TensorChat vs. Microsoft Copilot: A Head-to-Head Enterprise Search Benchmark

By Tensor Data Dynamics

We've met with several large enterprises demonstrating our technology. Many of these organizations use Copilot, which makes sense—the Microsoft Suite is embedded in the infrastructure of nearly every large enterprise. You would think it's performing well, right?

Not according to several large enterprises we spoke with. They reported challenges with integration and search accuracy.

Tensor Data Dynamics built TensorVault and TensorChat, products that work together to provide an enterprise-grade, guard-railed AI platform built on your data, and your data alone. No data leakage, no superfluous answers, just grounded, accurate results. That's where we separate ourselves from the competition including Microsoft Copilot. Our system is purpose-built using a private governed AI knowledgebase. TensorChat provides precise answers enforced by privileged-based searches, without the hallucinations.

The Test Setup

We created a neutral storage system utilizing an AWS S3 bucket with the additional configuration to provide IAM privileges supported by Microsoft Entra to serve as a third-party data storage repository. The dataset consisted of a couple thousand documents from the Cornell arXiv research paper library (acting as company private data).

Both systems were provided with the AWS bucket credentials and Microsoft Entra privileges so both Microsoft Studio and Tensor Data Dynamics' TensorVault had identical access and controls for the testing.

The Configurations

Microsoft Copilot Configuration

Copilot is structured to support multiple configurations to meet varying enterprise needs—from basic functionality directly within Microsoft 365 applications to custom agent configurations requiring significant development time and costs.

To perform a comparable test, we leveraged Copilot Studio for the configuration. Utilizing this, we were able to create a searchable index where Copilot could then gather data from the document index used in this testing.

For Copilot to obtain the level of accuracy and security features of Tensor Data Dynamics products, a company would have to perform considerable custom development. A company would need to set up a separate environment and assemble a dedicated Microsoft development team working for at least six months to match the real-time data processing speed and accuracy offered by TensorVault.

TensorVault & TensorChat Configuration

TensorVault turns a company's documents, files, and records into a single, permission-aware AI knowledgebase. This knowledgebase is what powers TensorChat search, feeds our agents, and serves as the baseline for our API.

Administrators manage this entirely through a no-code, web-based console. Point TensorVault at data sources such as file shares, cloud drives, and collaboration tools. Define which spaces to include or exclude, and inherit existing identity and permission models with a few clicks instead of writing integration code.

As a result, organizations get a single, consistently governed knowledgebase that can be safely reused across search, agents, and custom apps without having to build separate pipelines or custom integrations for each use case.

The Results

Tensor Data Dynamics' TensorChat (web-based) client was used to perform the queries and Microsoft Copilot served as the retrieval client for Microsoft. The queries chosen focused on data retrieval precision, context, and accuracy. The systems were prompted with the same 40 questions and judged on grounded accuracy, faithfulness, and task completeness.

Overall Outcome

Tensor Data Dynamics outperformed Copilot on the vast majority of queries.

TensorChat Performance

TensorChat provided accurate answers for every reply with direct sources to the original data and often located the exact page the data was sourced. When available, it also showed the correct figure or image supporting the precise topic in the query.

Copilot Performance

For some queries, Copilot provided accurate results. However, there were several instances where Copilot could not find any data at all. The errors seemed to occur where more precise queries were performed. This is obviously an issue when someone is doing research on their own enterprise data.

Another core issue regarding privacy and accuracy occurs when Copilot mixes in external data from the web, even when configured to use only internal data sources. This further undermines the reliability of results.

Why This Matters

TensorChat was built around two core principles of equal importance:

1. Maintaining tight controls over privacy and security
2. Ensuring the ability to retrieve data and construct replies from your local data with high precision

When your enterprise search tool pulls in external web data instead of your internal documents, or fails to find precise answers to specific queries, it defeats the entire purpose of having an enterprise knowledge system. Your teams need to trust that the answers they receive are grounded in your organization's actual data—not hallucinated or mixed with irrelevant external sources.

See the Full Results

All the queries and results are available in our GitHub repository with the 40 prompts and screenshots documenting each response from both systems.

View the Complete Benchmark Results on GitHub →

The Bottom Line

If your organization is struggling with Copilot's search accuracy or integration challenges, you're not alone. The enterprises we've spoken with share the same frustrations. TensorChat and TensorVault offer an alternative—one that's purpose-built for precision, privacy, and enterprise governance from day one.

No six-month development projects. No custom integrations. No web data mixing with your internal knowledge. Just accurate, grounded answers from your data.