Your data is in one place, but teams still wait weeks for answers and nobody quite trusts the dashboard. Flentas turns governed data into self-service BI, advanced analytics, and GenAI your teams can act on.
The data is finally in one place. Getting a decision out of it is still the hard part.
The model is ready, but the data isn't clean, trusted or accessible enough.
Usage grows, and so does the compute bill.
By the time a report is ready, the business question has already moved on.
Analysts spend more time cleaning and joining data than analyzing it.
Finance, sales and ops each report a different figure, and meetings turn into reconciliation.
A simple question waits weeks because only a handful of people can write the query.
A data platform on its own does not create insight. Analysts still lose close to 80% of their time preparing data instead of analysing it, almost 40% of teams name a lack of usable data as their top barrier to adopting AI, and nearly a third of GenAI projects are expected to be abandoned. The gap now is not storage — it is trusted, self-service intelligence people actually use.
Finance, operations, and marketing each define a metric their own way, and meetings turn into arguments about whose figure is correct. A governed metrics layer means a KPI means the same thing in every report.
Business teams queue behind a small, overloaded analytics team, and by the time a manual export lands the moment has passed. Self-service BI and natural-language analytics let teams answer their own questions.
The model is ready but the data behind it is not — quality, lineage, and access questions stall promising GenAI pilots before production. We ground GenAI in the same governed data your BI already trusts.
Self-service adoption without cost visibility turns into a runaway compute bill. Analytics FinOps and right-sized compute keep scaling insight from becoming a budget problem.
We begin with an assessment and sequence by value and feasibility, usually starting with a metric you already track and a business owner who wants the outcome. Prove value on one question, then extend the same pattern.
We map the decisions the business needs to make, the metrics behind them, and where trust in the numbers breaks down today.
We stand up governed metrics, the first dashboards or models, and self-service access for the teams that need answers.
We run analytics as a managed capability, add use cases, and keep quality and cost under control as adoption grows.
Amazon QuickSight · Power BI · Tableau · Redash · Databricks SQL · embeddable analytics
Amazon SageMaker · Databricks Mosaic AI · forecasting & segmentation on curated, verified data
Databricks Genie · Amazon Q · Amazon Bedrock · natural-language-to-SQL and RAG on your own data
Athena · Redshift · Amazon Q · Bedrock · SageMaker
Semantic layer · Unity Catalog governance · cost visibility and right-sized compute
ChangeSafe — Flentas' proprietary GenAI accelerator · Unity Catalog end-to-end governance
Marketing, merchandising, and finance each run their own version of revenue — a governed metrics layer puts every team on one number before the next planning cycle.
Player and marketing events pile up faster than a small analytics team can turn into dashboards — a partitioned analytics lake and self-service BI let studios ship KPI views without a backlog.
Mobile game events and third-party marketing sources streamed into a partitioned analytics lake, with custom KPI dashboards for business, marketing, and leadership on Athena and QuickSight — at roughly 40% lower pipeline cost, and scaling to more titles.
Application and environment sprawl leaves no single view for operations or compliance — unifying everything into one self-service analytics layer restores visibility without adding headcount.
More than 20 applications and environments unified into a single self-service analytics and monitoring view, with reporting for business teams through QuickSight — Tableau and Redash offered as options, on a costed AWS design.
GenAI pilots ask a model to answer questions the underlying data was never governed to support — grounding natural-language analytics in a curated, lineage-tracked foundation is what gets a pilot into production.
“Every team had its own version of the numbers, and by the time an answer came back from analytics, the question had moved on. Flentas gave us a governed metrics layer and self-service dashboards our own teams could build from — and when we finally tried natural-language querying on top of it, people started using it the first week.”
VP of Product AnalyticsConsumer Mobile Gaming Platform
One engagement is one stage. Here is what usually comes before and after, so the next step is always clear.
One governed copy of your data, ready for BI and AI.
Explore Data EngineeringSelf-service BI and GenAI grounded in data you trust.
Build and run production agents with governance and cost under control.
Explore Agentic AI SolutionsA scored roadmap for which AI use case to build first and what it needs.
Explore AI Readiness AssessmentBook an analytics assessment. We map the decisions your business needs to make, the metrics behind them, and where trust in the numbers breaks down today.