Business intelligence services that end the guesswork

Business intelligence services cover everything between the systems that create your data and the people who need an answer from it. In practice that means four things: data integration services that pull records out of your CRM, ERP, billing system, and product database on a schedule you can rely on; a data model that defines what a term like active customer or gross margin actually means, once, so two teams cannot compute it differently; dashboard development and reporting that put the resulting numbers in front of the people who act on them; and the consulting work that decides which questions are worth answering first. We deliver all four, and we deliver them as engineering rather than as a slide deck.

Business intelligence answers what happened and what is happening now: revenue by region this quarter, orders stuck in fulfilment today, churn by cohort since January. Data analytics services reach further, into why it happened and what happens next, which is where segmentation, statistical testing, and predictive analytics services live. The distinction matters less than the sequence. Predictive work built on data nobody trusts produces confident forecasts of the wrong thing. We build the reporting layer first because it is also the layer that proves your pipelines are correct, then extend into forecasting once the numbers hold up.

Cost tracks three variables: how many systems have to be integrated, how messy the data in them is, and how many dashboards and reports you need on day one. A focused engagement covering two or three sources and a single executive KPI dashboard is a very different number from an enterprise business intelligence programme spanning a dozen systems, a warehouse build, and role-based access for hundreds of users. We do not publish a rate card because a range that wide is not useful to you. Bring us your source systems and the decisions you want to improve, and discovery ends with a scope, a fixed build cost, and a running cost model that separates our fees from the platform and infrastructure you will pay for directly.

No, and usually you should not. Most organisations that come to us already own licences, and replacing a tool the finance team knows is rarely the highest-value change. The problem is almost never the chart tool. It is the pipelines feeding it, the definitions behind the measures, and a refresh that does not finish before the meeting starts. We build that layer, expose it over standard SQL, and connect your existing tool to it, so the reports people already rely on keep working and start being right. We build custom dashboards when the requirement genuinely calls for it, typically embedded analytics inside your own product, where per-seat licensing is the wrong commercial shape.

PrismAnalyst is our AI data analytics platform. You upload a CSV or Excel file, ask a question in plain English, and get a chart or table back with the SQL query that produced it attached. The difference from a traditional BI tool is what happens before the first answer: a dashboard has to be configured in advance by someone who already knows the question, whereas PrismAnalyst has nothing to pre-build. The difference from a general-purpose AI assistant is the audit trail. Every number traces to a query you can read and re-run, so nothing is estimated from model memory. In most engagements the two work together: governed dashboards for the metrics the business watches every day, and PrismAnalyst for the follow-up question the dashboard was never built to answer.

The first dashboard a team actually uses is usually live within four to six weeks, covering one domain and the sources it needs. That is deliberate. Programmes that spend six months integrating everything before showing anyone a number tend to discover their definitions were wrong in month seven. Broader enterprise business intelligence rollouts run three to six months depending on source count and data quality, and the pace is set almost entirely by how clean the upstream systems are and how quickly your team can settle definitional disputes. We scope both in discovery and give you a dated plan.

Yes. Our data integration services cover relational databases, cloud data warehouses, SaaS platforms through their APIs, and the spreadsheets and flat-file exports that still carry real business logic in most companies. We build on PostgreSQL by default and read from whatever your systems already run on. Where no API exists we build the extraction. Pipelines run as scheduled, queue-backed jobs with retry and alerting, so a source system that goes down delays a refresh visibly instead of quietly publishing stale numbers as current ones. Tell us your specific sources in discovery and we will confirm what connects directly and what needs building.

We start from decisions, not from data. For each team we ask what they would do differently if a number moved, and any metric that produces no answer does not become a tile. That single filter usually cuts a proposed dashboard in half, which is the point: KPI dashboard development fails far more often from clutter than from missing charts. What survives gets a written definition, an owner, a refresh cadence, and a target, and the definition lives in the data model rather than in a formula someone typed into one report. When sales and finance disagree about what a closed deal means, that gets resolved in discovery, before it becomes two dashboards that never reconcile.

Yes, with a precondition. Forecasting is only as good as the history feeding it, so we do it once the reporting layer proves the history is correct. From there the common work is demand and revenue forecasting, churn and risk scoring, anomaly detection on operational metrics, and period-over-period comparison that separates a genuine trend from seasonality. PrismAnalyst computes forecasts as regression over your actual data via SQL rather than estimating from a language model, so the projection is reproducible and you can see exactly what it was fitted on. For heavier modelling we build in Python against the same warehouse, and the output lands back in your dashboards rather than in a notebook nobody opens.

Your data stays in your environment. We deploy into your cloud account or your own servers, and we work bring-your-own-key, so you hold the credentials to your sources and your warehouse. When AI is part of the build, PrismAnalyst uses a Schema-Only Protocol: the language model sees column names, data types, and aggregated results, never individual records or PII, and the analysis itself runs on a local query engine. That is what makes the AI layer defensible to a legal or compliance team rather than something they have to prohibit. Access inside the dashboards is role-based, so a regional manager sees their region and the board sees the roll-up.

You do, in full. Pipelines, transformation logic, data models, and dashboard definitions are delivered into your repositories and your infrastructure, with documentation written for the engineer who inherits it rather than for us. There is no proprietary runtime you have to keep paying for to keep your own reports working, and no lock-in clause that makes leaving expensive. We would rather be retained because the work is good.

Less than most teams expect. You need read access to the systems that hold the data, one person per domain who can settle definitional questions with authority, and a short list of the decisions you want to make better. You do not need a warehouse, a data team, or clean data. Cleaning it is part of the work, and the mess is usually informative: the places where two systems disagree are almost always the places where the business is losing money or time.