Agree what every number means
Each KPI gets one definition, one source and one owner, so revenue or margin reads the same on every dashboard and in every meeting.
Discuss it with us[00] Business Intelligence
Business Intelligence Development Company for Dashboards and Data Warehouses
We build business intelligence your managers trust. Data from your ERP, CRM, accounts, spreadsheets and apps is brought into one warehouse, every KPI is defined once, and dashboards and reports refresh on a schedule instead of being rebuilt by hand every Monday.
Case study85% less manual work for finance, e-commerce and enterprise operations teams
built for
- Leadership and finance teams
- Operations heads with data spread across systems
- Retail, e-commerce and distribution businesses
- Healthcare, education and service providers
- Companies outgrowing spreadsheet reporting
Illustration: an example dashboard with 4 numbers (net revenue $1.24M, gross margin 31.8%, stock cover 23 days, pipeline $3.9M). Selecting a number traces it back through the data model: net revenue is defined as “Invoiced sales less returns and discounts, counted on the invoice date.”, owned by finance, built fromerp.invoices · erp.returns, then stg_invoice_lines, then fct_sales, then net_revenue, and checked on every refresh: matches the ledger within 0.1%; no duplicate invoices; loaded at 06:00, on time. All values are demo values.
dash.weekly
trace · net revenueowner finance
Invoiced sales less returns and discounts, counted on the invoice date.
sum(invoice_lines.amount) − returns − discounts
- srcerp.invoices · erp.returnsread-only account
- stgstg_invoice_linesduplicates removed
- fctfct_salesone row per invoice line
- msrnet_revenuesigned off by finance
- matches the ledger within 0.1%
- no duplicate invoices
- loaded at 06:00, on time
trace · gross marginowner finance
Net revenue less the cost of the goods sold, as a share of net revenue.
(net_revenue − cogs) / net_revenue
- srcerp.invoices · acct.ledgercost from the ledger
- stgstg_cost_linesmapped to products
- fctfct_salescost joined per line
- msrgross_marginsigned off by finance
- every line has a cost
- ledger cost matches to 0.1%
- alert: below the 33% target
trace · stock coverowner operations
Days the stock on hand would last at the average daily sales of the last four weeks.
stock_on_hand / avg_daily_units(28d)
- srcwms.stock · erp.invoicessnapshot at 05:00
- stgstg_stock_snapshotone row per item and site
- fctfct_inventorystock and sales by day
- msrstock_cover_daysagreed with operations
- all 7 warehouses reported
- no negative stock
- alert: 5 days above target
trace · pipelineowner sales
Value of open deals that have reached the qualified stage, in the deal’s own close quarter.
sum(deals.value) where stage ≥ qualified and open
- srccrm.deals · crm.stagesread-only API
- stgstg_dealscurrencies converted
- fctfct_pipelinedaily deal snapshot
- msrqualified_pipelineagreed with sales
- every deal has an owner
- no deals past close date
- loaded at 06:00, on time
key facts
- Power BI, Tableau, Looker, Metabase and Superset, or custom
- 5 BI tools
- warehouse and pipelines run in your own account
- Your cloud
- new figures checked against your current reports before switch-over
- Reconciled
- or a dedicated team, quoted after scoping
- Fixed scope
What we deliver
What you get with Business Intelligence. Nine parts of the service, each scoped with you.
Good BI starts with agreeing what each number means. Before we build, we sit with finance, sales and operations to define the KPIs, where each one comes from and who needs to see it. A prototype dashboard comes early, so you can change direction before the pipelines are built.
We work in Power BI, Tableau, Looker and open-source tools such as Metabase and Apache Superset, or embed custom dashboards in the web and mobile apps you already have. The warehouse runs in your cloud account, with access controlled by role so each person sees only the data they should.
- t.01
KPI and reporting design
We agree the metrics, their exact definitions and their sources with each department, so a figure means the same thing on every dashboard.
- t.02
Self-service models
Clean, documented semantic models that let analysts build their own reports without writing SQL or breaking the agreed definitions.
One warehouse, fed on a schedule
Pipelines pull from the systems you already run into a warehouse in your own cloud, cleaned, modelled and kept up to date.
Discuss it with us- t.03
Data warehouse design
A central warehouse on Snowflake, BigQuery, Amazon Redshift, Azure Synapse or PostgreSQL, modelled for reporting rather than copied table by table.
- t.04
Data integration and pipelines
Scheduled pipelines pull data from ERPs, CRMs, accounting systems, spreadsheets and APIs, then clean, de-duplicate and load it into the warehouse.
- t.05
Cloud BI
Container-based BI infrastructure on AWS, Azure or Google Cloud that scales with your data and users, with costs you can see per service.
Reports people open every day
Dashboards and scheduled reports for each role, in the BI tool you license or built into your own web and mobile apps.
Discuss it with us- t.06
Dashboards and reports
Executive, sales, finance and operations dashboards with tables, charts, gauges and maps, plus scheduled reports sent by email.
- t.07
Custom BI applications
When an off-the-shelf tool does not fit, we embed dashboards in the web, desktop or mobile app you already have, with the same warehouse behind them.
Retire the old reports, then add AI
Move off spreadsheets or an older BI tool with figures reconciled, then add forecasts, alerts and plain-English questions on the same model.
Discuss it with us- t.08
BI implementation and migration
Data preparation, architecture fixes and migration from Excel reports or an older BI tool, with old and new figures reconciled before switch-over.
- t.09
AI on top of BI
Forecasts, anomaly alerts and natural-language questions answered from your warehouse, with every answer linked back to the query behind it.
which approach fits
Pick the tool after the questions.
The tool matters less than the definitions under it. We recommend one based on what you already license, who reads the dashboards and where your data lives, and we build on licences you already pay for.
Run on Microsoft 365 and Azure
what you have now: Excel reports, Teams, Dynamics or SQL Server
we usually start with: Power BI on one shared semantic model, so every report uses the same measures
Have analysts who want to explore freely
what you have now: Many sources and people who like to dig into the detail
we usually start with: Tableau, with certified data sources for the agreed KPIs
Run on Google Cloud
what you have now: BigQuery, Google Workspace and Google Analytics
we usually start with: Looker, with each metric defined once in its modelling layer
Want no per-user licence fees
what you have now: Many viewers, a tight budget and a technical team in-house
we usually start with: Metabase or Apache Superset, hosted in your own cloud
Need dashboards inside your own product
what you have now: A portal or app your customers or partners sign in to
we usually start with: Embedded or custom dashboards, with row-level access for each customer
Want to know why a number moved, and what comes next
what you have now: History in a warehouse and questions dashboards cannot answer
we usually start with: Analysis and forecasting models on the same warehouse
Data analytics & insightsHave one system and a handful of reports
what you have now: One source and a few people reading the numbers
we usually start with: Use the reports built into that system for now. A warehouse can wait, and we will tell you so.
What a data warehouse is
connect your data
Connect your data. One warehouse, so every dashboard reads the same numbers.
Your figures already exist, spread across your ERP, CRM, accounts, spreadsheets and apps. Scheduled pipelines pull them into one warehouse, clean and model them, and keep every dashboard and report up to date without rebuilding anything by hand.
Data analytics & insights- erpERPorders · stock · purchasing
- crmCRMleads · deals · accounts
- acctAccountingledger · invoices · payments
- shopE-commerceorders · returns · products
- xlsSpreadsheetsbudgets · targets · trackers
- dbDatabasesSQL · app tables · logs
- webWeb analyticstraffic · funnels · events
- mktMarketingcampaigns · spend · leads
- apiCloud appsSaaS tools · APIs
extract: ERP, CRM, Accounting, E-commerce, Spreadsheets, Databases, Web analytics, Marketing, Cloud apps → data warehouse (cleaned · modelled · refreshed) → dashboards · reports · alerts
- dw.01 · connect
Built on the systems you run
Pipelines pull from the ERP, CRM, accounting tools and spreadsheets your teams already use, on a schedule you agree.
- dw.02 · define
One definition per number
Each KPI is defined once, with finance, sales and operations, so revenue or margin means the same on every dashboard.
- dw.03 · access
Your cloud, your access rules
The warehouse runs in your cloud account, with access by role so each person sees only the data they should.
Why it matters
What changes once it is live. Each outcome is measured, not promised.
Business Intelligence is planned around these outcomes, measured against a baseline we agree with you up front.
Discuss your project- 01
One version of the numbers
Sales, finance and operations read the same agreed figures, so meetings are spent on decisions, not on whose spreadsheet is right.
- 02
Less time building reports
Reports refresh on a schedule, so analysts stop copying and pasting every Monday and start looking at what changed.
- 03
Earlier warning
Dashboards and alerts show a drop in sales, a rise in costs or a stock problem while there is still time to act.
- 04
Access by role
Each manager sees their own branch, region or team, while leadership sees the whole business from the same model.
How we work
How we deliver Business Intelligence
A clear five-step process, so you always know what happens next and what you will get.
Every project starts with a free, no-obligation consultation.
run.logappsinai / project
$ appsinai run --pilot-first
Step 1: Agree the questions and KPIs
We interview the people who will use the dashboards, list the decisions they make each week, and agree each KPI, its definition and the system it comes from.
you get kpi list with definitions · source map · fixed quote
we need time with the people who read the reports · your current reports and spreadsheets
Step 2: Audit the data and design the model
We check each source for gaps and duplicates, design the warehouse and pipelines, and sketch the dashboard layout and navigation.
you get data quality report · warehouse design · dashboard wireframes
we need read-only access to each source · a contact for each system
Step 3: Prototype on your own data
A working dashboard on a sample of your real data lets your team test the layout and the definitions while changes are still cheap.
you get working prototype · agreed definitions · go / no-go
we need feedback from the people who will use it
- gatego / no-go on the results before “Build and reconcile”
Step 4: Build and reconcile
We build the pipelines, warehouse and dashboards, and reconcile the figures against your current reports until finance signs them off.
you get pipelines and warehouse · dashboards and scheduled reports · reconciliation sign-off
we need finance to check figures against current reports
Step 5: Deploy, train and support
Dashboards go live on web and mobile, users are trained by role, and we monitor the pipelines and add reports as your needs grow.
you get training by role · pipeline monitoring and alerts · new reports
we need notice of new systems or changed definitions
↺ results feed the next improvement cycle$
Security & control
Data access your IT team can sign off
A warehouse brings sensitive data together in one place, so access, encryption and auditing are designed in from the first pipeline.
Ask us about security- 01
Row-level and role-based access
Permissions are set by role, branch or region in the BI layer and the warehouse, so a dashboard never shows more than the viewer is allowed to see.
- 02
Read-only source connections
Pipelines use read-only accounts on your source systems, with credentials held in a secrets manager rather than in code.
- 03
Encryption in transit and at rest
Data is encrypted between systems and in the warehouse, using your cloud provider's key management.
- 04
Audit trails and lineage
Every pipeline run is logged and each metric can be traced back to its source tables, which helps with audits and regulatory questions.
where this fits
Dashboards are the last step. They show the numbers the data work is meant to move.
A dashboard is only as good as the data under it. Behind every report we build are the same stages: data collected, labelled where needed and analysed before it reaches a chart.
- stage.01 · collectout → raw rows
Data collection & web scraping
Exports from your own systems, plus public or partner sources gathered into clean, structured rows.
- stage.02 · labelout → labelled rows
Annotation & labelling
People tag text, images and records to an agreed guide, and a second reviewer checks the hard cases.
- stage.03 · analyseout → findings
Data analytics
We clean and profile the data, find what actually predicts the outcome and spot gaps before anything is built on it.
- stage.04 · reportyou are hereout → dashboards
Business intelligence
Dashboards your managers will open, showing the numbers the work is meant to move.
FAQ
Questions about Business Intelligence
Straight answers to what buyers ask us most. Can't find what you need?
Email sales@appsin.ai or call +91-9025555584.
Ask us directlyWhat does a business intelligence development company do?
We connect your data sources, build a warehouse and pipelines that keep it clean and current, and design dashboards and reports on top. The result is a reporting system your team uses every day instead of a one-off analysis.
What is the difference between BI and data analytics?
BI tracks what is happening through agreed KPIs, dashboards and reports that refresh automatically. Data analytics digs into why it happened and what is likely next, using statistics and predictive models. Most businesses need BI first, then analytics on top of the same data.
Which BI tools do you work with?
Power BI, Tableau, Looker, Metabase and Apache Superset, and custom dashboards built into web or mobile apps. If you already have licences, we build on them. If not, we recommend a tool based on your users, data volume, existing Microsoft or Google set-up and budget.
Do we need a data warehouse?
If your reports combine data from more than one or two systems, usually yes. A warehouse keeps history, applies one set of definitions and stops dashboards from slowing down your live systems. For a single source, a well-modelled direct connection can be enough to start.
Can you improve our existing Power BI or Tableau set-up, or move us off Excel?
Yes. We often take over existing reports, fix slow or conflicting models, move logic out of individual reports into a shared model and document what each measure means. When moving off Excel or an older tool, old and new figures are reconciled before anyone switches over.
Can dashboards update in real time?
They can, where the source systems allow it, through streaming or frequent refreshes. Most management reporting works well with hourly or daily refreshes, which cost less to run, so we agree the refresh rate per dashboard.
How long does a BI project take?
Scoping takes one to two weeks and a working prototype on your own data two to three more. The full build depends on the number of sources, how clean they are and how many dashboards you need, and the quote sets it out milestone by milestone.
Can we ask our data questions in plain English?
Yes, once the warehouse and definitions are in place. A language model turns the question into a query against your agreed model and shows the query and the figures behind each answer, so people can check it. It works best on a clean, documented model, which is why we build that first.
ChatGPT integrationHow much does a BI project cost?
It depends on the number of data sources, how clean they are, the number of dashboards and users, and the tool licences. After a scoping call you get a fixed quote or a dedicated team proposal.
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related
Keep exploring
- Data Analytics & InsightsForecasts, customer analysis and predictive models built on the same data as your dashboards.
- Machine Learning DevelopmentModels for forecasting, scoring and anomaly detection, trained on your warehouse data.
- AI readiness checklistCheck whether your data, systems and team are ready for analytics and AI projects.
- AI & ML DevelopmentEnd-to-end AI and machine learning development, from a feasibility check and proof of concept to models running inside your ERP, CRM or apps.
- Generative AI DevelopmentGenerative AI applications that draft, summarise, search and answer from your own content, built on the model that fits and tested before rollout.
- AI Agent DevelopmentAI agents that read, decide and act across your systems, completing multi-step tasks with human approval where it matters and a log of every step.
- AI Chatbot DevelopmentCustomer-facing AI chatbots on your website, WhatsApp and app that answer from your content, complete simple tasks and hand difficult conversations to your team.
- ChatGPT & OpenAI IntegrationOpenAI models built into your apps, SaaS products and internal tools, with retrieval over your data, tested prompts and running costs you can plan for.
- LLM DevelopmentWe select, adapt and deploy large language models for your tasks, from fine-tuned open-source models on your servers to integrations with hosted APIs.
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