Anyone can put a chart on a screen. The hard part is the layer underneath: pulling from your CRM, your store, your database, and your ad platforms; reconciling records that don't agree; and keeping it all current without somebody exporting a CSV every Monday. We build that layer, then the dashboard on top of it.
Runs on your infrastructure · You own the code and the pipeline
Most dashboard projects only build the top layer. That's why they stop being trusted.
Trusted by teams in the US, UK, and India
Siddhraj
Unoloft
Kofeko
3nStar
Veda
Cerata
Shubham
Consultup IndiaSiddhrajUnoloftKofeko3nStarVedaCerataShubhamConsultup IndiaYour revenue number lives in the accounting system. Your pipeline lives in the CRM. Your delivery status lives in a project tool, your ad spend lives in three ad accounts, and the number your leadership team actually looks at lives in a spreadsheet somebody rebuilds every Monday morning from all of the above.
So every meeting starts with a discussion about whose figure is right. Someone rebuilds the export. A decision waits three days for a number that already existed in four places. And the report is always describing last week, because it takes until Wednesday to assemble.
The built-in reporting in each of those tools can only ever show you its own slice. That's not a flaw in the tools, it's the definition of them. The view you actually need is the one that spans all of them, and no vendor is incentivised to build it for you.
A dashboard is only worth building when someone would make a different decision because of it. If nobody would act differently, you don't need a dashboard, you need a weekly email.
Different jobs, same foundation. What changes is who's looking and what they're allowed to see.
Revenue, pipeline, cash position, delivery load, and headcount utilisation on one screen, reconciled across your accounting system, CRM, and project tool. The number everyone argues about becomes one number with a definition attached.
Best for: founders and leadership teams making decisions from a spreadsheet someone rebuilds by hand.
A live, branded dashboard per client, pulling from ad platforms, analytics, and your project tool, replacing the deck your team rebuilds every month. Clients log in whenever they want instead of emailing to ask. You keep the monthly call for the commentary that actually needs a human.
Best for: agencies losing two to three hours per client, per month, to report assembly.
Orders, inventory, exceptions, SLA breaches, and queue depth in real time, with the flagged items surfaced rather than buried. Built so the first screen shows what needs a person, not what happened last quarter.
Best for: e-commerce and logistics teams who find out about problems from customers.
Pipeline by stage, source, and owner, with conversion and cycle-time tracked over real history rather than whatever your CRM's built-in report happens to expose. Forecast against your own definitions, not the vendor's.
Best for: teams whose CRM reporting can't answer the question the sales meeting keeps asking.
Cash flow, ageing, spend by vendor and category, margin by client or product, assembled from your accounting system and the documents feeding it. document and invoice processing, which is where the clean data comes from in the first place.
Best for: finance teams closing the month from a position they can't see until it's over.
Analytics inside your own product or client portal, under your brand, with per-tenant data isolation and role-based access. Built as a product feature, with the security model designed before the charts.
Best for: SaaS and platform businesses where “can we see our own data?” is now a sales objection.
Getting the data into place is often the larger half of a dashboard project, and it's workflow automation wearing a different hat. If your data is already clean and in one system, say so on the call, the project gets meaningfully cheaper and we'll tell you so before you commit.
The chart layer is a week of work. Everything below it is the reason dashboards succeed or get abandoned.
We pull from each source through its API on a schedule you set, with retry logic, rate-limit handling, and alerting when a connection fails. The failure mode of most dashboards is silent: an integration breaks, the chart keeps rendering yesterday's data, and nobody notices for three weeks. Every pipeline we build tells us it broke before it tells you.
Role-based access down to the row where you need it: an account manager sees their clients, a client sees only themselves, leadership sees everything. For embedded dashboards, tenant isolation is designed and tested before a single chart is styled.
The dashboard nobody opens is worth nothing. Thresholds you define push to Slack, Teams, or email when something crosses them, so the dashboard becomes the place you go to investigate rather than the place you were supposed to check.
The chart layer is a week. The data layer is the project. Any quote that doesn't distinguish between the two is a quote that will change later.
This covers what one dashboard needs. Reconciling data across systems into one modelled warehouse, so the fifth and tenth dashboard cost almost nothing, is a bigger foundation with its own page: data warehousing & ETL. Agreeing what the numbers in that warehouse mean, so two dashboards never contradict each other, is business intelligence, layered on top of it.
Before anything technical, we agree what the dashboard has to answer and what each metric means. Half of this session is usually the first time two departments discover they define the same word differently. You get the written definition set whether or not you hire us.
We connect to each source and check what's actually available through its API, how clean it is, and how far back the history goes. Only then do you get a written scope, a fixed price, and a date. If a source genuinely can't provide what the dashboard needs, you find out here, before you've committed anything.
Connectors, sync scheduling, reconciliation rules, and the modelled layer. You get a written update every Friday plus a short Loom walkthrough of what moved. This is the phase where a project is won or lost and it's the phase most vendors compress in the quote.
Charts, filters, drill-downs, roles, and alerting, built against your real data rather than sample data. You review it against numbers you already know are correct, that reconciliation against a source you trust is a scheduled step, not a favour.
Deployed with pipeline monitoring from day one. Then a monthly plan: watching the syncs, adding metrics as the questions change, and adjusting when a source system changes its API, which they do, without asking you first.
We don't ask you to migrate anything. The dashboard reads from where your data already lives.
HubSpot · Salesforce · Pipedrive · Zoho · Shopify · WooCommerce · Stripe · QuickBooks · Xero · NetSuite · Google Analytics · Google Ads · Meta Ads · LinkedIn Ads · Postgres · MySQL · MongoDB · Airtable · Google Sheets · Notion · Asana · ClickUp · Jira
Not listed? If it has an API, we can almost certainly read from it. If it doesn't, and some older systems genuinely don't, we'll tell you before you commit and propose a file-based or database-level import instead of promising an integration that doesn't exist.
An off-the-shelf BI tool is the right answer more often than any agency page will admit. Here's the honest version of the decision.
Your data already lives in one or two systems that the tool connects to natively, your metrics match how those systems already define them, a daily refresh is fast enough, and the audience is your own team rather than your customers. Looker Studio, Metabase, or Power BI will be live in days and cost a fraction of a custom build. We'll tell you this on the call and we won't quote you for something you don't need.
Your data spans four or more systems that disagree with each other, your metrics need definitions the source tools can't express, you need history a source system overwrites, the dashboard has to be embedded in your own product under your brand, you need row-level access rules that match your org chart, or per-seat licensing has stopped making sense at your headcount. You also own it outright, which matters once this becomes something the business runs on.
| Off-the-shelf BI tool | Custom build | |
|---|---|---|
| Time to first version | Days | Weeks, scoped after the source audit |
| Upfront cost | Low | Higher, fixed and quoted in writing |
| Ongoing cost | Per-seat licence, grows with headcount | Hosting plus a monthly plan |
| Data across many systems | Possible, usually fragile | Designed for it |
| Custom metric definitions | Constrained by the tool | Whatever your business actually means |
| Historical snapshots | Only what sources retain | Kept from day one |
| Embedding in your product | Limited, and licensed accordingly | Built as a product feature |
| Row-level access control | Tool-dependent | Modelled to your org |
| Ownership | You rent it | Code, pipeline, and model are yours |
Honest answer: a good number of clients should start with Metabase or Looker Studio rather than a full custom build, live in days and a fraction of the cost. The pipeline underneath is the part that has to be right, whichever chart layer sits on top of it. If that pipeline needs to serve several dashboards from one shared, modelled foundation rather than one dashboard on its own, that's a bigger piece of work with its own page: business intelligence.
You need to see the state of the business, from data that currently lives in five places.
You need to answer questions you haven't thought of yet, repeatedly, not just the ones this dashboard was built for. The warehouse and metric layer underneath several dashboards, not one screen.
You need a place to actually do the work, enter it, track it, approve it, not just read it out.
You need something to happen when the data changes: a record updated, a person notified, a report sent.
You need a system people enter and manage data inside, not just read from.
The dashboard reads out of it, it doesn't replace it. If nothing's connecting your ERP to the rest of the business yet, that's the prior step.
If the actual problem is the stock number itself, sync and accuracy, not visibility, fix that first, then read it out here.
The next step depends on judgement rather than a rule, not a fixed report.
The engineering underneath all of these. If you're evaluating who can build custom software at all, not just this shape of it.
People outside your organisation need to look something up on their own schedule, not just see a report you send them.
Honest answer: roughly half the dashboard enquiries we take turn out to be workflow-automation projects with a reporting screen at the end. That's a cheaper project and a better outcome, and we'd rather say so in week one.
And if the real request is "let me ask questions about our own data in plain language," that's closer to a RAG chatbot than a dashboard, and it's often a smaller build.
The pipeline fails silently. The charts keep rendering the last successful sync. Three weeks later somebody notices a number hasn't moved, and from that day nobody trusts the dashboard again, including for the metrics that were still correct. This is why monitoring is scoped in from day one here rather than sold at handover.
Forty widgets, no hierarchy, and the screen that was meant to answer “are we okay?” now takes ten minutes to read. We start from the decisions the dashboard has to support and refuse charts that don't support one. Fewer, better-placed numbers beat completeness every time.
Two departments define “active client” differently, so the dashboard shows a number neither of them accepts, and both go back to their own spreadsheet. Getting the definitions written down first isn't bureaucracy, it's the whole difference between a dashboard people use and one people argue about.
If a field is only filled in when someone remembers, a dashboard doesn't fix that; it publicises it. Sometimes the honest answer is to fix the process feeding the system first and build a smaller dashboard afterwards. We'll say so.
Fixed price, quoted in writing before we start. No hourly billing, no surprise change orders. If scope moves, we re-quote in writing first.
One dashboard: definitions, pipeline, model, build, and launch, plus the first month of monitoring
Scoped and quoted after the source audit
Good for: Proving the data layer works before committing further
The full pipeline plus multiple role-based dashboards reading from one modelled source of truth
Priced once we know how many dashboards and sources
Good for: Most teams with data across four or more systems
Pipeline monitoring, source-change fixes, new metrics and views, plus a set amount of new build work each month
A monthly plan sized to your pipeline
Good for: Teams where the reporting questions keep evolving, most clients end up here
Every build includes monitoring for the first month. After that it's a monthly fee, see what that covers, and you can stop any time, you keep the code, the pipeline, the data model, and the documentation either way.
Book a 30-minute call. We'll map where that number actually lives, tell you honestly whether an off-the-shelf tool would do the job, and give you a fixed price if a custom build is genuinely the right answer.
