Braintix Labs builds AI analytics solutions on top of the data you are already collecting — warehouse, CRM, ERP, billing, product events — instead of asking you to move any of it.
We are an applied AI company, not a BI vendor with one fixed dashboard. Every deployment is scoped against your data model, your reporting cycle and your own definition of each metric.
Our team has built dashboards that take a follow-up question in plain language and return a chart, dashboards that flag a metric moving out of pattern before anyone opens them, and dashboards that write their own monthly commentary.
Decision first, chart second. We ask which decision your team keeps making late or making blind, and design the dashboard around that one answer instead of putting every available metric on a screen.
No second reporting system for your team to maintain. The dashboard queries your existing warehouse directly, respects the permissions you already have in place, and pushes alerts and summaries into Slack, Teams or email rather than waiting for someone to log in.
Most companies do not have a data collection problem. They have an answer-latency problem: the number is somewhere in the warehouse, but getting it into a decision takes days and one specific person. Here is where that time goes.
The dashboard shows that revenue has declined. Analysts spend most of their week writing one-off queries instead of building something sustainable, and the business learns to stop asking questions.
No one monitors metrics around the clock. A drop in conversion rates, a spike in customer churn, cost overruns, or a data transmission failure are usually detected during monthly analysis—much later than the stage when the situation could have been corrected without significant costs.
The finance, sales, and product development departments each use their own versions of terms that were created by different people in different tools. Without a single, agreed-upon set of metric definitions, implementing artificial intelligence will only result in definitive answers from three incompatible sources.
Someone exports the data, pastes it in, reformats it, and writes the same comment as last month, only with new numbers. This work is entirely repetitive, and that is precisely what makes it an inefficient use of a qualified specialist's time.
Book a 30-minute working session. We will look at your current reporting setup, show where answers are getting stuck, and tell you honestly whether AI is worth adding to it.
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Each module below runs independently, so you can start with one and expand once it proves out.
Your team asks a question in plain language and gets a chart back, with the query and source rows visible underneath. The model is limited to metric definitions you have approved, so it cannot invent a formula or reach into data it should not see.
Every metric gets its own learned baseline, so a quiet Sunday does not trigger a page and a real 6% drift does. Alerts arrive with the drivers already worked out — which segment, which channel, when it started.
Demand, revenue, cash flow and churn forecasts trained on your own history, with confidence ranges rather than one confident line. Scenario controls show what happens if pricing moves or a budget is cut, without opening a spreadsheet.
Weekly and monthly write-ups produced from live data: what moved, which segments drove it, how it compares to plan. They land in email, Slack or Teams so people actually read them.
We deploy in four stages, and you can stop after any of them. Nothing goes into production before it has been proven on your own data — a full rollout typically runs six to ten weeks from kickoff.
We audit your data sources, warehouse structure, existing dashboards and metric definitions, then agree on the first decision to support and the measure that will judge it. Output: a written scope with success criteria, an integration map and an honest list of the data gaps that need closing first.
A limited proof of concept runs on one team, one domain or one set of metrics, tested against your real historical periods. You see answer accuracy, forecast error and alert precision before committing further. Typical pilot length: two to four weeks.
The validated setup is built out properly: semantic layer and metric governance, permissions and row-level access, alert routing, escalation rules, dashboards and user training. We roll out team by team so existing reporting keeps running while the switch happens.
We monitor answer quality, retrain forecasts on new outcomes and correct drift as your business and data model change. Once the first domain is stable, we extend the same ai analytics dashboard into adjacent ones — operations, finance, support, supply chain.
We start with a short pilot on your real history. If the accuracy does not hold up, you have lost a few weeks — not a year-long platform contract.
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AI analytics for companies with recurring reporting cycles and a reasonably clean data source pays back fastest. If the same questions get asked every week and answered by hand every week, that pattern is exactly what a model handles well.
High event volume, well-defined metrics and a direct link between product behaviour and revenue make SaaS the natural first fit. Usage, billing and support data together carry enough signal for reliable cohort analysis and churn prediction.
CFOs and COOs get a picture that is current rather than reconciled after the fact. Plan-versus-actual, margin movement and cash position update continuously, with the drivers behind each variance already attributed.
Large organisations already have the warehouse and the BI licences; what they lack is capacity to answer everyone. An AI layer absorbs the repetitive requests so the data team can work on models and infrastructure instead of ad-hoc SQL.
When reporting is somebody's second job, ai powered business intelligence gives a small team the analytical capacity of a much larger one without the hiring risk. In practice, an ai data analyst covers the questions a ten-person company has no one to ask.
Illustrative examples of the kind of AI analytics systems Braintix deploys — not client case studies, just a look at the shape of the work.
A governed natural-language layer over the warehouse. Managers ask a question, get a chart, a number and the query behind it, and drill down without waiting on the data team.
Demand, revenue and churn forecasts with confidence ranges and adjustable scenario inputs, so planning discussions start from a model rather than from opinion.
White-labelled dashboards inside your own product, with multi-tenant isolation and per-customer permissions, so clients get their own data under the same governance.
Yes. We connect to PostgreSQL, MySQL, ClickHouse, BigQuery, Snowflake and Databricks, and we work alongside Power BI, Looker, Tableau, Metabase and Superset rather than replacing them. Your existing dashboards, permissions and scheduled reports stay exactly as they are — we add a layer on top. If your data sits in a CRM, ERP or billing system with a documented API, we can bring it in without a migration project.
An AI analytics dashboard is a reporting interface that uses machine learning and language models to do the parts of analysis that normally need a person: turning a question into a query, spotting when a metric behaves unusually, projecting a trend forward and writing up what changed. It runs as a layer over your existing data — the warehouse supplies the numbers, a governed semantic layer supplies the approved metric definitions, and the model works only inside those boundaries.
Only if the system is built to be checked, which is how we build it. The model can query approved metrics and tables and nothing else, every answer displays the generated SQL and links back to the underlying rows, and ambiguous questions get a clarifying prompt instead of a guess. We validate accuracy during the pilot against questions your team already knows the answer to.
Yes. Metric definitions, access rules, languages and alert routing are configured per team, region or product line while reporting stays consolidated. For customer-facing analytics, multi-tenant isolation keeps each client's data separate under one deployment. We usually prove the setup with one team first, then replicate it — each additional rollout is faster because the integration work is already done.
Pricing depends on the number of data sources, the complexity of your metric model and query volume, so we quote after Discovery rather than from a rate card. A scoped pilot is deliberately small and fixed-price, which lets you judge accuracy and value before committing to a full deployment. Ongoing cost splits into support and underlying model usage, and we show both transparently — including what we expect it to cost at your volume, not just the entry figure.
Tell us which reporting question keeps your team waiting. We will look at your data sources and come back with a concrete first step to get that answer into a decision.
Ukraine — working with teams worldwide