AI Agents That
Not Just Analysis.
Purpose-built digital coworkers that monitor performance, investigate what changed, and deliver polished deliverables on-demand + proactively — amplifying your impact 100×.
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The Capabilities
What Kaiya Agents Actually Do
Three things separate doing the work from answering questions.
01 · Mission-Oriented
Mission-Oriented
Set the outcome. Kaiya plans and runs the mission.
- Declarative, not scripted.Describe the outcome; Kaiya figures out the steps. No prompt engineering, no pipeline.
- Grounded in your semantic layer.Runs on metrics your team already trusts.
- Autonomous or interactive.Hands-off by default; shifts to back-and-forth when you want to drill.
02 · 24/7 Proactive
24/7 Proactive
Runs on your cadence — and on data's.
- Scheduled and event-driven.Briefs land before meetings; alerts fire the moment a KPI breaks pattern.
- Persistent memory.Each mission builds on the last. Recurring briefs get sharper.
- Scales past the analyst queue.More questions than capacity — Kaiya closes the gap.
03 · Finished Outputs
Finished Outputs
Every mission ends in something your team can act on.
- Board-ready artifacts.Briefings, driver memos, PDFs, Slack summaries.
- Recommendations, not readouts.Not "here's what the data shows" — "here's what to do."
- Triggers the next step.Routes to stakeholders and fires follow-up missions.
Why Tellius
Tellius beats legacy tools, generic LLMs, and vibe-coded toys because…
Data Context
Agents That Know Your Data Before They Touch It
Your metrics, entity relationships, and access rules are encoded in a governed semantic layer. Kaiya reasons against that layer, not raw SQL, so agents never invent definitions or produce wrong joins. Change a definition once and every agent reflects it immediately.
Deep Insights & Domain Knowledge
Industry Veterans From Day One
Agents arrive fluent in pharma, CPG, FP&A, and RevOps, with Price-Volume-Mix decomposition, NRx territory drivers, and brand share shifts built in from day one. No six-month onboarding to teach your agent how your industry actually works.
Analytic Rigor
Multi-Level Deep Insights Across All Your Data
Like having a team of AI analysts working your problem around the clock. Kaiya runs thousands of hypotheses across region, territory, product, channel, and customer, pulling signal from unstructured sources like call notes and rep feedback alongside structured metrics.
Missions · A Squad of Agents, One Deliverable
Many specialists. One mission.
Specialist agents — for SQL, statistical analysis, ranking, synthesis — each handle their piece of the investigation. Kaiya orchestrates the squad, governs the work, and lands a single board-ready brief. When the trigger fires, the whole squad goes to work autonomously.
Sales Productivity Review
You are viewing: Sales Productivity Review · Approved by Maya Rodriguez · 2 days ago
LIVE
v3.1
Objective · Written by the Mission Owner
"Investigate the relationship between rep activity and closed revenue across territories. Surface efficiency outliers, identify replicable best practices, and recommend remediation for underperforming territories."
Knowledge Sources
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Data Warehouse
fact_activities · dim_account
CRM
Opportunity · Activity
Marketing
Campaigns · Attribution
Financials
Bookings · ARR
People
Reps · Territory
Call Intelligence
Transcripts · Sentiment
Triggered by Anomaly·2 min ago
Activity volume +12% QoQ but closed revenue flat — variance threshold breached across 4 territories
Auto-investigating
Iteration 1
Step 1
SQL Query— Extract territory-level aggregated activity and revenue data for correlation analysis
Step 2
Python Analysis— Compute correlation statistics, fit linear regression, identify outlier territories
Step 3
Python Analysis— Create detailed summary tables for correlation stats, outliers, and efficiency rankings
Step 4
Summary— Generate executive summary with correlation analysis, outlier insights, and actionable recommendations
01SQL Query · Running
SELECT
Territory_Code AS territory,
SUM(Activity_Count) AS total_activities,
SUM(Closed_Won_ARR) AS total_arr,
COUNT(DISTINCT Account_ID) AS unique_accounts
FROM crm.fact_sales_activity
WHERE Territory_Code IS NOT NULL
GROUP BY Territory_Code
ORDER BY total_activities DESC
02Python Analysis · Running
# Compute Pearson + Spearman correlation
pearson_r, pearson_p = pearsonr(df.total_activities, df.total_arr)
spearman_r, _ = spearmanr(df.total_activities, df.total_arr)
# Fit linear regression to get expected ARR per territory
model = LinearRegression().fit(X, y)
df['expected_arr'] = model.predict(X)
df['residual'] = df.total_arr - df.expected_arr
df['efficiency'] = df.total_arr / df.total_activities
# Flag outliers via z-score on residuals
df['z'] = (df.residual - df.residual.mean()) / df.residual.std()
outliers = df[abs(df.z) > 1.5]
03Summary Tables · Running
Top Most Efficient Territories
| Rank | Territory | $/Activity | vs Avg |
|---|---|---|---|
| 1 | AMER-NE | $3.2K | +71.2% |
| 2 | EMEA-DACH | $2.6K | +39.3% |
| 3 | AMER-MW | $2.4K | +29.7% |
| 4 | APAC-JP | $2.2K | +15.3% |
Bottom Least Efficient
| Rank | Territory | $/Activity | vs Avg |
|---|---|---|---|
| 11 | EMEA-NORDICS | $1.6K | −15.4% |
| 12 | EMEA-UKI | $1.6K | −17.3% |
| 14 | AMER-SE | $1.3K | −31.0% |
| 15 | AMER-WEST | $1.2K | −36.0% |
Executive Summary
Synthesized by Kaiya
.svg.png)
PPTX · 12 slides
Yes — but activity quality matters significantly more than volume.
Pearson r = 0.664 (p<0.01) explains only 44.1% of variance — each activity generates roughly $1.10K ARR, but efficiency spreads 167% across territories.
Correlation
0.664r
Moderate · significant
Opportunity Gap
+18.8%
+$26M ARR recoverable
Efficiency Spread
167%
$3.2K vs $1.2K per activity
Where the $26M Opportunity Lives
ARR variance from expected · Top & bottom 4 territories shown
AMER-NE
+$8.5M
EMEA-DACH
+$7.7M
AMER-MW
+$5.2M
APAC-JP
+$2.7M
7 territories near average
EMEA-NORDICS
−$1.5M
EMEA-UKI
−$1.7M
AMER-SE
−$3.6M
AMER-WEST
−$5.9M
Key Findings & Recommendations
- 9 of 15 territories (60%) underperform expectations — a $26M ARR opportunity gap (+18.8%).
- Top territories convert at $3.2K ARR/activity vs bottom at $1.2K — a 167% efficiency spread.
- AMER-NE delivers +$8.5M ARR (+57.5%) over expected with the lowest activity volume — top replication candidate.
- AMER-WEST and AMER-SE underperform by $5.9M and $3.6M ARR despite high activity — quality, not volume, is the gap.
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