$12.9M
Average annual cost of poor data quality
Gartner
55%
of enterprise data is never used for a decision
15-25%
of revenue lost annually to poor data quality
MIT Sloan
Customer, product, inventory, pricing, and operational data live across dozens of systems, making every important decision slower than it should be.
We consolidate that data into a governed cloud platform that delivers a single source of truth — so reporting, analytics, AI, and forecasting all start from the same trusted foundation.
Each of these is a production outcome, measured against the cost it removes or the decision it speeds.
The problem
The same number reads three different ways across commerce, ERP, and CRM, so every meeting starts by arguing about whose figure is right.
The solution
We consolidate sources onto one governed platform with identity resolution, deduplication, and lineage - so the business asks questions of one trusted set of data instead of reconciling systems by hand.
The problem
Brittle nightly batch jobs break silently, and data is stale before anyone sees it.
The solution
Cloud-native ingestion and ELT - batch and streaming, with change-data-capture - that scales with volume and lands data fresh, so analysts spend their time on analysis rather than the ~40% of it the industry still loses to data prep (Anaconda).
The problem
only 20% of leaders say their organization excels at decision-making (McKinsey) - the data exists but never reaches the moment of choice.
The solution
operational dashboards, self-service and embedded analytics, and forecasts wired to the decisions that matter - demand, inventory, pricing, margin - delivered where your teams and customers work.
The problem
after an acquisition, leadership flies blind for months while data is consolidated by hand - and the cost savings, vendor consolidation, and portfolio synergies that justified the deal stay hidden.
The solution
a unified onboarding platform that rapidly folds an acquired company's data into one model and surfaces vendor overlap, product rationalization, and cross-business performance in weeks, not months - so the deal's value shows up on the clock you answer to.
The problem
teams wait on analysts to write reports, and most data never informs anything.
The solution
a semantic layer and clean, governed data that make natural-language querying, ML forecasting, and anomaly detection work on your numbers - the same foundation our Applied AI and Knowledge Grounding work builds on.
The warehouse/lakehouse every workload runs on, elastic and pay-for-use — Snowflake, Google BigQuery, Databricks, or Microsoft Azure (Synapse, Fabric, Data Lake), on Azure, GCP, or AWS.
Move data in reliably, from batch to near-real-time — ELT and ETL, change-data-capture, and streaming via Fivetran, dbt, Airflow, Spark, Kafka.
Turn raw sources into a coherent, query-fast model — a data lake, lakehouse, and dimensional warehouse, with a semantic layer as one source of truth.
Make the result trustworthy and controlled — identity resolution, deduplication, data lineage, cataloging, and access control.
Put insight where decisions are made — Power BI, Looker, Tableau, self-service, embedded, and natural-language analytics.
Predict and explain, on your own data — ML forecasting, anomaly detection, and GenAI over governed data.
Engagements we've shipped - data foundations built around a specific operational question. Client names are under NDA; the outcomes are the clients' own.
Enterprise analytics platform — FinTech asset management
THE PROBLEM
Fragmented reporting across multiple systems gave leadership little real-time visibility into business performance or customer activity.
THE SOLUTION
We designed and built a modern cloud analytics ecosystem that unified operational and analytical data — real-time ingestion, an enterprise data warehouse, embedded analytics inside customer-facing applications, self-service dashboards, and a scalable pipeline and governance framework spanning executive, operational, and customer reporting.
THE RESULT
Near real-time visibility into performance, faster decisions through self-service, lower reporting latency and manual effort, and a richer customer experience through embedded insight.
TECHNOLOGY
Snowflake, BigQuery, Looker, Pub/Sub, ETL pipelines, cloud data architecture.
Acquisition onboarding & synergy analytics — distribution enterprise
THE PROBLEM
After each acquisition, integrating the new business took months of manual analysis, leaving leadership without a timely view of acquired operations — and slow to spot cost savings, vendor consolidation, and portfolio synergies.
THE SOLUTION
A unified acquisition-onboarding platform that rapidly folds acquired-company data into one enterprise model and delivers a full operational view in weeks instead of months — vendor and supplier synergy analysis, product-overlap identification, and cross-business performance in executive dashboards.
THE RESULT
Onboarding cut from months to weeks, faster realization of acquisition value, quicker vendor-consolidation and cross-sell wins, and far better visibility during M&A integration.
TECHNOLOGY
Microsoft Azure, Azure Data Factory, Azure Data Lake, Power BI, Tableau.
Natural-language querying gets teams answers directly, without writing a report.
These run inside the workflow itself, not as a separate exercise someone has to remember to do
Instead of being hunted for across dashboards nobody opens.
Before they reach a decision, not after.
See how AAXIS turns scattered data into a trusted, governed foundation.
We consolidate the sources that decision needs onto one governed foundation, with quality and lineage controls, so you ask questions of one trusted set of data, and expand from there.
It depends on your workloads, your existing cloud, and where your team's skills sit. We deliver across all four and choose for your case rather than a house preference, building cloud-agnostic so the decision isn't a lock-in.
No. We build the analytics foundation on top of your systems of record and integrate to them — modernizing the experience and the pipelines without ripping out what works.
Governance is a layer of the build, not an afterthought—access control, data lineage, cataloging, and quality rules—so the platform is auditable and teams only see what they should.
Yes—through self-service dashboards and, increasingly, natural-language querying, so teams get answers directly instead of waiting on an analyst.
Building Materials
Streamlining operations through enterprise digital services and automation.
Explore
Start with one high-value
question.
We'll help you pick the question with the clearest payoff, then build the foundation that answers it.