Composable Agents. Not Fragile Scripts.

DataGOL treats agents as durable system components that are reusable, versionable, observable, and deployable across workflows without rebuilding logic each time.

Multi-Agent Orchestration

Powered By ContextOS

Data is necessary but insufficient; context is what makes AI resilient and relevant.

Role, History, Permissions, KPIs

Role, History, Permissions, KPIs

These shape what "good" looks like for each request.

Continuity Across Journeys

Continuity Across Journeys

Context follows issues across time, channels, and teams. No re-explaining. No lost state.

Right Answer, Right View

Right Answer, Right View

Two people ask the same question, need different responses. Role-appropriate. Evidence-backed.

Compose Agents Like Infrastructure

Compose Agents Like Infrastructure

Compose Agents Like Infrastructure

DataGOL treats agents as durable system components. You can reuse them, chain them, version them, and deploy them across workflows without rebuilding logic each time

The DataGOL Advantage

The DataGOL Advantage

DataGOL gives you the fastest path to launching and scaling a vertical AI product, without sacrificing trust or reliability.

  • Launch customer‑facing agents in weeks, not quarters

  • Ship AI your customers can rely on

  • Build reusable intelligence, not brittle bots

  • Create a real moat beyond models



DataGOL gives you the fastest path to launching and scaling a vertical AI product, without sacrificing trust or reliability.

  • Launch customer‑facing agents in weeks, not quarters

  • Ship AI your customers can rely on

  • Build reusable intelligence, not brittle bots

  • Create a real moat beyond models



system.ready·multi_agent_orchestration_engine
Multi-Agent
Orchestration

Compose, chain, and orchestrate specialized AI agents that work together to solve complex data workflows — autonomously.

13agents
7stages
142mslatency
| 99.9% uptime
Composable pipelinesParallel branchingReal-time executionAuto-scaling
Start

Pipeline trigger received — dispatching agents

Scroll to explore7 stages · 13 agents · 142ms
pipeline_input
Describe your data analysis task...
01 · INTERPRET

Context & Intent Analysis

Before dispatching agents, the planner decomposes your request — understanding intent, recalling prior analyses, and building a structured task plan.

Reasoning / Planner Agent— Task Interpretation
task_plan.yaml
analyst: "Data Analyst DF"
dataset: "Telco_Churn.csv"
unknown: "churn drivers in first 6 months"
data: "multi-channel acquisition + churn"
condition: "tenure_months <= 6"
intent_breakdown:
retention: "D1 / D14 anchored vs install"
attribution: "CAC + LTV + ROAS by channel"
signals: "behavioral drivers of retention"
agents_required: "search, sql, datascience, viz"
Episodic Memory Check— Contextual recall
match_found:true
match:churn segmentation report (April)
→Similar analysis scope identified
→Same 6-month cohort window
→Same acquisition intent
Expert Analysis Panel
original_question

"Which acquisition channels have the highest churn rate in the first 6 months, and how does that compare to their CAC and LTV?"

feedback_query_normalized

SELECT acquisition_channel, churn_rate, cac, ltv WHERE tenure_months <= 6 GROUP BY channel, cohort_week

conditiontenure_months <= 6
intent_analysis
•Compare churn rate across all acquisition channels
•Cross-reference with blended CAC per channel
•Estimate 12-month LTV and payback period
•Identify highest-risk early-life segment
4
sub-tasks
2
data sources
142ms
plan latency
02 · DISPATCH

Parallel Agent Dispatch

The pipeline splits into specialized agents running concurrently — each one tackling a different data domain for maximum throughput.

User Request

"Which acquisition channels have the highest churn rate in the first 6 months, and how does that compare to their CAC and LTV?"

Search Agent— Knowledge retrieval

Searching connected knowledge sources...

Internal analytics wiki + methodology docs
data_sources_located4 resolved
→Marketing CRM export
→Product event stream
→Revenue subscriptions
→Churn surveys
SQL Agent— Warehouse schema + seed queries
warehouse_schema
fact_customer_lifecycle
customer_idsignup_datechurn_datechurnedtenure_monthsplan_typeacquisition_channelmonthly_revenue
fact_marketing_channel_monthly
monthacquisition_channelspend_usdimpressionsclicksinstallsactivations
dim_plan
plan_typeplan_priceplan_family
dim_channel
acquisition_channelchannel_type
dim_churn_reason
churn_reasonchurn_group
seed_queries
churn_rate_by_channel5 rows12ms
cac_by_channel5 rows8ms
churn_by_plan_type15 rows18ms
monthly_churn_trend30 rows14ms
agent_dispatch_config
{
  "agents": ["parse","summarize","query_db","transform"],
  "parallel": true,
  "timeout_ms": 5000,
  "priority": "high"
}
03 · PROCESS

Parallel Data Processing

Each dispatched agent works simultaneously — parsing documents, querying the warehouse, and transforming raw records into clean cohort data.

Parse Agent— Extract & structure

Extracting and structuring event fields from raw data.

extracted_fields
user_idactivation_timestampsession_countfeature_usageretention_day
events parsed3.1M
derived timestamps4 types
D1–D30 markersset
Summarize Agent— Condense content

Summarizing cohort performance across marketing channels.

summary_preview

Top acquisition sources by install volume:

1.Paid Social
2.Google Search
3.Influencer campaigns
preliminary_finding: Search traffic shows higher early activation.
Query DB— Generate analytical tables

Building cohort tables and revenue joins.

Cohort tables created
fields:
cohort_weekacquisition_channelretention_d1retention_d7retention_d14ltvcac
Transform Agent— Normalize datasets

Normalizing datasets and aligning cohort anchors.

Generated 2 cohort models
Model AInstall anchored
Model BActivation anchored
field_lineage
SourcesWarehouseDerivedTransform
product_eventsStreamuser_idevent_typetimestampsession_idfeature_nameplatform
user_lifecycleWarehouseuser_idsignup_dateactivation_datechurn_datetenure_monthsacquisition_channel
cohort_metricsDerivedcohort_weekacquisition_channelretention_d1retention_d7retention_d14ltvcac
anchored_modelsTransformmodel_typeanchor_dateuser_idretention_curvenormalized_ltv
3.1M
events parsed
7
derived fields
2
cohort models
04 · CONVERGE

Intelligent Convergence

All upstream outputs merge into a single reasoning layer that cross-references, deduplicates, and enriches the combined dataset.

Data Science Agent— Statistical modeling

Running statistical analysis and cohort modeling.

→ parse_results→ summaries→ query_data→ transformed
Grounded Execution Plan REVIEW REQUIRED

"Before running the full analysis, here is the grounded execution plan. Review the data sources, filters, formulas, code, and planned outputs."

Approve & Run Analysis
or review sections below →
Objective

Analyze churn for customers within their first 6 months of tenure and evaluate acquisition efficiency by channel.

•Churn rate by acquisition channel
•CAC vs estimated LTV by channel
•Top churn reasons
•Churn by plan type
•Monthly churn trend
Grounded Data
1. fact_customer_lifecycle
customer_idsignup_datechurn_datechurnedtenure_monthsplan_typeacquisition_channelmonthly_revenue
2. fact_marketing_channel_monthly
monthacquisition_channelspend_usdimpressionsclicksinstallsactivations
3. dim_plan
plan_typeplan_priceplan_family
4. dim_channel
acquisition_channelchannel_type
5. dim_churn_reason
churn_reasonchurn_group
Filters & Assumptions
→Include customers where tenure_months ≤ 6
→Include churned and active customers for denominator consistency
→Use monthly marketing totals aggregated by acquisition_channel
→Estimate retention as 1 − churn_rate
Metrics & Formulas
Churn Rate= churned_customers / total_customers
Retention Rate= 1 − churn_rate
Blended CAC= total_spend / total_installs
Cost per Activation= total_spend / total_activations
Est. 12-Month LTV= avg_monthly_revenue × 12 × retention_rate
LTV / CAC Ratio= estimated_12m_ltv / blended_cac
Planned Deliverables
KPI summary cards
Channel churn ranking
CAC vs LTV chart
Monthly trend chart
Top churn reasons
Plan-type breakdown
Downloadable CSV
Executive summary
4
inputs merged
3
models applied
99.7%
data fidelity
05 · GENERATE

Analysis & Visualization

Specialized agents produce final artifacts — pattern detection, statistical insights, and interactive data visualizations.

Analyze— Pattern detection

Identifying patterns associated with high retention users.

key_insight

High retention users share three signals:

Activate within first session
Use collaboration features within 48 hours
Originate from high-intent search traffic
analysis_metrics
patterns detected3 signals
confidence94.2%
cohorts analyzed42 weekly
Visualize— Rendering charts

Generating 6 interactive charts from the analysis output.

12,481
Total Customers
3,892
Churned
31.2%
Overall Churn
Organic
Best Channel
Retention CurveD1–D30 by acquisition channel
Google SearchInfluencerOrganicPaid SocialReferral
CAC vs Estimated LTVBy acquisition channel
Google SearchInfluencerOrganicPaid SocialReferral
ROAS by ChannelReturn on ad spend comparison
Churn Rate by Channeltenure ≤ 6 months, sorted desc
Monthly Churn TrendChurned customers by month & channel
Google SearchInfluencerOrganicPaid SocialReferral
Top Churn ReasonsEarly-tenure customers (≤ 6 months)
Churn by Plan TypeChurn rate % by plan & channel
Google SearchInfluencerOrganicPaid SocialReferral
06 · DELIVER

Pipeline Complete

All results compiled, validated, and delivered — narrative summary, ranked tables, charts, and downloadable artifacts.

Output— Final delivery
overall_churn_profile
~1,500
Customers analyzed
~24%
Early churn rate
≤ 6 months
Tenure filter

Roughly one in four new customers churns within the first six months, making early lifecycle retention a critical improvement area.

channel_performance
lowest_churn
Referral
Google Search

Highest quality users with stronger retention profiles.

highest_churn
▲Influencer campaigns
▲Paid Social

Generate volume but lower customer quality and faster churn.

marketing_efficiency
best_ltv_cac_ratio
Organic

Extremely low acquisition costs deliver exceptionally strong efficiency.

weakest_efficiency
Paid Social

High acquisition costs + above-average churn reduce ROML.

plan_churn_and_drivers
Churn by plan:
highest:Standard
lowest:Basic

Suggests pricing or product-fit issues in the mid-tier offering.

Top churn drivers:
1.Competitor higher speeds
2.Poor support experience
3.Price sensitivity
executive_takeaways
1. Acquisition quality varies dramatically by channel
Referral and search traffic deliver the highest retention and strongest customer value.
2. Paid social drives volume but weak economics
High CAC combined with elevated churn produces the lowest efficiency channel.
3. Organic acquisition is extremely efficient
Organic users cost very little to acquire and produce the best LTV/CAC ratio overall.
4. Customer support is a major retention lever
Support quality appears among the top churn drivers — operational improvements could significantly improve retention.
5. Early lifecycle retention is the most important growth lever
Reducing churn during the first six months would dramatically improve lifetime value across all channels.
deliverables
executive_summary.md12 KB
channel_performance_ranked.csv48 KB
churn_by_channel.svg190 KB
cac_vs_ltv_scatter.svg280 KB
monthly_trend.svg210 KB
churn_reasons.svg175 KB
churn_by_plan_type.svg195 KB
retention_curves.svg320 KB
full_data_export.csv2.4 MB
pipeline_run_summary
agents_invoked13
context_analysisreasoning_agent
execution_plangrounded ✓
parallel_branches2
sub_tasks4
convergence_pointdata_science_agent
charts_rendered7
deliverables9 files
dashboardpublished ✓
total_latency142ms
status✓ completed
Publishing to Dashboarddelivered
6/6
datagol.ai/dashboard/churn-analysis
12.5K
3.9K
31.2%
Organic
Ready for next analysis

The full pipeline ran in 142ms end-to-end. All agents have been returned to standby. Your dashboard is live at datagol.ai.

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DataGOL© 2026 DataGOL. All rights reserved.

Built for Customer Products & Internal Systems

Built for Customer. Products & Internal Systems

Built for Customer Products & Internal Systems

DataGOL treats agents as durable system components. You can reuse them, chain them, version them, and deploy them across workflows without rebuilding logic each time

DataGOL treats agents as durable system components. You can reuse them, chain them, version them, and deploy them across workflows without rebuilding logic each time

INTERNAL · ACROSS YOUR ORGANIZATION
Deploy AI across your teams.
Copilots for data, ops, and product
Faster experimentation cycles
Shared organizational context
Unified system of execution
EXTERNAL · IN YOUR PRODUCT
Ship AI inside your SaaS product.
Embed agents directly into your application
Deliver instant, trusted outputs to customers
Maintain strict tenant isolation
Scale across accounts without per-customer rewrites
Policy Guardrails & Enforcement
Output validation, content filtering, and safety constraints are native, not afterthoughts.
Enforced
Built-In Token & Cost Controls
Usage tracking and budget enforcement per agent, per tenant, per workflow. No surprise bills. No runaway compute.
Metered
Full Interaction Observability
Every agent interaction traced end-to-end. Latency, token usage, reasoning paths — captured and queryable in real time.
Traceable
Deterministic Temporal Workflows
Durable execution with automatic retries, timeouts, and versioned workflow definitions. Every step is recoverable and auditable.
Deterministic

Organizational Memory
is Your Long-Term Moat.

Knowledge shared with agents isn't lost. DataGOL stores business context, user corrections, and domain-specific intelligence.

Business Context

Persistent understanding of your specific domain logic.

Feedback Loops

Agents learn from every user correction and edit.

Compound Growth

Your vertical advantage grows with every interaction.

BUILT_FOR

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CTOs shipping customer-facing AI

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3

CEOs selling AI to enterprise buyers

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Teams that need speed and

trust

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