Faster reporting
Automated workflows replace manual spreadsheet assembly.
For CEOs and operators who need one version of the truth
Build the data foundation your reporting and AI can finally rely on.
We connect, clean, and validate the systems running your business so leadership gets trusted numbers — no spreadsheet reconciliation.
- 3–5 core systems unified
- 6–10 week engagement
- AI analyst in your team’s existing channel
Why this changes the business
A clean data foundation fixes more than reporting.
When your systems finally line up, leadership moves faster and internal AI becomes usable.
One trusted number set
Every team works from the same definitions.
Cleaner internal AI
AI answers grounded in validated data, not fragmented exports.
Less spreadsheet firefighting
Business logic moves into repeatable pipelines with checks.
Before
- Manual exports and spreadsheet stitching
- Different numbers from different teams
- AI stalls on unreliable source data
After
- Core systems feed one validated layer
- Metrics mean the same thing everywhere
- Teams ask business questions against trusted data
Services
Data Pipelines
Connect your existing systems into one reliable, automated data flow.
Automation
Replace manual reporting and spreadsheet tasks with repeatable workflows.
AI Analytics
Ask business questions in plain language and get answers from validated data.
01. Explore current setup
02. Determine a solution
03. Build and validate
04. Hand over and train
05. Continued support
Customer success stories
What this looks like in practice
Based on common engagements with growing operational businesses.
Manufacturing120 employees$8M+ ARR
From weekly spreadsheet chaos to one trusted operations view
A 120-employee manufacturer with over $8M ARR pulling numbers from ERP, production spreadsheets, purchasing exports, and manually updated QA logs.
Weekly reporting shifted from a full-day exercise to a trusted operating rhythm.
Core pain
Operations, finance, and leadership all had different numbers. The weekly production report took nearly a full day to prepare, and no one trusted scrap, output, or late-order figures without manual checking.
What we changed
Connected ERP, production spreadsheets, QA logs, and purchasing data into one operating layer. Standardized order, production, and product records. Added quality checks for missing fields, duplicates, and mismatched order IDs. Deployed an AI analyst in Microsoft Teams and added a structured downtime capture form for supervisors.
Business outcome
Reporting moved from manual spreadsheet assembly to a mostly automated workflow. Leadership got one trusted set of numbers, and managers could ask operational questions directly in Teams.
Commerce75 employees$5M ARR
Unified revenue visibility across sales, marketing, and finance
A 75-employee commerce company doing roughly $5M ARR using Shopify, HubSpot, ad platform exports, QuickBooks, and spreadsheets.
Commercial decisions shifted from stitched exports to one consistent revenue story.
Core pain
Leadership could not get clean answers to questions like CAC by channel, profitability by product, why finance and marketing reports did not match, and what changed week over week.
What we changed
Connected Shopify, HubSpot, ad data, and finance data into one central repository. Standardized customer, order, campaign, and revenue data. Added validation for duplicate customers, refunds, attribution gaps, and missing campaign tags. Deployed an AI analyst into Slack and added a lightweight form for campaign notes and promotions.
Business outcome
The company moved from fragmented channel reporting to one trusted commercial dataset. Leadership got faster answers on revenue, marketing efficiency, and product performance, and commercial teams could query the AI analyst inside Slack.
Distribution & Services160 employees$12M+ ARR
One operational data layer for service, sales, and fulfillment
A 160-employee distributor and service business with $12M+ ARR using a CRM, ERP, service logs, and spreadsheet-based field updates.
Operational blind spots across teams gave way to one shared view of execution.
Core pain
Sales, operations, and service teams each had partial visibility. Managers struggled to answer questions about overdue jobs, customers with both open service issues and unpaid invoices, fulfillment bottlenecks, and underperforming regions.
What we changed
Connected CRM, ERP, service logs, and spreadsheet field updates into one trusted data layer. Standardized customer IDs, service references, invoice statuses, and territory mappings. Added checks for broken cross-system links, stale job records, and missing updates. Deployed an AI analyst into WhatsApp and built a mobile-friendly form for field staff to submit structured service updates.
Business outcome
The business gained a shared operational view across service, sales, and fulfillment. Managers stopped chasing updates across tools, and the company created a practical foundation for more reliable reporting and future automation.
Scope and pricing
Clear scope, clear finish line
A focused engagement — not an open-ended consulting loop.
Included in the sprint
- Connect 3–5 core systems (ERP, CRM, finance, spreadsheets)
- Clean and standardize data so metrics mean the same thing everywhere
- Automated validation and quality checks
- One centralized, trusted data layer
- AI analyst deployed in your messaging channel
- Technical handoff documentation
Typically out of scope
- Dashboard redesigns and broad BI rebuilds
- Full internal app or platform development
- Major ERP or CRM reconfiguration projects
- Large-scale historical cleanup or reconstruction
- Enterprise change management or training programs
- 24/7 support or enterprise SLA support
Sprint engagement Starting at $10,000
Designed to get the foundation in place without turning into a long implementation program.
Ongoing support Available upon request
Ongoing monitoring & maintenance as needed
Best fit
Outgrown spreadsheets, no data foundation yet?
This engagement is for you if most of these apply.
- $1M+ ARR with growing operational complexity
- 50–200 employees across multiple teams
- Manufacturing, distribution, or commerce
- Multiple disconnected tools (ERP, CRM, spreadsheets)
- No dedicated data architecture function
- Manual or inconsistent reporting
Questions buyers usually ask
Frequently asked questions
How long does the sprint take?
Most engagements take 6–10 weeks from kickoff to a working AI analyst. The exact timeline depends on the number of source systems and the complexity of your data.
Do we need a data warehouse already?
No. We set up the data infrastructure as part of the sprint. You don't need any existing warehouse, data lake, or pipeline tooling in place before we start.
Can this work with our ERP and CRM?
Yes. We regularly work with systems like SAP, NetSuite, Microsoft Dynamics, Salesforce, HubSpot, Shopify, QuickBooks, and many others. If your system has an API or data export, we can connect it.
Do we need to replace our current reports?
No. The sprint creates a trusted data layer underneath your existing tools. Your current reports can stay in place — they just get fed better, cleaner data.
Which messaging tools can the AI analyst use?
We can deploy the AI analyst into Microsoft Teams, Slack, WhatsApp, or Telegram — whichever your team already uses day to day.
What does the monthly retainer include?
The ongoing retainer covers monitoring data pipelines for failures, maintaining data quality checks, adapting to source-system changes, and minor adjustments to the AI analyst. It does not include new feature development or major scope expansion.
What is not included?
The sprint does not include dashboards, custom reporting, full internal app development, major ERP/CRM reconfiguration, company-wide data cleanup programs, enterprise change management, or 24/7 support.
How much support is needed from our internal team?
We typically need one internal point of contact who can coordinate access to your systems and answer domain-specific questions. We don't require dedicated engineering resources on your side.
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