AI Agents That

Lift revenue. Close pipeline gaps. Win shelf share. Find what changed. Do the Work.

Not Just Analysis.

Purpose-built digital coworkers that monitor performance, investigate what changed, and deliver polished deliverables on-demand + proactively — amplifying your impact 100×.

See Kaiya in Action

Watch the 2-Min Demo

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KAIYA

Coordinating agent team…

Customer_Risk_Brief.pptx

12 slides · $14.8M exposure · Ready

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Trusted by the world’s most innovative teams

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.

The Context

Every mission runs on the same grounded context + reasoning

The know-how in your best analyst's head — your terms, your conventions, what good analysis looks like — now lives in one place. Teach Kaiya once and every mission applies it automatically. And it compounds. The more your team uses it, the sharper it gets.

Skills

Type a question or set a recurring mission. Kaiya understands business context, not just keywords.

Phrase Learning

Your vocabulary mapped to the model. "My region," NBRx, promo cycle — Kaiya knows what your team means.

Query Learning

The queries your team confirmed as right. Preferred cuts and rules of thumb become the default.

Memory

Context that carries across sessions and builds over time — the longest-tail layer, richer every week.

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

6 sources connected to this mission

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

Output:territory_data Rows:15

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

Lib:scipy.stats, sklearn Output:correlation, regression, outliers

# 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

Generated:3 ranked tables

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 PowerPoint

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.

AI Agents That

Not Just Analysis.

What Kaiya Agents Actually Do

Mission-Oriented

24/7 Proactive

Finished Outputs

Every mission runs on the same grounded context + reasoning

Skills

Phrase Learning

Query Learning

Memory

Tellius beats legacy tools, generic LLMs, and vibe-coded toys because…

Agents That Know Your Data Before They Touch It

Industry Veterans From Day One

Multi-Level Deep Insights Across All Your Data

Many specialists. One mission.

Sales Productivity Review

Knowledge Sources

Executive Summary

Every team has a hundred missions waiting to be run.

Territory Performance

Revenue Anomaly Monitor

Variance Investigator

Trade Promotion Optimizer

Executive Brief Agent

An Agent for Every Mission You Define

NRx Drop Investigator

Territory Performance

HCP Targeting Optimizer

Patient Adherence Tracker

Field Force Performance

Trade Promotion Optimizer

Pricing Strategy Agent

Category Growth Detector

Variance Investigator

Revenue Anomaly Monitor

Churn Risk Detector

Pipeline Velocity Agent

Executive Brief Agent

Compliance Watchdog

Missions reach you everywhere Kaiya works

Apps

Browser

Slack & Teams

Inbox

MCP

Embedded

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