The question every founder asks before signing

"What do I actually get for $5K-$8K?"

It is a fair question. Consulting has a reputation problem when it comes to deliverables. Too many founders have paid five figures for a slide deck that says "you should use your data better" without delivering anything that runs in production.

I run fixed-price automation sprints specifically designed for mid-market SaaS founders who have a concrete operational pain point. Not a vague strategy engagement. Not a multi-month transformation. A two-week sprint that starts with a specific problem and ends with working code in production.

This post breaks down exactly what that sprint looks like: the timeline, the deliverables, and three real examples of what founders received.

The two-week sprint structure

Every sprint follows the same structure. This is not a loose framework - it is a tested process that I have refined across dozens of engagements.

Week 1: Discovery and build

Day 1-2: Scoping and discovery

The sprint starts with a 90-minute discovery session. I come prepared, having already reviewed any materials the founder shared during the sales process (dashboards, tool screenshots, process descriptions). During the session, we:

  • Map the current manual workflow step by step
  • Identify every data source involved and assess API availability
  • Define the exact output format (Slack message, email report, dashboard, spreadsheet, or API endpoint)
  • Agree on success criteria in writing - what does "done" look like?
  • Identify who on the team will own the system after handoff

After discovery, I produce a one-page scope document that both sides sign off on. This is critical. Scope creep is what turns a $5K sprint into a $20K open-ended project. The scope document names the specific inputs, outputs, and boundaries.

Day 3-5: Core build

This is where the actual engineering happens. Depending on the project, this typically involves:

  • Setting up data extraction from source systems (APIs, database connections, file exports)
  • Building transformation logic (dbt models, Python scripts, or SQL procedures depending on the client's stack)
  • Creating the delivery mechanism (Slack integration, email automation, dashboard, or API)
  • Writing automated tests that catch failures before they reach the end user

I work in the client's infrastructure whenever possible. If they already have a PostgreSQL database, I use it. If they are on BigQuery, I build there. The goal is to minimize new infrastructure that the team has to learn and maintain.

Week 2: Test, deploy, and handoff

Day 6-7: Testing and hardening

I run the pipeline against real production data (not sample data, not staging data) and validate the outputs with the founder or their designated reviewer. This usually surfaces edge cases: a customer with a negative invoice amount, a timezone boundary that shifts a metric into the wrong week, a source API that paginates differently for large result sets.

Every edge case gets a test. By the end of day 7, the pipeline has a comprehensive test suite that will catch regressions.

Day 8-9: Deployment and monitoring

The system goes live in production. I configure monitoring and alerting so that failures are visible immediately - not discovered two weeks later when someone notices the numbers look wrong.

Monitoring always includes:

  • Pipeline execution alerts (success/failure notifications)
  • Data freshness checks (warn if source data is stale)
  • Output validation (sanity checks on the final numbers)
  • A simple health check that the team can glance at without logging into any tools

Day 10: Documentation and handoff

The final day is entirely about making sure the team can own the system without me:

  • Technical documentation covering the architecture, data flow, and key design decisions
  • A runbook with step-by-step instructions for the five most likely failure scenarios
  • A 60-minute walkthrough session with the person who will maintain the system
  • A clear list of what to do if something breaks and it is outside the team's ability to fix

30-day support window

After the sprint ends, I provide 30 days of support via Slack or email. This covers bug fixes, minor adjustments, and questions about maintenance. In practice, most issues that surface during this period are small: an API credential that expired, a new edge case in the source data, or a formatting tweak to the output.

Three real examples of what founders received

Example 1: Weekly KPI brief automation ($5K, 5 days)

The problem: A Series A SaaS founder (40 employees, $4M ARR) spent 3+ hours every Monday pulling numbers from Stripe, HubSpot, and Google Sheets to compile a leadership brief.

What I delivered:

  • 8 dbt models extracting and transforming data from two APIs plus a CSV export
  • A cron-based pipeline running every Monday at 7:30am
  • A formatted Slack message delivered to the leadership channel with 5 KPIs and week-over-week trends
  • 18 automated data quality tests
  • Documentation and runbook

The result: 3 hours per week returned to the founder. The entire leadership team now references the same numbers, eliminating the "which churn number is right?" debates.

Example 2: Customer health scoring pipeline ($7K, 8 days)

The problem: A Series B vertical SaaS company (80 employees) had no systematic way to identify at-risk customers. The CS team relied on gut feel and anecdotal signals. By the time they noticed a customer was unhappy, the cancellation request was already in.

What I delivered:

  • A scoring model combining product usage data (from Segment), support ticket volume and sentiment (from Zendesk), billing patterns (from Stripe), and NPS survey responses (from Delighted)
  • Scores computed daily and written to a customer_health table in their existing PostgreSQL database
  • A weekly Slack alert listing the top 10 accounts whose health score dropped the most, with the primary contributing factors
  • Integration with their existing HubSpot CRM so that health scores appeared on each company record
  • Threshold-based alerts: any account dropping below a score of 40 triggered an immediate Slack notification to the account owner

The result: The CS team identified and saved two at-risk accounts worth a combined $180K ARR in the first month. The VP of Customer Success said it was the first time they had a leading indicator rather than a lagging one.

Example 3: Invoice reconciliation automation ($8K, 10 days)

The problem: A B2B SaaS company with usage-based pricing was spending 20+ hours per month manually reconciling usage data against invoices. Their billing system (Stripe) and their usage tracking system (a custom PostgreSQL database) frequently disagreed. The finance team caught discrepancies by manually comparing spreadsheets.

What I delivered:

  • An automated reconciliation pipeline that ran daily, comparing usage records against Stripe invoice line items
  • A discrepancy report delivered via email every morning, highlighting any accounts where usage and billing diverged by more than 2%
  • Automated categorization of discrepancy types: under-billing, over-billing, timing differences, and data quality issues
  • A simple web dashboard (built with Metabase, which they already had) showing reconciliation status across all accounts
  • Historical backfill covering the prior 6 months, which uncovered $23K in cumulative under-billing

The result: Monthly reconciliation time dropped from 20+ hours to about 2 hours (reviewing the automated report and handling the exceptions). The historical backfill paid for the entire engagement several times over.

Addressing the objections

"Why not just hire a data engineer?"

You absolutely should hire a data engineer - eventually. But hiring takes 2-4 months (job posting, interviews, offer negotiation, notice period, onboarding). A sprint solves your immediate pain in two weeks. Many of my clients use a sprint to get the urgent problem solved, then hand the system to their first data hire when that person starts. The code, documentation, and tests are all designed for handoff.

For companies under 50 employees, a full-time data engineer may not have enough work to stay engaged. A sprint handles the high-priority automation, and you bring me back for the next one when it surfaces.

"What if it breaks after you leave?"

This is why the testing layer and documentation exist. Every system I build includes automated tests that catch the most common failure modes: stale source data, schema changes, and calculation errors. When something does break, the monitoring fires an alert, and the runbook tells your team exactly what to do.

During the 30-day support window, I fix anything that comes up. After that, you have three options: your team handles maintenance using the documentation, you book a follow-up support block, or you bring me back for the next sprint.

In practice, well-built pipelines with proper testing require very little maintenance. Most of my clients go months between any intervention.

"Can my team actually maintain this?"

I build on tools your team already knows or can learn quickly. dbt models are SQL files. Orchestration is a cron job or a simple Python script. Slack integrations use well-documented APIs. I do not introduce exotic frameworks or proprietary tools that create vendor lock-in.

The handoff session is not a formality. I walk your team through every component, answer their questions, and make sure they are comfortable before I close the engagement. If they are not, we extend the walkthrough until they are.

Why pricing transparency matters

I publish my price range ($5K-$8K for a standard sprint) because I think the consulting industry's habit of hiding pricing wastes everyone's time. You should know what something costs before you get on a sales call.

The variation within that range depends on:

  • Number of data sources - each API integration adds extraction, staging, and testing work
  • Metric complexity - a simple count is faster than a trailing 12-month cohort calculation with expansion and contraction logic
  • Infrastructure setup - if a scheduler or server already exists, the sprint is shorter; if I need to set one up, it takes longer
  • Delivery format - a Slack message is simpler than a multi-page dashboard or an API endpoint

After the discovery session, I provide a fixed price. Not an estimate, not a range - a fixed number. If the project takes me longer than expected, that is my problem, not yours.

How to know if a sprint is right for you

A sprint works well when:

  • You have a specific, repeatable manual process that involves pulling data from multiple tools
  • The process runs on a regular cadence (daily, weekly, monthly)
  • You can name the person who currently does this work and estimate the hours it takes
  • You have API access or database credentials for the relevant source systems

A sprint is not the right fit when:

  • You need ongoing analytics support or ad-hoc analysis (that is a retainer, not a sprint)
  • The problem is organizational rather than technical (you need a data strategy before you need a pipeline)
  • You do not have the source systems or data to work with yet

Ready to scope a sprint? Book a call and we will map your workflow in 30 minutes.

FAQ

What is included in a fixed-price automation sprint?

A standard sprint includes scoping and discovery, the core engineering build (extraction, transformation, delivery), automated testing, production deployment with monitoring, technical documentation, a runbook, a team walkthrough session, and 30 days of post-delivery support. The deliverable is working code running in your production environment, not a prototype or a proof of concept.

How do you keep a two-week sprint from expanding in scope?

The discovery session produces a written scope document that both sides sign before the build begins. The document explicitly names what is included and what is not. If something comes up during the build that falls outside scope, I flag it and we decide together whether to address it in a follow-up sprint. I have never had a sprint exceed its fixed price because the scoping process is rigorous and I have done enough of these to estimate accurately.

What tools and technologies do you use for automation sprints?

I build on the client's existing stack whenever possible. The most common combination is dbt for data transformation, a cron job or lightweight scheduler for orchestration, PostgreSQL or BigQuery for storage, and Slack or email for delivery. I avoid introducing tools the team does not already use unless there is a strong reason. The goal is a system your team can maintain, not a showcase for the latest technology.

What happens after the 30-day support period ends?

You have three options. Most clients maintain the system independently using the documentation and runbook - dbt models are SQL and any engineer comfortable with SQL can modify them. Some clients purchase a follow-up support block (typically 10 hours at a fixed rate) for ongoing peace of mind. Others bring me back when they have the next automation opportunity, usually 3-6 months later. There is no ongoing retainer or subscription required.