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.
Expertise in AI Analytics Dashboard Development. 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.
Approach to Dashboard Design. 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.
Built Into the Stack You Already Use. 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.
The case in favor of artificial intelligence has long since moved beyond the purely theoretical — your competitors are now using it to make deals more quickly and improve their operations.
AI removes repetitive, low-value work so your teams can focus on decisions and outcomes.
By automating routine tasks and surfacing the right information at the right moment, people spend less time searching and coordinating — and more time on work that actually moves the business forward
Organizations that operationalize AI respond to customers and markets faster than those still relying on manual processes.
Braintix helps you build capabilities that are hard to copy — models and workflows shaped around your data, your customers and the way your business actually runs.
AI reduces the cost of doing business by streamlining operations, cutting rework and preventing expensive errors.
Instead of adding tools that create more overhead, we target the workflows where automation and better decisions translate directly into lower operating costs.
AI helps you see problems earlier and act on them with confidence.
From monitoring anomalies to improving forecasting and compliance checks, the right systems give leaders clearer signals and fewer surprises — with human oversight kept firmly in the loop.
Our AI transformation agency works throughout various industries, but we never treat them as interchangeable; we approach every project by focusing on the real challenges of that specific industry, rather than relying on generic templates.
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.
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.
Book a ConsultationEach module below runs independently, so you can start with one and expand once it proves out.
About solution
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.
About solution
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.
About solution
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.
About solution
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.
Request a Pilot
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.
Choosing the right partner is even more important than the underlying technology. That’s why companies that are seeing real results from artificial intelligence are choosing Braintix.
Request a Free CallWe operate in short cycles and make each process simple to understand. Your team actively engages in AI-driven business transformation, and you remain informed throughout.
More than 9 out of 10 of the systems we implement are still up and working a year after launch. You’re paying for AI that keeps going for the long, not for prototypes that quietly get shelved.
We work with the unique characteristics that you already possess — your data, procedures, and customers. You receive a finished product that is entirely yours.
From the start, we immediately focus on the use cases that generate profit. You rapidly obtain tangible results, without being dragged into a prolonged evaluation phase that exhausts your budget.
5 → 1tools replaced by one CRM
A CRM built around your business process, with messengers, lead tracking, dashboards and an AI autopilot inside. Add finance, project management, HR or any other module you need, and run your whole business from one place.
1 questioninstead of 5+ services
A private AI agent connected to your documents, CRM and finance systems. Employees ask questions and get answers, reports and files, with role-based access.
5+data sources in one view
A custom analytics dashboard that connects your CRM, finance systems, Google Analytics and ad platforms like Meta and LinkedIn, builds end-to-end funnel analytics and comes with an AI assistant that reads the numbers for you. Running at client companies and in our own team.
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.
