This promise looks almost great to come true: leave a dirty almiram sepretted values (CSV) file in the AI agent, wait two minutes, and prepare a polished, interactive chart for your next board presentation.

But this is exactly the Chinese startup Manus.im It is distributed with its latest data visualization facility launched this month.

Unfortunately, testing on my early hands with corrupt dataset reveals a fundamental venture problem: impressive abilities added with insufficient transparency about data changes. While Manas handles better dirty data than the chat, neither the tool is ready for the boardroom-redi slide.

Plinging the spreadsheet problem enterprise analytics

Rasoms’ Survey of 470 finance leaders found that 58% are still primarily dependent on Excel for monthly KPI, despite being the owner of the BI license. Another Tekardar The study estimates that the overall spreadsheet dependence affects approximately 90% of the organizations-constructing a “final-mile data problem” between the operated warehouses and the hasty CSV exports exports the land to the inboxes of analysts hours before the significant meetings.

Manas targets this accurate difference. Upload your CSV, describe what you want in the natural language, and the agent automatically cleanses the data, selects the appropriate vega-light grammar and returns a PNG chart designed for export-no axis table is required.

Where Manas beat chat: 4x slow but more accurate with messy data

I tested both Manus and Chatgpt’s advanced data analysis using three datasets (113K-ro) E -commerce order200k-row Marketing funnel 10k-rov Mother -in -law MRR), First clean, then corrupted with 5% error injection including taps, mixed-rebellion dates and duplicate.

For example, testing the same prompt — "Show me a month-by-month revenue trend for the past year and highlight any unusual spikes or dips" — across clean and corrupted 113k-row e-commerce data revealed some stark differences.

tool	data quality	Time	Cleans the tap	Parsus date	Duplicate handle	Information
Manus	Clean	1:46	N/A		N/A	Right trend, standard presentation, but incorrect number
Manus	messy	3:53				The right trend despite the wrong data
Puffy	Clean	0:57	N/A		N/A	Fast, but wrong visualization
Puffy	messy	0:59				Wrong trend from impure data

For reference: Deepsec can only handle 1% of the size of the file, while the cloud and grouke took more than 5 minutes, but produced interactive charts without PNG export options.

Output:

Figure 1-2: Chart output from the same revenue trend prompt on dirty e-commerce data. The psyche (below) produces a consistent trend despite the data corruption, while the chat (top) shows a deformed pattern from the impure date format.

Manas behaves as a vigilant junior analyst – To tide the data before charting automatically, successfully parses date discrepancies and handle nulls without clear instructions. When I requested the same revenue trend analysis on corrupt data, Manas took about 4 minutes but produced a consistent scene despite data quality issues.

Chatgpt acts like a speed coder – Priority to rapid output on data hygiene. The same request took just 59 seconds, but produced misleading visualization as it automatically did not clean formatting discrepancies automatically.

However, both equipment failed in terms of “executive readiness”. Neither the board-Reddy Axis Scaling or readable label without a follow-up prompt. The data labels were often overlapping or too small, the bar chart lacked the proper gridline and the number formatting was inconsistent.

Transparency crisis enterprise cannot ignore

Here is the place where the psyche gets problematic to adopt the enterprise: The agent ever applies cleaning steps that applies itAn auditor who reviews the final chart has no way to confirm whether the outlairs were dropped, planted or converted.

When a CFO presents quarterly results based on a psychological chart, what happens when someone asks, “How did you handle duplicate transactions with Q2 system integration?” The answer is silent.

Chatgpt, clouds and groukes all show their python code, although transparency through code reviews does not lack programming experience for business users. Enterprises require a simple audit trail, which creates confidence.

Warehouse-Environment AI is running ahead

While Manas focuses on CSV uploads, major platforms are directly creating chart generations in enterprise data infrastructure:

Google’s Gemini in Bigkwear Generally became available in August 2024, which enables the SQL questions on the live table and the generation of inline visualization on the live table, respecting the row-level security.

Microsoft’s Copilot in Clothes In May 2024, Power BI reached GA in experience, causing views inside the fabric notebook while working directly with the Luxes dataset.

AI assistant to GuddataLaunched in June 2025, the customer works within the environment and respects the existing semantic model, allowing users to receive answers to receive answers to receive answers to ask questions in plain language that align with predefined matrix and business conditions.

These warehouse-root solutions completely eliminate CSV exports, preserve full data descent and take advantage of existing security models-file-upload tools such as manus struggle for matching.

Important interval for enterprise adoption

Many blockers revealed by my test:

Live data connectivity Absence remains – Manas supports the file upload only with snowflake, Bigkwear or S3 connector. Manus.im says that the connectors are “on the roadmap”, but there is no time.

Audit application transparency Is completely missing. Enterprise data teams require transformation log, showing how AI has cleaned its data and whether its field is correct.

Export flexibility PNG is limited to output. Staying enough for quick slide decks, enterprises require adaptable, interactive export options.

Decision: Impressive technology, prematurely for enterprise use cases

To drown in the ad-hoc CSV analysis for SMB officials, Manas’s drag-and-drop visualization is working.

Autonomous data cleaning handles the real -world disturbance, which otherwise requires manual preprosasing, when you have the appropriate data, cut the turnaround for hours to minutes.

Additionally, it provides an important runtime benefit on Excel or Google Sheets, requiring manual pivotes and adequate load time due to local calculation power boundaries.

But regulated enterprises with governed data lakes should wait for warehouse-original agents such as Gemini or Fabric Copilot, who keep data inside the security perimeter and maintain full lineage tracking.

Ground level: The Manas proves a-prompt charting functions and manages dirty data effectively. But for enterprises, the question is not whether the charts look good – whether you can put your career at stake at data changes that you cannot audit or verify. Excel will continue to play its role in quarterly performances until AI agents can plug into the tables directly governed with rigid audit trails.