specify→ Intervene→ Verify→ Monitor→ (repeat)
Product 1 covers Evaluate through Specify. Product 2 covers Intervene through Verify. Monitoring renews both.
Who buys

Every company whose AI generates charts, slides, or reports.

The same capability gap shows up across five categories of product. Different buyers, different deal sizes, same underlying problem.

Category Why they care
LLM products ChatGPT, Claude, and Gemini all have data analysis features that generate charts. Output quality drives feature adoption and reduces compute waste from multi-turn iteration.
AI copilots in office suites Copilot in Excel and PowerPoint, Gemini in Sheets and Slides. "Make a chart" is a core promoted use case. Output quality determines whether enterprises renew the AI add-on.
BI platforms with AI ThoughtSpot, Hex, Sigma, Power BI, Looker. AI-generated charts are a core part of the analytics experience. Quality affects whether business users adopt the AI feature or go back to building charts manually.
AI presentation tools Gamma, Beautiful.ai, Plus AI. Slide and deck quality is the product. 76% of exports need manual fixes. The "70% ceiling."
AI reporting & storytelling Automated reports, data narratives, portfolio bulletins. Charts inside reports. Quality determines whether the report is read or ignored.
How it works

The evaluation framework is the core IP.

We built a multi-level assessment system for AI-generated visual communication. It measures not just whether the chart is correct, but whether it communicates — and diagnoses exactly why when it doesn't.

01
Multi-level evaluation
Three levels of assessment. Element-level checks (is each part clear and appropriate?). Cross-element fit (do the parts work together?). Holistic communication (does a viewer get the intended message in seconds?). Each level catches failures the others miss.
02
Execution risk and interpretation risk
A chart can be technically correct and still mislead. The framework evaluates both: was it built right (execution risk) and will the audience read it right (interpretation risk). Different failures, different fixes.
03
Maker-checker agents
A creation agent works in spec space — choosing chart type, encodings, storytelling elements. A review agent works in pixel space — evaluating the rendered output. Different modalities, different blind spots. They can't rubber-stamp each other.
04
Factorial data generation
Controlled single-axis variation produces the cleanest training signal in the market. One dimension changed, quality measured, root cause isolated. Each cycle captures multiple training signals: targets, preference pairs, reasoning traces.
Get in touch

Building AI that generates charts, slides, or reports?

We benchmark AI-generated business reporting quality, diagnose why it fails, and build the fix. If your product generates visual analytical output, we should talk.

Get in touch