Your own Business Intelligence tool: embedded in your product, multi-tenant for your customers, or fully AI-driven with natural-language query. We build custom BI when Tableau, Power BI or Looker doesn't quite fit the way your data and users work.
Tableau, Power BI and Looker are market leaders for good reason. For the average data analyst running ad hoc queries on a warehouse, there is little they don't do well. The calculation and pivot layer is mature, the visualisation library is deep, and the integrations with Snowflake, BigQuery or Redshift work out of the box. For 80% of the "we want to see our data" requests, a standard BI tool is simply the best choice, and building your own would waste both money and attention.
We build custom BI for the other 20%. These are usually organisations where the BI product is not for their own analyst, but for an end user outside the organisation: SaaS companies delivering embedded analytics to their customers, multi-tenant platforms that want a separate dashboard environment per customer, or sector-specific workflows (healthcare with EHR data, financial services with DORA compliance) that the standard product does not cover. We have been working on custom software projects since 2015 and have delivered analytical dashboards for SaaS platforms, financial institutions and healthcare organisations that wanted their data product to be part of their own brand.
The question we start with is almost always the same: who is looking at this dashboard, and what are they actually going to do based on what they see? A Tableau report for your own analyst is not an embedded analytics product for 200 customers. The UX requirements differ by orders of magnitude. End users expect an interface that fits the rest of your product, with logins, branding, performance and mobile support that enterprise BI tools don't deliver by default. Only once that foundation is right do we design the architecture beneath it.
Our BI projects start small. Within a few sprints, the first tier is in place: a working dashboard on your data sources, with the first few charts and filters your team will actually use. From there, we build towards the full feature set in two-weekly sprints: additional data layers, drill-downs, alerting, exports and, if it fits your business case, an AI layer that turns natural-language questions into SQL and charts. That last part is no longer a demo feature. LLMs are now reliable enough to run text-to-SQL on top of a semantic layer in production, provided you build in the right guardrails.
SaaS companies with embedded analytics: your customers expect a reporting layer within your product, not a click-through to Tableau Public or a PDF export. Embedded BI in your own look and feel extends your product well. It lengthens session times, reduces support questions ("where can I see my revenue?") and becomes a selling point at renewal. Looker Embedded, Sisense, Cumul.io and ThoughtSpot Embedded are useful alternatives, but their licensing models quickly become expensive as your customer base grows, and they remain recognisably third-party. Beyond a certain scale, an in-house analytics layer is simpler to run, cheaper, and recognisably part of your brand.
Mid-market organisations with BI needs that have outgrown Excel: your data volume or complexity has grown beyond what spreadsheets can handle. Tableau or Power BI licences for the whole organisation are on the pricey side, or you need a specific workflow that the generic BI tool does not support out of the box. A custom dashboard layer on top of your existing data sources gives your operations and management teams exactly the insights they need, without having to train the entire department on Tableau Desktop.
Sector-specific organisations — healthcare organisations with EHR data and NEN 7510 requirements, financial institutions with DORA and MiFID reporting, energy or utility companies with IoT meter data. The compliance requirements alone are often a reason against an off-the-shelf tool. We build a custom BI tool directly around your compliance framework: row-level security, audit logging, end-to-end encryption of patient or client data, and a data flow that fits your DPIA.
Multi-tenant platforms: a marketplace, a partner platform, a franchise chain or a holding with multiple business units. Each tenant has its own data environment, branding and permissions, but a shared codebase. Looker and some enterprise tools can handle multi-tenancy, but their licensing models are cumbersome. Once the number of tenants runs into the dozens, a custom multi-tenant BI layer is almost always simpler to maintain.
AI-augmented BI: organisations that want their users to be able to "talk" to the data. "Show me Q3 revenue by region with year-on-year comparison" is converted by an LLM into a SQL query on your warehouse, plus a suitable visualisation. ThoughtSpot offers a commercial version of this, but the real strength lies in a custom build where the LLM knows your semantic layer (column names, business definitions, joins) and stays neatly within row-level security. We build this with Claude or GPT-4 as the backend, plus a custom text-to-SQL controller that validates the query result.
Real-time streaming BI: operational dashboards that show live status rather than "last night's data". Logistics, energy, financial trading, gaming platforms: environments where a dashboard refreshing every five minutes is too slow. We build real-time dashboards on Kafka, Materialize or ClickHouse streams, often integrated with a real-time analytics platform as the top layer.
Most BI projects fall into one of these three profiles. Which one fits depends on who will use the dashboard, which compliance requirements apply, and how deep into the stack you want to go. In the first conversation we advise which profile is the right match, and we're honest when a standard BI tool is simply the better choice.
An analytics layer inside your SaaS product that feels like part of your own UI. Login-aware, in your brand style, with the visualisations and filters your customers actually use. Under the hood: a React frontend with a charting library such as ECharts or Recharts, a custom query layer on top of your existing data model, and row-level security so that customer A never sees customer B's figures. We start with the three to five dashboard views most requested by your customer success or support team, and expand based on what is actually used in practice. Often a replacement for a slow PDF report or a click-through to an external tool nobody opens.
A dashboard platform used by multiple customers, partners or business units, each with their own data environment, branding and permission structure. Often integrated with a data engineering platform that manages the ETL and warehouse. The multi-tenant layer is usually where the complexity lies: how do you partition data per tenant without query plans degrading, how do you scale the RBAC layer so that one tenant can manage its own roles, and how do you keep operating costs under control as the customer base grows from ten to a thousand. We build this on a data warehouse (Snowflake, BigQuery, ClickHouse or Postgres with partitioning), with a metadata layer that filters per tenant.
A BI layer with a natural-language interface: your user asks a question, an LLM such as Claude or GPT-4 translates it into SQL, runs the query and delivers a suitable visualisation. Or: a real-time streaming dashboard that shows live status instead of a nightly refresh. Both use cases are the most technically demanding BI projects we undertake, and require thorough LLM integration or a streaming architecture built on Kafka or Materialize, respectively. For text-to-SQL we build a controller that validates the generated query against a whitelist of tables and columns: not a "do whatever the LLM wants" approach, but a controlled semantic layer in which the business definitions are explicitly captured. For real-time we work with aggregations that update incrementally, so the dashboard stays fast even at high event volumes.
A working BI tool that your team manages itself, plus everything around it to keep building without vendor lock-in on a commercial platform.
With OneDayBuild, we turn your idea into something tangible in one day for €1,150, so you can see whether further development is worth the investment. Decide to go ahead with the full build? Then we credit the full cost.
Explore OneDayBuild →Four patterns in which we help organisations move to their own BI tool. If you recognise one, we're happy to talk further. If you recognise none, a standard tool is probably the better option.
Your customers ask for dashboards within your SaaS, not a separate Tableau licence. A built-in analytics layer in your own branding becomes part of the proposition and reduces support questions about "where do I see this number?". A commercial embedded BI tool may work for a while, but above a certain scale it becomes more expensive than building your own.
You serve multiple clients, partners or business units, each wanting their own data environment and branding. A Looker or Power BI licence per tenant becomes unmanageable; a custom multi-tenant layer with a shared codebase is both cheaper and easier to adapt to client-specific requirements. See also our page on a custom KPI dashboard for a simpler variant.
Healthcare with NEN 7510 requirements on patient data, finance with DORA and MiFID reporting, energy with regulatory logging. The compliance layer sits so close to your data that a standard BI tool becomes more of an obstacle than a solution. A custom tool is built directly around the right audit trails, row-level security and encryption requirements.
You want text-to-SQL ("show me Q3 revenue by region"), or a live streaming dashboard that lags only a few seconds behind the source event. Both use cases call for an architecture that falls outside the scope of a standard BI tool. We build them on a Databricks layer or a custom ClickHouse / Materialize stack.
A conversation in which we understand who will use the dashboard, which decisions it supports, and which sources need to be connected. We check straight away whether the business case justifies custom development or whether an off-the-shelf tool would suit better. That's an honest question, not a sales funnel.
A workshop with your team plus interviews with three to five end users. We draw up an architecture blueprint (warehouse layer, semantic layer, frontend, RBAC model) and an initial screen flow for the key dashboards. At the end you have a concrete scope and a chosen tech stack.
A working build every two weeks. We start with the data layer and one basic dashboard, then expand to the full feature set. Your team tests along with us each sprint and helps decide the priorities, so the scope often shifts along the way based on what works in practice.
Before go-live: penetration testing, a row-level security review, an audit trail check and data flow mapping for the DPIA. For sector-specific regimes (NEN 7510, DORA, the AI Act in the case of text-to-SQL), we deliver the additional documentation and governance materials your regulator expects.
A phased rollout to your user group, training sessions for key users, and ongoing management of security and performance. For multi-tenant platforms, this also includes tenant onboarding and setting up your operational runbook.
What clients typically want to know before starting on a BI tool.
A thirty-minute introductory call, no obligation. We listen to who will be using the dashboard, which data sources are involved and which compliance requirements apply. We will then tell you honestly whether custom development is the right route or whether a standard BI tool is simply the better choice.
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