Agent simulations
Why simulated data?
The Age of AI Demands a New Kind of Data
Before investing in AI tools for your enterprise, you need to test them on business data that mirrors your actual operations. Meaningful evaluation requires realistic, interconnected documents: emails referencing meetings, contracts reflecting negotiations, and communications that evolve over time.
the obstacles
Your Corporate Data Creates Impossible Tradeoffs
Using your own data for AI evaluation seems logical, but it introduces critical obstacles.
Privacy & Security Risks
Sensitive corporate data could leak during demos or pilots. Privacy regulations like GDPR and HIPAA often prohibit using real data for purposes beyond its original collection, even for internal testing.
Availability Challenges
Finding the right content in sufficient volume is difficult. You need specific scenarios, edge cases, and enough data to stress-test the AI, but sourcing this internally is time-consuming and often impossible.
Preparation Costs
Anonymization and redaction are resource-intensive processes that still leave residual privacy risks. These methods also distort the data, undermining the accuracy of your evaluation.
No Ground Truth
Without built-in answer keys, you can't objectively measure AI performance. There's no reliable way to verify whether the AI correctly identified key information or hallucinated results.
the industry's problem
Synthetic Data Doesn't Solve the Problem
Traditional synthetic data approaches create their own limitations:
Still Requires Your Data
Most synthetic data tools need seed sets of your corporate data as a starting point, reintroducing all the privacy, compliance, and access challenges you were trying to avoid.
Lacks Narrative Coherence
Synthetic data generators produce statistically similar individual documents, but they don't create interconnected datasets. An email might reference a meeting that doesn't exist, or contracts lack the supporting correspondence that reflects real business processes.
Missing Critical Edge Cases
Random variations don't inject the specific scenarios, complexities, and anomalies you need to future-proof your AI evaluation. Without intentional edge cases, you can't test how tools handle the situations that matter most.
the answer
What is Simulated Data and how is it unique?
Simulated Data
Synthetic Data
How it's generated
Replication of private data
How it fits the customer
Focused on industries such as pharma and legal
Most approporate for the text sector
What context is embedded
Stories relevant to the tools being tested and their end-buyers
Random content: Follows patterns of whatever data is available to be replicated
What formats are generated
Chats, emails, PDFs, contracts, industry-specific doc types
Primarily structured and application-specific data
