Chinese language startup Manus challenges ChatGPT in information visualization: which ought to enterprises use?

The promise sounds virtually too good to be true: drop a messy comma separated values (CSV) file into an AI agent, wait two minutes, and get again a elegant, interactive chart prepared on your subsequent board presentation. 

However that’s precisely what Chinese language startup Manus.im is delivering with its newest information visualization characteristic, launched this month.

Sadly, my preliminary hands-on testing with corrupted datasets reveals a elementary enterprise drawback: spectacular capabilities paired with inadequate transparency about information transformations. Whereas Manus handles messy information higher than ChatGPT, neither device is but prepared for boardroom-ready slides.

The spreadsheet drawback plaguing enterprise analytics

Rossums’ survey of 470 finance leaders discovered 58% nonetheless rely totally on Excel for month-to-month KPIs, regardless of proudly owning BI licenses. One other TechRadar research estimates that total spreadsheet dependence impacts roughly 90% of organizations — making a “last-mile information drawback” between ruled warehouses and hasty CSV exports that land in analysts’ inboxes hours earlier than crucial conferences.

Manus targets this precise hole. Add your CSV, describe what you need in pure language, and the agent robotically cleans the information, selects the suitable Vega-Lite grammar and returns a PNG chart prepared for export—no pivot tables required.

The place Manus beats ChatGPT: 4x slower however extra correct with messy information

I examined each Manus and ChatGPT’s Superior Information Evaluation utilizing three datasets (113k-row ecommerce orders, 200k-row advertising and marketing funnel 10k-row SaaS MRR), first clear, then corrupted with 5% error injection together with nulls, mixed-format dates and duplicates. 

For instance, testing the identical immediate — "Present me a month-by-month income pattern for the previous 12 months and spotlight any uncommon spikes or dips" — throughout clear and corrupted 113k-row e-commerce information revealed some stark variations.

Device	Information High quality	Time	Cleans Nulls	Parses Dates	Handles Duplicates	Feedback
Manus	Clear	1:46	N/A	✓	N/A	Appropriate pattern, customary presentation, however incorrect numbers
Manus	Messy	3:53	✓	✓	✗	Appropriate pattern regardless of inaccurate information
ChatGPT	Clear	0:57	N/A	✓	N/A	Quick, however incorrect visualisation
ChatGPT	Messy	0:59	✗	✗	✗	Incorrect pattern from unclean information

For context: DeepSeek may solely deal with 1% of the file measurement, whereas Claude and Grok took over 5 minutes every however produced interactive charts with out PNG export choices.

Outputs:

Determine 1-2: Chart outputs from the identical income pattern immediate on messy e-commerce information. Manus (backside) produces a coherent pattern regardless of information corruption, whereas ChatGPT (prime) reveals distorted patterns from unclean date formatting.

Manus behaves like a cautious junior analyst — robotically tidying information earlier than charting, efficiently parsing date inconsistencies and dealing with nulls with out specific directions. After I requested the identical income pattern evaluation on corrupted information, Manus took almost 4 minutes however produced a coherent visualization regardless of the information high quality points.

ChatGPT operates like a pace coder — prioritizing quick output over information hygiene. The identical request took simply 59 seconds however produced deceptive visualizations as a result of it didn’t robotically clear formatting inconsistencies.

Nonetheless, each instruments failed by way of “govt readiness.” Neither produced board-ready axis scaling or readable labels with out follow-up prompts. Information labels had been regularly overlapping or too small, bar charts lacked correct gridlines and quantity formatting was inconsistent.

The transparency disaster enterprises can’t ignore

Right here’s the place Manus turns into problematic for enterprise adoption: the agent by no means surfaces cleansing steps it applies. An auditor reviewing the ultimate chart has no technique to affirm whether or not outliers had been dropped, imputed or remodeled.

When a CFO presents quarterly outcomes based mostly on a Manus-generated chart, what occurs when somebody asks, “How did you deal with the duplicate transactions from the Q2 system integration?” The reply is silence.

ChatGPT, Claude and Grok all present their Python code, although transparency by means of code evaluation isn’t scalable for enterprise customers missing programming expertise. What enterprises want is a less complicated audit path, which builds belief.

Warehouse-native AI is racing forward

Whereas Manus focuses on CSV uploads, main platforms are constructing chart technology straight into enterprise information infrastructure:

Google’s Gemini in BigQuery grew to become usually accessible in August 2024, enabling the technology of SQL queries and inline visualizations on stay tables whereas respecting row-level safety.

Microsoft’s Copilot in Cloth reached GA within the Energy BI expertise in Might 2024, creating visuals inside Cloth notebooks whereas working straight with Lakehouse datasets. 

GoodData’s AI Assistant, launched in June 2025, operates inside buyer environments and respects current semantic fashions, permitting customers to ask questions in plain language whereas receiving solutions that align with predefined metrics and enterprise phrases.

These warehouse-native options remove CSV exports totally, protect full information lineage and leverage current safety fashions — benefits file-upload instruments like Manus wrestle to match.

Crucial gaps for enterprise adoption

My testing revealed a number of blockers:

Dwell information connectivity stays absent — Manus helps file uploads solely, with no Snowflake, BigQuery or S3 connectors. Manus.im says connectors are “on the roadmap” however affords no timeline.

Audit path transparency is totally lacking. Enterprise information groups want transformation logs displaying precisely how AI cleaned their information and whether or not its interpretation of the fields are right.

Export flexibility is proscribed to PNG outputs. Whereas ample for fast slide decks, enterprises want customizable, interactive export choices.

The decision: spectacular tech, untimely for enterprise use circumstances 

For SMB executives drowning in ad-hoc CSV evaluation, Manus’s drag-and-drop visualisation appears to be doing the job. 

The autonomous information cleansing handles real-world messiness that might in any other case require handbook preprocessing, reducing turnaround from hours to minutes when you’ve got moderately full information. 

Moreover, it affords a major runtime benefit over Excel or Google Sheets, which require handbook pivots and incur substantial load occasions as a result of native compute energy limitations.

However regulated enterprises with ruled information lakes ought to look ahead to warehouse-native brokers like Gemini or Cloth Copilot, which preserve information inside safety perimeters and preserve full lineage monitoring.

Backside line: Manus proves one-prompt charting works and handles messy information impressively. However for enterprises, the query isn’t whether or not the charts look good — it’s whether or not you may stake your profession on information transformations you may’t audit or confirm. Till AI brokers can plug straight into ruled tables with rigorous audit trails, Excel will proceed to carry its starring position in quarterly shows.