Reesync AI Ltd
Fix manual reporting, spreadsheet bottlenecks, and dashboards nobody fully trusts.
Reesync AI helps businesses automate messy reporting processes, improve data quality, and turn operational data into reliable dashboards and plain-English insight.
Senior AWS data engineering, Tableau, and AI consulting from James Rees — 13+ years' experience delivering analytics and reporting platforms in regulated UK environments.
Primary offer
Reporting & Data Automation Sprint
A focused 10-working-day sprint for businesses with manual reporting packs, spreadsheet bottlenecks, unreliable dashboards, repeated KPI commentary, or operational data quality issues.
A fixed-scope first engagement designed to create clarity and a practical next step without starting a large transformation programme.
Many businesses do not have a lack of dashboards. They have a trust problem.
- —The data arrives late.
- —The spreadsheet needs manual fixes.
- —The KPI definition is unclear.
- —The dashboard looks polished, but people still question the numbers.
The Reporting & Data Automation Sprint is designed to identify one high-value reporting or data process that can be simplified, automated, or made more trustworthy.
What you get
- Review of one existing reporting, spreadsheet, dashboard, or data process
- Current-state process map
- Data quality and trust issues identified
- Highest-value automation opportunity selected
- Working prototype or practical implementation roadmap
- Plain-English summary of risks, quick wins, and recommended next steps
Best for teams who say
- "This report still takes hours every week."
- "We do not fully trust the dashboard."
- "Only one person understands the spreadsheet."
- "We keep arguing about whose number is right."
- "The commentary is repetitive and manual."
- "We need better reporting, but we are not ready for a huge transformation project."
The process
What happens in 10 working days
Understand the process
We review the existing reporting process, spreadsheet flow, dashboard, data source, or manual workflow. The goal is to understand where time is lost, where trust breaks down, and where automation would create the most value.
Identify the data quality and trust issues
We check for issues such as missing values, duplicate records, late data, inconsistent definitions, manual adjustments, unclear ownership, or fragile spreadsheet logic.
Build the prototype or solution outline
Depending on the situation, this may include a data quality checker, cleaned data output, reporting automation, dashboard improvement, KPI commentary generator, or implementation design.
Package the findings
You receive a practical summary of what was found, what should be automated first, what risks exist, and what the next implementation step should be.
Core capabilities
What the sprint draws on
The sprint is supported by deep experience across AWS data engineering, Tableau, operational reporting, data quality, and AI-assisted automation.
AWS Data Engineering
Design, build, and improve cloud data pipelines using services such as AWS Glue, S3, Redshift, Athena, Lambda, Step Functions, and EventBridge.
Useful for teams that need cleaner ingestion, more reliable data processing, better observability, or production-grade reporting data foundations.
Tableau & BI Reporting
Build and improve dashboards, reporting layers, Tableau Server environments, permissions, extracts, row-level security, and operational reporting processes.
Useful for teams that need dashboards people trust, reporting processes that scale, and clearer definitions behind business KPIs.
AI-Assisted Reporting & Commentary
Use AI to help explain KPI movements, summarise data quality issues, generate management commentary, and highlight operational exceptions.
This is not generic chatbot work. The focus is on helping teams understand what changed, what needs attention, and whether the underlying data can be trusted.
Data Quality & Reporting Health Checks
Review existing reporting flows, spreadsheets, dashboards, and data pipelines to identify where trust breaks down.
Typical checks include data freshness, duplicates, missing values, schema changes, manual adjustments, inconsistent definitions, and unclear ownership.
What you could walk away with
Example sprint outputs
These illustrate the kind of working prototypes and deliverables the sprint can produce, depending on the problem.
Data Quality Monitor
A prototype that checks data before it reaches a dashboard.
Example checks
- Freshness
- Row count changes
- Duplicate IDs
- Missing required fields
- Schema changes
- Basic outliers
Yesterday's orders file contains 42% fewer rows than the 7-day average. Customer ID is missing in 8.4% of rows, above the 2% threshold. Three duplicate order IDs were found. The weekly dashboard should be treated as incomplete until the source extract is checked.
Spreadsheet to Dashboard
A workflow that takes a messy spreadsheet or CSV file, validates it, cleans it, and prepares it for dashboarding or automated reporting.
Typical output: a cleaned, validated dataset ready for dashboarding or automated reporting.
AI Reporting Commentary
A workflow that takes KPI movements and data quality checks, then produces a plain-English summary of what changed, what needs attention, and what caveats should be included in management reporting.
Typical output: a plain-English KPI movement summary with data quality caveats and recommended follow-up actions.
Selected work
Case studies
These examples show the kind of reporting, data quality, automation, and stakeholder trust problems the Reporting & Data Automation Sprint is designed to uncover and solve.
Fleet MI
AWS data engineering, telematics integration and Tableau delivery
A UK regulated utility
The problem
Driver telematics and vehicle financials lived in separate systems with no unified view. Safety events were hard to monitor, utilisation was unclear, and fuel and maintenance spend was difficult to control.
The solution
Built an end-to-end AWS pipeline and Tableau suite combining Webfleet telematics and TM1 financial data via S3, Glue/PySpark, Redshift and Tableau. Included row-level security aligned to the organisational hierarchy, 5-minute live route and position tracking, and GDPR/DPIA compliance for location data. Replaced legacy reporting tooling entirely.
Trust & governance benefit
Replaced a fragile, manually maintained legacy reporting setup with a governed, automated pipeline. Managers got data they could act on without questioning its source.
Outcomes
Tech used
Fatigue Manager
Award-winning safety analytics - AWS ETL and Tableau monitoring
A UK regulated utility
Winner - Automation Project of the Year, UK IT Industry Awards 2021
The problem
Engineer fatigue was a safety and compliance risk. There was no reliable data to quantify it, and no operational tool for managers to monitor it day to day.
The solution
Designed and built raw and summarised fatigue datasets from timesheet exports, applying business rules to classify work/non-work and core/overtime hours. Productionalised ETL on AWS and built Tableau dashboards with drill-down from company to individual level. Implemented row-level security so managers only saw their own reporting lines, with Gantt-style views to highlight breaches.
Trust & governance benefit
Data moved from a one-off consultancy extract to a trusted, operational monitoring tool. Managers could act on it daily without relying on analysts to run reports manually.
Outcomes
Tech used
Rural vs Urban Classification
Geospatial AWS analysis supporting regulatory funding evidence
UK regulated utility - Econometrics team
The problem
The Econometrics team needed evidence to justify additional OFGEM funding based on how much vehicle time was spent in rural versus urban areas. No automated pipeline existed to produce this at scale.
The solution
Reused route-history outputs from Fleet MI and integrated them with GIS coverage data and government urban/rural classifications. Converted geospatial data into Redshift-ready format using Python, built Glue workflows and SQL logic to classify locations against polygons, and attributed trips to regions with rural/urban travel time calculated excluding idle periods.
Trust & governance benefit
Delivered analysis that could be independently verified and presented to a regulator. The pipeline was repeatable, documented, and based on a trusted data foundation rather than manual spreadsheet work.
Outcomes
Tech used
Why Reesync AI
Practical delivery, not consulting theatre
Reesync AI is led by James Rees — a senior data and analytics specialist with 13+ years of experience across AWS data engineering, Tableau, operational reporting, automation, and regulated business environments.
The focus is practical delivery: cleaner data, better reporting, trusted dashboards, and automation that removes repeated manual effort. Not transformation programmes. Not strategy decks. Work that gets done.
Hands-on delivery
I build the thing. Actual pipelines, cleaned data, working dashboards, and automation that runs in production - not a slide deck with recommendations you have to implement yourself.
Regulated environment experience
13+ years working in environments where governance, GDPR, audit trails, and data accuracy are non-negotiable. I understand what production-grade actually means when the stakes are high.
End-to-end across the full stack
Most contractors specialise in one layer. I span AWS infrastructure, Python ETL, SQL transforms, and Tableau delivery. Fewer handoffs, faster delivery, one point of accountability.
Award-winning delivery
The Fatigue Manager project won Automation Project of the Year at the UK IT Industry Awards 2021 - evidence of the quality standard I aim for on every engagement.
Stakeholder-fluent
I can present findings to a board and write a Glue job in the same week. Plain-English communication with non-technical stakeholders is part of the job, not an afterthought.
Pragmatic, not over-engineered
I build what the problem actually needs. No unnecessary complexity, no gold-plating. The right tool, delivered on time.
Get in touch
Have a report, spreadsheet, dashboard, or data process that still takes too much manual effort?
Send a short message about the reporting process, spreadsheet, dashboard, or data quality issue you want to improve. I'll tell you whether it looks like a good fit for a Reporting & Data Automation Sprint.
Available for contract and consulting engagements across the UK. Remote-first, UK business hours.
Request a reporting automation review