Reports assembled by hand
Someone exports from Xero, the CRM and Shopify, pastes it all into a spreadsheet and repairs the formulas — every week, with errors nobody can trace.
Business intelligence and analytics
Business Intelligence and Analytics for Growing UK Businesses
Pull the numbers out of your CRM, finance, eCommerce and operational systems into one reliable reporting source, with dashboards and automated reports built on KPI definitions everyone has agreed.
- One written definition for every KPI
- Every figure traceable to its source system
- Your data stays in accounts you own
Why reporting breaks down
Three spreadsheets, three different answers.
Most businesses do not lack data. They lack one version of it that everyone trusts, that arrives on time and that does not depend on someone exporting files every Monday.
What good BI gives you
Not more charts — fewer, trusted numbers, delivered without anyone assembling them:
- One reporting database fed automatically from each system
- KPIs defined once and used everywhere
- Dashboards for each team, built on the same data
- Scheduled reports that arrive without anyone exporting
- Forecasts and alerts where the data supports them
KPIs that mean different things
Sales counts revenue at order, finance at invoice and operations at dispatch, so every meeting starts by arguing about whose number is right.
Data scattered across systems
Customers, orders, stock and finance sit in separate tools with different IDs, so a simple question like margin by customer takes days to answer.
Answers that arrive too late
Month-end packs land weeks after the month closed, when the chance to act on a slipping margin or a stock problem has already passed.
What we build
From scattered data to reporting you can trust.
We build the plumbing as well as the dashboards. The pipelines, the reporting database and the KPI logic underneath are where reporting projects succeed or fail.
Data pipelines
Automated extraction from CRM, ERP, accounting, eCommerce and marketing platforms through their APIs, with checks that flag missing or late data.
Learn more
Reporting database or warehouse
A single reporting database — PostgreSQL or BigQuery, sized to your volumes — with data cleaned, joined and modelled for analysis.
Learn more
KPI definitions
Workshops to agree what each metric means, how it is calculated and who owns it, written into a data dictionary the whole business can read.
Discuss this
Dashboards
Power BI or Looker Studio dashboards for internal teams, or custom React dashboards when reporting needs to live inside your own product.
See an example
Automated reporting
Board packs, weekly trading reports and client reports generated on a schedule and sent out, with every figure traceable to its source.
Learn more
Forecasting and anomaly alerts
Demand, cash and churn forecasts using machine learning where there is enough history to support it — and simpler methods where there is not.
Learn more
How we work
How a BI project comes together.
Reporting projects fail on definitions and data quality, not chart design. So those come first, and dashboards follow once the numbers underneath them can be trusted.
- 01
Discovery
We list the decisions you need to make and the questions behind them, then audit the source systems: what data exists, how clean it is and how to get at it.
Question list and data source audit
- 02
Strategy
We agree the first reporting slice — usually one team or one set of KPIs — plus the tools, where the data will live and what it will cost to run.
First slice, tools and running costs
- 03
UX & architecture
KPI definitions agreed with each owner, a data model for the reporting database, and dashboard wireframes tested with the people who will use them.
Data dictionary and dashboard wireframes
- 04
Development
Pipelines, models and dashboards built in two-week sprints and demoed on your real data from the first sprint, so gaps show up early.
Working dashboards on live data
- 05
Testing
Every figure reconciled against the source system and your existing reports; differences explained, then fixed or documented.
A reconciliation sign-off
- 06
Launch
Dashboards rolled out team by team with a short walkthrough, scheduled reports switched on and data-freshness alerts in place.
Reporting in daily use
- 07
Optimisation & support
New sources and metrics added as your questions change, pipelines kept working when source systems update, and forecasts retrained as data grows.
Reporting that keeps up with the business
Technology
The data stack we build with.
Chosen around your team, budget and data volumes. A clean PostgreSQL database with Looker Studio can be the right answer; so can BigQuery with Power BI.
Sources we connect
Xero, Sage and QuickBooksCRMs and helpdesksShopify, WooCommerce and MagentoERP and stock systems
Pipelines
Python and Node.jsScheduled and event-driven loadsIncremental syncsData quality checks
Storage
PostgreSQLBigQueryMySQLModelled reporting tables
Dashboards
Power BILooker StudioCustom React dashboardsAnalytics embedded in your product
Forecasting
PythonTensorFlow and PyTorchTime-series forecastingAnomaly detection
Governance
Row-level accessPersonal data kept to a minimumData dictionaryAccess audit
If your team already works in Microsoft 365, Power BI is often a natural fit. If a spreadsheet connected to a clean reporting database is all you need, we will say so.
Use cases
Reporting UK teams rely on every week.
Specific reports and dashboards, each replacing a manual job someone used to own.
Trading dashboard
Sales, margin, returns and ad spend by channel in one view, combining Shopify or WooCommerce, marketplace and Xero data, refreshed daily.
Management accounts pack
Monthly P&L, cash and debtor reports generated from the accounting system and CRM, ready for the board without a week of spreadsheet work.
Revenue and churn metrics
MRR, churn, expansion and cohort retention calculated consistently from billing data, instead of from a founder’s spreadsheet.
Operational performance
On-time dispatch, picking errors, courier costs and stock cover tracked per site, with alerts when a measure drifts out of range.
Utilisation and profitability
Time recording, billing and CRM data joined to show utilisation, recovery and profitability by client, team and matter.
Customer-facing analytics
Dashboards embedded in your own product, so each customer sees their own usage and results — and nobody else’s.
Relevant work
Products we have designed and built.
A selection of client projects related to this work. Each case study covers the brief, the approach and the stack.
All case studiesFinance · Analytics
Modernize
Revenue analytics dashboard with multi-source data and automated reporting.
ReactNode.jsPostgreSQLD3.js
Read the case studyIoT · Smart Home
Connect Home
Smart-home dashboard for device control, schedules and energy use.
ReactNode.jsMQTTAWS IoT
Read the case studyFintech · Payments
e-Wallet
Multi-bank digital wallet with transfers, analytics and virtual cards.
React NativeNode.jsStripePostgreSQL
Read the case studyWhy Techsleight
What working with us is actually like.
No inflated numbers — just how we run projects, and what you can hold us to.
Product engineering, not ticket-taking
We ask what the software is for before we estimate it — and we will tell you when something should not be built, or should be bought instead.
AI where it earns its place
LLM features, retrieval and automation built with evaluation, guardrails and cost controls, and plain software where that is the better answer.
Full-stack under one roof
Design, frontend, backend, mobile, cloud and QA in one team, so nothing falls between suppliers.
UK-focused delivery
UK business hours, estimates in pounds, and a contract with a UK company. Our engineers are based in the UK and India.
Flexible engagement
A fixed-scope project, dedicated developers or a monthly retainer — and you can move between them as the work changes.
You own everything
Code, IP, cloud accounts and documentation are yours from day one. We sign an NDA before discovery if you need one.
Support after launch
We stay on for fixes, upgrades and new features, or hand over cleanly to your in-house team with the documentation to match.
FAQs
Questions we get asked.
Straight answers on scope, cost, timelines and how we work. If yours is not here, ask us directly.
How much does a business intelligence project cost?
It depends mostly on the number of source systems and how clean their data is. A discovery sprint, from £2,000, audits your sources and defines the first reporting slice, which is then quoted as a fixed price. Tool licences and cloud costs are separate, and we estimate them up front.
Should we use Power BI, Looker Studio or custom dashboards?
Power BI suits teams already working in Microsoft 365 who need rich internal reporting. Looker Studio is a low-cost option that works well with Google data sources. Custom React dashboards make sense when analytics are part of your own product. We recommend based on your users and budget.
Do we need a data warehouse?
Not always. For many SMEs, a well-modelled PostgreSQL reporting database is enough and cheaper to run. A warehouse such as BigQuery makes sense when volumes are large, sources are many or queries are heavy. We size it to your data rather than to a trend.
Which systems can you pull data from?
Anything with an API, a database connection or a reliable export: Xero, Sage, QuickBooks, CRMs, Shopify, WooCommerce, Magento, marketing platforms, ERP and stock systems, and your own applications. Where a system has no API, we explain the options and their limits.
How do you make sure the numbers are right?
We reconcile every figure against the source system and your existing reports before launch, document each KPI definition, and add automated checks that flag missing, late or unusual data. When a number differs from the old spreadsheet, we find out why.
Can you add forecasting or machine learning?
Where the data supports it. Demand, cash-flow and churn forecasts need enough clean history to be useful. We test forecasts against past periods before anyone relies on them, and sometimes a simple trend is the honest answer. No forecast is presented as more certain than it is.
How soon will we see a first dashboard?
After discovery, the first reporting slice is usually planned over weeks rather than months, and you see dashboards on your real data from the early sprints. Further sources and teams are then added slice by slice.
Where is our data stored, and who can see it?
In your own cloud account or BI tenant, in a UK region where you need it. Access is role-based, down to row level where needed — so a regional manager sees only their region — and personal data is kept out of reporting unless there is a reason for it.
Can you work with our existing spreadsheets?
Yes. We usually start with the spreadsheets people already trust, because they hold the business logic. We move that logic into the reporting database, reconcile the results, and keep the spreadsheet as a comparison until everyone is confident.
Who maintains the reporting after launch?
You can keep us on a support retainer to maintain pipelines when source systems change and to add new metrics, or we hand over to your team with documentation, the data dictionary and a walkthrough of how everything fits together.
Start a project
Which number do you wish you could trust?
Tell us which reports take too long, disagree with each other or do not exist yet. We will suggest a first reporting slice and what it would take to build.
What happens next
- 1A senior engineer reads your brief within one working day, and replies with questions or a first view.
- 2A 30-minute call to understand the goal, constraints and what good looks like — no sales script.
- 3A written proposal with scope, milestones, team and a GBP estimate you can take to your board.
Techsleight Labs is a trading name of Krapton IT Consultancy.
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