Turn Expertise Into Endpoints.
Build Better AI Agents.
You're building world-class data analysis agents. Truesight lets you capture what expert data scientists do and deploy it as eval endpoints.
Expert Data Analysis is Highly Contextual
You're encoding expertise into your agents. But expert judgment varies by context:
- The right analysis depends on the dataset characteristics
- The right analysis depends on the question you're answering
- “Good” analysis adapts to the analytical goal
Prompts alone won't solve this.
Prompts are static instructions. They don't adapt to new data shapes, shifting business contexts, or the nuanced trade-offs that expert data analysts make instinctively. You need a way to capture this expertise, measure it, and improve your agents systematically.
Truesight's answer: Contextual evals deployed as API endpoints.
One Quality Dimension, Many Right Answers
Let's examine chart selection. Even expert data visualization practitioners disagree on the “right” choice for every dataset and question because context matters.
Novice Approach
Sometimes AI agents will perform simplistic analyses like these. These mistakes erode customer trust and lead to bad decisions.
Expert Approach, Context A: Trend Analysis
When the analytical goal is understanding how regions perform over time.
Expert Approach, Context B: Regional Comparison
When the primary goal is comparing regions individually, sorted by growth rate.
East
North
Central
West
South
Expert Approach, Context C: Executive Communication
When the audience is leadership and clarity trumps completeness.
Overall Sales Trend
Q4 2025 Regional Breakdown
The Insight: Any AI can generate a chart. The competitive advantage is generating the right chart: the one an expert would choose for this specific question, audience, and dataset. That judgment is what separates a tool people try from a tool people trust.
Contextual evals let you encode that judgment once and apply it to every analysis your agents produce.
From Expert Judgment to Deployed API
Capture What Good Means
Consult with domain experts to define quality for specific contexts. If they can grade a paper, you can use Truesight. We handle the technical complexity of converting their judgment into automated evaluations.
An expert reviews the same dataset and grades each visualization:
Truesight captures this expert judgment into a contextual eval that your AI agents can call at runtime.
Deploy as API Endpoint
Truesight packages your quality definitions as a REST API your agents can call:
import requests
# Agent generates analysis
query = "Analyze sales trends"
dataset_type = "time_series_regional"
generated_code = analysis.code # e.g. Python/SQL the agent wrote
analysis_report = analysis.report # e.g. narrative report with results & explanations
# Call Truesight eval
response = requests.post(
"https://truesight.goodeyelabs.com/eval/chart_quality",
headers={
"Authorization": "Bearer your_api_key_here",
"Content-Type": "application/json"
},
json={
"inputs": {
"query": query,
"dataset_type": dataset_type,
"generated_code": generated_code,
"analysis_report": analysis_report
}
}
)
result = response.json()Get Pass/Fail with Specific Feedback
The result contains the eval verdict and actionable feedback:
{
"passed": false,
"feedback": "Pie chart used for regional breakdown loses all temporal
information from time series data. Time series shown as bar chart
obscures trend over time. Recommend: (1) Use line chart for quarterly
trend to show temporal patterns clearly. (2) Use horizontal bar chart
for regional comparison in latest quarter only if comparison is the
key question."
}Agent Self-Corrects at Runtime
Your agent calls the eval during generation and uses the feedback to regenerate, learn patterns, or flag for human review, all before the user sees the result.
Your Team Iterates with Confidence
Because the evals lock in specific quality dimensions, your engineers can tune prompts, swap models, or refactor agent architecture and immediately see how each change affects the metrics that matter. No more guessing whether a prompt tweak helped or hurt.
One Example Among Many Quality Dimensions
Chart selection demonstrates how contextual evals work. The same approach applies to other quality dimensions where expert judgment varies by context:
When to treat outliers as errors vs. meaningful extremes. What missing data handling fits this use case.
When causal language is warranted vs. just correlation. Appropriate confidence levels for high-stakes decisions.
When to explore broadly vs. answer specifically. What follow-up analyses matter for this domain.
Technical detail for analyst users. Executive summary for decision-makers.
Each dimension is contextual. Each can be captured as an eval. Each can be deployed as an API your AI agents can call and adapt to.
What This Means for Your Team
Catch quality issues before users do
Your agents call evals during generation and self-correct in real time. Quality problems get resolved at runtime, not in support tickets.
Iterate on prompts and models with clear signal
Lock in quality dimensions so every change comes with before-and-after data. Your team ships faster because they can see exactly what improved and what regressed.
Scale expert judgment across your engineering team
The judgment that makes a senior data scientist effective doesn’t have to stay in their head. Encode it as an eval, deploy it as an API, and every engineer ships with that same standard.
Add depth to your quality metrics
User feedback tells you what resonates. Contextual evals tell you what’s correct. Together they give you the full picture of agent quality.
Build a quality bar that compounds
Each eval you add raises the floor permanently. As you cover more quality dimensions, your agents don’t just avoid mistakes; they consistently make the choices an expert would.
Fits Your Architecture, Not the Other Way Around
Truesight is a REST API, not a platform migration. Add eval calls wherever your agents make decisions and expand coverage at your own pace.
Plain HTTP, any language. Standard POST requests with JSON payloads. If your agents can call an API, they can call Truesight. No SDK required.
Add alongside, don’t rip and replace. Eval calls slot into your existing agent logic as an additional step. No changes to your pipeline architecture.
Versioned eval endpoints. Update quality criteria without redeploying your agents. Roll out new eval versions gradually and compare results side by side.
Start with one dimension, expand from there. Pick a single quality dimension, prove the value, then extend to others as your team sees fit.
See How This Works for Julius
Chart selection is one example. The real value is helping you identify and measure the quality dimensions that matter for your data analysis agents.
Next step: 30-minute demo where we:
- Discuss your specific quality challenges
- Show how contextual evals address them
- Explore a pilot eval for one of your dimensions
Questions? hello@goodeyelabs.com