Workshop: AI Analytics Use Cases

What AI changes about data engineering?

What your team deals with every day

"Senior engineers write staging models instead of building architecture."
80% of tickets are pattern-based. An AI agent handles them in minutes.

→ 5 tickets/day, 1 engineer reviewing.
"Backlogs grow faster than we can hire."
Every new hire needs 3–6 months to ramp. Same team. 3× output. No hiring cycle.

→ No new headcount required.
"The tools exist. A repeatable workflow doesn't."
This workshop installs that
workflow. Your team runs it from Monday.

→ Live on day one.

What participants build on the day?

Working Infrastructure, Not a Slide Deck.

Production DWH ready to connect your data
A fully operational staging / curated / presentation DWH on a cloud platform. Pre-built as a template ready to connect to your own data sources and deploy.
Automated pipeline, runs itself from day one
dbt models connected to Airflow DAGs with full pipeline execution: staging → curated → presentation. Scheduling, failure handling, and log inspection covered end-to-end.

Ticket in, code out - in under 5 minutes
An AI agent that reads a Jira ticket, generates dbt model code, tests, and documentation, then submits a pull request. The full cycle runs in under five minutes.
SOX-ready governance built into every deploy
Git-based workflow: branch → PR → CI build (dbt build + test) → merge → deploy. Governance, quality, and SOX compliance are built into the process by default.

What does the day look like?

What you build, hour by hour

Each block ends with something your team has. Not a theory, a working artifact.

09:00 DWH in 60 minutes
Deploy a 3-layer DWH from a dbt template. Staging models, naming conventions, tests wired up and green.

✓ You have: working 3-layer DWH, deployed, all tests passing
10:30 Airflow pipeline + CI/CD
Wire the orchestration layer. DAG templates, environment configs, first automated deploy with SOX-ready governance baked in.

✓ You have: automated pipeline running on schedule, CI/CD configured
12:00 Agent architecture + intent routing
Build the AI agent that reads Jira tickets and routes by intent. Prompt engineering, structured output, error handling.

✓ You have: intent router that classifies and routes ticket types
14:00 Full cycle: ticket to PR in under 5 minutes
Create a Jira ticket. Watch the agent generate dbt code, write tests, open the PR. Review it. Merge it. Table is live.

✓ You have: complete working pipeline, your data, your warehouse, end-to-end
15:30 Edge cases, ROI math, deployment playbook
Handle complex tickets, schema drift, multi-table dependencies. Calculate your team's ROI and build the 30-day adoption plan tailored to your stack.

✓ You have: playbook your team runs from Monday

Why this workshop produces ROI?

The Math Behind One Day

$30K–$50K vs Hiring 1 DE
Onboarding cost for one new hire, 3-6 months to full productivity. This workshop delivers the same capability uplift for 12 people in one day.
$20K–$40K vs Building Internally
4-8 weeks of a senior engineer's time to build DWH + CI/CD + agent + playbook, delivered in 8 hours. Your team owns it with no vendor dependency.
3× output
ROI After Week 1
5 DEs handling 15 tickets/week move to 40+ tickets with the same headcount. The workshop pays for itself in the first week of production use.

Every participant receives working infrastructure they can deploy immediately

The workshop is not a learning exercise, it's a delivery sprint with an output your organization keeps.

Complete dbt project - reusable as a template

Models, tests, documentation, and CI/CD configuration. Ready to connect to your own data sources without rebuilding from scratch.

Airflow DAG templates for dbt orchestration

Production-grade scheduling logic, adapted to your pipeline patterns and error-handling requirements.

AI agent configuration and prompt templates

The exact agent setup used during the workshop, including prompt templates calibrated for data engineering ticket formats.

30-day team adoption playbook

Tailored to your stack, team size, and backlog. Week 1 tasks written down before you leave the room.

Who should attend?

The Right People In the Room

Data & Analytics Engineers
"I spend my days writing staging models instead of building architecture."

You leave with a working agent handling the pattern work, so you get back to architecture. Prerequisite: basic SQL and Git.
Platform and DE Team Leads
"My team delivers 15 tickets a week and the backlog has 200."

You leave with a concrete model for AI-assisted delivery. Same headcount, 3× throughput.
CDOs and VP Data
"I need to know if this works before I bet my budget on it."

You leave with a live proof-of-concept, the ROI math, and a 30-day plan. A running pipeline, not a slide deck.

What happens after?

The workshop is a starting point

Week 1-2: Follow-up call with workshop sponso
Review what landed, identify priority use cases for your team's specific backlog. A working session, not a sales call.
Week 3–4: Optional Data Readiness Assessment
$5K–$10K, credited in full toward any engagement. Maps your data estate to the pipeline model you've just built.
Month 2+: AI-powered capacity packs
Transition to ongoing Analytics Agent modules. Your team keeps ownership. Astral Forest keeps the infrastructure running.

From Jira Ticket to Production Table.
In One Day.

What your team deals with every day

"Senior engineers write staging models instead of building architecture."

"Backlogs grow faster than we can hire."

"The tools exist. A repeatable workflow doesn't."

Working Infrastructure, Not a Slide Deck.

Production DWH ready to connect your data

Automated pipeline, runs itself from day one

Ticket in, code out - in under 5 minutes

SOX-ready governance built into every deploy

What you build, hour by hour

09:00 DWH in 60 minutes

10:30 Airflow pipeline + CI/CD

12:00 Agent architecture + intent routing

14:00 Full cycle: ticket to PR in under 5 minutes

15:30 Edge cases, ROI math, deployment playbook

The Math Behind One Day

$30K–$50K vs Hiring 1 DE

$20K–$40K vs Building Internally

3× output
ROI After Week 1

Every participant receives working infrastructure they can deploy immediately

Complete dbt project - reusable as a template

Airflow DAG templates for dbt orchestration

AI agent configuration and prompt templates

30-day team adoption playbook

The Right People In the Room

Data & Analytics Engineers

Platform and DE Team Leads

CDOs and VP Data

The workshop is a starting point

Week 1-2: Follow-up call with workshop sponso

Week 3–4: Optional Data Readiness Assessment

Month 2+: AI-powered capacity packs

One Day.
Your Whole Team.

We are here to answer your questions

From Jira Ticket to Production Table.In One Day.

What your team deals with every day

"Senior engineers write staging models instead of building architecture."

"Backlogs grow faster than we can hire."

"The tools exist. A repeatable workflow doesn't."

Working Infrastructure, Not a Slide Deck.

Production DWH ready to connect your data

Automated pipeline, runs itself from day one

Ticket in, code out - in under 5 minutes

SOX-ready governance built into every deploy

What you build, hour by hour

09:00 DWH in 60 minutes

10:30 Airflow pipeline + CI/CD

12:00 Agent architecture + intent routing

14:00 Full cycle: ticket to PR in under 5 minutes

15:30 Edge cases, ROI math, deployment playbook

The Math Behind One Day

$30K–$50K vs Hiring 1 DE

$20K–$40K vs Building Internally

3× output ROI After Week 1

Every participant receives working infrastructure they can deploy immediately

Complete dbt project - reusable as a template

Airflow DAG templates for dbt orchestration

AI agent configuration and prompt templates

30-day team adoption playbook

The Right People In the Room

Data & Analytics Engineers

Platform and DE Team Leads

CDOs and VP Data

The workshop is a starting point

Week 1-2: Follow-up call with workshop sponso

Week 3–4: Optional Data Readiness Assessment

Month 2+: AI-powered capacity packs

One Day.Your Whole Team.

We are here to answer your questions

From Jira Ticket to Production Table.
In One Day.

3× output
ROI After Week 1

One Day.
Your Whole Team.