AI-Powered Web Development
Custom dashboards that show what matters, without the spreadsheet
We connect the systems you already use and build dashboards and scheduled reports your team trusts, so nobody spends Monday morning copying numbers.
The problem
The problem this solves
Most reporting is still done by hand. Someone exports data from three systems, pastes it into a spreadsheet, fixes the formats and builds the charts. It takes hours every week, and by the time the report is ready, the numbers are already old.
Manual reports also drift. Two people calculate the same figure differently, a formula breaks when a column moves, and meetings turn into arguments about whose number is right. Managers stop trusting the report and start asking for their own.
A good dashboard fixes the source, not just the display. It pulls data directly from your systems on a schedule, applies one agreed definition for every metric, and shows it in a way that answers real questions: What is behind schedule? Where are we losing money? What changed this week? It pairs well with automated reporting, which sends the right summary to the right people without anyone lifting a finger.
Dashboards are only useful if people act on them. So we design each one around a few decisions it should support, show the numbers that drive those decisions first, and leave the long tail of detail one click away.
What you get
What you get
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Agreed metric definitions
A short, written definition for every number on the dashboard, signed off by the people who use it.
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Data connections
Scheduled or live connections to your databases, CRM, accounting, ads and operations tools.
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A clean reporting layer
Data combined and cleaned in one place, so every chart uses the same numbers.
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Dashboards for each role
Executives, managers and teams each get views that answer their own questions.
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Filters and drill-downs
From the total down to the individual record in a few clicks.
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Scheduled reports
PDF or email summaries sent daily, weekly or monthly to the right people.
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Plain-language summaries
Optional AI-written notes that explain what changed and why, with structured output checked against the data.
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Alerts
Notifications when a number crosses a threshold, so problems surface before the weekly meeting.
How we build it
How we build it
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1
List the decisions
We start with the decisions the dashboard should support and the questions people ask today.
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2
Audit the data
We check where each number comes from, how reliable it is and what is missing.
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3
Design the views
Mockups with real sample data, reviewed with the people who will use them.
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4
Build pipelines and dashboards
Data connections, the reporting layer and the dashboards, tested against your current reports.
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5
Reconcile and launch
We compare new numbers with trusted old ones until they match, then switch over.
AI and people
Where AI helps, where people decide
AI makes the repetitive parts faster. The decisions that shape your product stay with experienced people.
Where AI speeds things up
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Profiling source data to find gaps, duplicates and odd values.
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Drafting SQL queries and data transforms for each metric.
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Generating chart layouts and dashboard components.
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Writing short summaries of what changed since the last report.
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Producing tests that check totals against the source systems.
Where people decide
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What each metric means and how it is calculated.
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Which numbers belong on the main screen.
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Whether a data source is reliable enough to trust.
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What an AI-written summary may and may not claim.
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When the new numbers match reality well enough to replace the old report.
Is this right for you?
When this is the right choice
A good fit when
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Someone spends hours each week building the same report by hand.
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Different teams quote different numbers for the same metric.
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You need data from several systems in one view.
Consider something else when
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All your data lives in one tool that already has good built-in reports.
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Nobody has agreed what the key numbers are. Start with the definitions.
Timeline and cost
What affects the timeline and cost
A first dashboard from two or three clean data sources can be ready in a few weeks. Messy data, many sources or strict reconciliation with finance add time. We usually ship one high-value view first and add more once it is trusted.
We do not publish fixed prices because scope drives cost. How we estimate.
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Number of data sources
Each system has its own API, limits and data quirks to handle.
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Data quality
Inconsistent or duplicated data needs cleanup rules before it can be reported.
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Refresh frequency
Live data costs more to build and run than an hourly or daily refresh.
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Audiences and permissions
Different views and row-level access for different teams add work.
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Metric complexity
Simple counts are quick; forecasts, allocations and blended metrics take longer.
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AI summaries
Written insights need prompt design, guardrails and checks against the numbers.
Keep exploring
Related services, solutions and reading
Related services
View all related services- AI-Powered Web Development Business websites, customer portals, dashboards and web applications that load fast, rank well and are easy to change.
- Web applications Custom browser-based software for the work your team does every day, from booking tools to operations systems.
- Ecommerce websites Online stores with clean product catalogs, simple checkout and the integrations that keep orders moving.
- Internal tools Custom software for your own team: trackers, approval flows and back-office systems that replace spreadsheets.
- Database design Data models that stay fast and correct as your business grows, with backups and access rules in place.
- Multi-tenant architecture One product serving many customer accounts, with each account's data kept separate and secure.
Solutions
View all solutions- Automated reporting Reports that build themselves from your systems on schedule, with a plain-language summary of what changed.
- Forecasting dashboards Forecast demand from your sales history and seasonality, and flag items to reorder before they run out.
- Feedback analysis Read every review, survey and ticket, group them by theme and sentiment, and show what customers keep asking for.
- AI automation and integrations Connect the tools you already use so data moves once, correctly, and AI reads the parts that arrive as text or documents.
- Invoice processing Read supplier invoices, match them to orders and push approved ones into accounting, with exceptions flagged for review.
- Automated quoting Draft accurate quotes and proposals from a request, your price rules and past work, for a person to approve.
Industries
View all industries- Retail Stock forecasting, sales dashboards, feedback analysis and store tools for retailers with shops and online sales.
- Manufacturing Quality inspection, production dashboards, quoting tools, knowledge assistants and legacy system modernization for manufacturers.
- Finance and accounting Client portals, document collection, invoice and receipt processing, and reporting for accounting firms and finance teams.
- Construction and facilities Job tracking, maintenance requests, inspections, quotes and proof-of-work photos for builders and facility teams.
- Marketing agencies Landing pages, content workflows, client reporting and white-label development capacity for marketing agencies.
- Property management Tenant portals, maintenance tracking, owner statements and inbox automation for property managers.
Case studies
View all case studies- Facility management platform Every request, inspection and cost for every building now lives in one place, with a clear owner and status.
- Vacation rental operations system One calendar per property, cleaning tasks created from bookings, and owner statements built from the same records.
- Payroll system for an IT services company Payroll is calculated from recorded inputs, reviewed once and sent as payslips in a single batch, with every change logged.
- Tax filing service platform Clients upload documents and see where their case stands, and staff work from one queue with deadlines and reminders.
- Beach rental booking system A long-running booking system kept earning while it was extended, tested and given a safe, gradual path to a modern stack.
Guides and articles
View all guides and articles- How to automate reporting with AI and MCP Build reports that assemble themselves from your systems through MCP, with a written summary people can trust.
- How to set up automated reporting Replace manual weekly and monthly reports with dashboards and summaries that update themselves.
- How to connect AI to your database safely Let staff ask questions of your data in plain English without risking production systems or sensitive records.
- PostgreSQL vs MySQL The two most popular open-source databases compared for business applications.
AI models
View all ai models- Reasoning Models that think through multi-step problems before answering: analysis, planning, math, complex documents and agent work.
- Claude Sonnet 5 Anthropic's balance of speed and intelligence: a strong everyday model for assistants, document work, tool calling and coding.
- GPT-6 Sol The middle model of the GPT-6 family, positioned for complex coding and agentic workflows at a mid price level.
- GPT-6 Luna OpenAI's most efficient model for focused, high-volume tasks, with vision, tool calling and structured outputs at the lowest price level in the family.
- GPT-4o mini A compact, low-cost older OpenAI model for focused tasks, with vision, tool calling and structured outputs and a 128K token window.
- text-embedding-3-small OpenAI's efficient, lowest-cost embedding model, with 1,536-dimension vectors for search, retrieval and similarity at scale.
MCP servers
View all mcp servers- Databases Servers that let AI query and, where allowed, change data in SQL, NoSQL, analytics and vector databases.
- Google Analytics Google Analytics' official MCP server lets AI run GA4 reports, funnels and realtime reports and read property settings. Read-only.
- MySQL A community MCP server that lets AI run SQL against MySQL, read-only by default, with insert, update and delete each switched on separately.
- Azure Microsoft's Azure MCP server lets AI work with many Azure services, from storage and Key Vault to SQL, monitoring and AKS, with a read-only mode.
- BigQuery Google's remote BigQuery MCP server lets AI list datasets and tables and run SQL, with a read-only query tool and IAM controls.
- Chroma Chroma's official MCP server lets AI create collections, add, query, update and delete documents in a Chroma vector database, local or cloud.
Glossary terms
View all glossary terms- Database A database is an organized store of data that software can search, update and keep consistent, such as customers, orders or bookings.
- API integration An API integration connects two or more systems through their APIs so data and actions flow between them automatically.
- Structured output Structured output is when an AI model returns its answer in a fixed format, such as JSON matching a schema, so software can use it reliably.
- Machine learning Machine learning is a way of building software that learns patterns from data to make predictions or decisions, instead of following only hand-written rules.
- API An API, or application programming interface, is a defined way for one piece of software to request data or actions from another.
- Backend The backend is the part of an application that runs on servers, storing data, applying business rules and serving the frontend through APIs.
FAQ
Questions about dashboards and reporting
Have a question that is not here? Ask us directly.
Often you should, and we can build on them. A custom dashboard makes sense when you need it inside your own product, want customers to see their own data, need logic those tools make awkward, or want to avoid per-user license costs.
We define every metric in writing, test calculations against the source systems, and reconcile the new dashboard with a report you already trust before switching over. Differences are explained, not hidden.
Yes. We connect each system through its API or database and combine the data in one reporting layer. See API integration and database design.
Yes, with care. We give the AI the exact figures and ask it to describe changes in plain language, then check its statements against the data before they are shown. It explains; it does not invent. Read our guide to automated reporting.