Skip to main content
Each artificial intelligence model is trained based on specific data, contexts, and guidelines — which means each one carries its own “bias” in how it interprets and responds to problems. In a professional context, this goes far beyond right or wrong: it is about understanding how different perspectives influence answers and, consequently, decisions.
Why ask a single AI when you can consult the top three?TESS Consensus was developed exactly to solve this challenge.

​
So, what is TESS Consensus?

It is an agentic technology that orchestrates multiple AI models simultaneously, promoting a “conversation” between them. Instead of returning a single isolated answer, it conducts a collaborative process where the models analyze, refine, and validate their own outputs until reaching a more robust result.
Image
This process generates something fundamental: consensus with greater reliability. Unlike a simple comparison between individual answers, the Consensus creates an additional validation layer, where the models:
  • Debate among themselves
  • Adjust inconsistencies
  • Converge on the best possible answer
The result is a delivery with higher quality, more consistency, and better performance.

​
Why “consensus” and not just comparison?

Comparing the answer of two isolated models is not enough to identify bias with statistical confidence. A single output does not necessarily represent the actual behavior of that model. TESS Consensus solves this by:
  1. Running multiple models per group (e.g., models with greater Western or Eastern influence)
  1. Generating an internal consensus among them
  1. Producing a statistically more reliable result
Only then does it make sense to compare different consensuses. That is why we launched at Tess three separate approaches such as:
  1. Consensus Global
  2. Consensus US
  3. Consensus China
Image
Each one represents a collective construction within a specific context — and not just the isolated opinion of a model.

​
Main features

Consensus Global

The top three global models work together to solve complex problems, combining their capabilities and reducing individual failures.

US vs China Consensus

Allows for a structured comparison of different worldviews — Western and Eastern — based on independent consensuses. This ensures a fairer and methodologically correct analysis.
Bias identification: By comparing consensuses, you can observe real divergences in interpretation, revealing how cultural, corporate, and training factors impact the results. **High performance through self-validation: **When multiple models collaborate and mutually validate each other, the quality of the answer tends to surpass traditional benchmarks. The Consensus acts as an “intelligent cross-checking” mechanism.

​
How does this apply in practice?

TESS Consensus was not created for superficial curiosities like “how a Chinese or American model thinks”. It is a strategic tool, aimed at deeper decisions.
Examples of use:
  • HR strategies adapted to different cultures
  • Definition of business personas with greater precision
  • Market analysis with multiple perspectives
  • Decision-making in complex and ambiguous scenarios
In many cases, different models diverge significantly. By promoting dialogue between them, the Consensus reduces noise and increases clarity. Furthermore, in scenarios where one intentionally wants to apply a cultural bias (for example, adapting an operation to the Chinese context), using a specific consensus is more effective than relying on a single model — especially considering that many models share similar training influences.

​
Why is this a game changer?

TESS Consensus transforms the way professionals use AI:
  • Moves from isolated answers to well-founded decisions
  • Introduces transparency in the use of models
  • Allows understanding and exploring different perspectives
  • Raises the level of confidence in the answers
More than a text generation tool, it is a mechanism for quality and performance.
Create, test, and shareThe true potential of TESS Consensus appears when applied to real problems.Explore different consensuses, compare results, and observe how the transparency of the models directly impacts the quality of your decisions.
We built the stage. Now it’s up to you.