Anstrom designs, builds, and operates data and AI infrastructure for mid-market companies — giving finance and operations leaders a reliable foundation for reporting, forecasting, and the decisions that run the business.
Anstrom works with companies across the US, UK, Canada, and Europe, serving as a trusted partner for data modernization, analytics, and AI transformation initiatives.
Board reporting is a manual, multi-day exercise — numbers pulled from disparate systems, reconciled in spreadsheets, and still questioned by the time they reach the boardroom.
Finance and operations work from different figures for the same metric — each drawing from separate systems with inconsistent definitions. Both are internally coherent. Neither is authoritative.
Forecasting is concentrated in individual expertise rather than embedded in a system. When that person moves on, the institutional knowledge goes with them.
There is no single, reliable view of operational performance. Decisions are made from lagged exports, partial dashboards, or the judgment of whoever compiled the last report.
The organisation has data capability, but not data infrastructure. The systems in place are functional but not governed, documented, or accessible to the people who depend on them.
These are not technology failures. They are the predictable consequences of data infrastructure that was never properly designed — and they are solvable.
Anstrom designs and builds the underlying infrastructure that makes data reliable, consistent, and usable across the organisation.
We map your current data landscape, identify where the trust breaks down, and agree on what a working system looks like.
We design and build the platform — ingestion, transformation, modelling, KPI design, and the outputs your team actually uses.
Once live, we run it. Monitoring, incident response, new source integration, model updates, and continuous improvement.
Modular by design. Coherent by intention.
The structural foundation. We consolidate source systems into a well-governed data platform - rigorous ingestion, consistent transformation, documented lineage, and clearly defined metric logic shared across the organisation.
Demand forecasting, revenue planning, and scenario modelling - engineered as durable production systems rather than spreadsheet processes that carry key-person risk and degrade over time.
Structured visibility into business performance, without the overhead of a dedicated analytics function. We connect the data platform directly to the operational decisions your leadership team makes each week.
Machine learning applied to specific, well-scoped problems — demand prediction, document intelligence, anomaly detection. We adopt AI where it materially improves the output, and decline to use it where it does not.
Industries Served
A data platform built to consolidate sales, inventory, field performance, and competitor intelligence across a global multi-channel cosmetics business.
A prestige cosmetics company with $350M in US domestic sales, distributed across department stores, Sephora, Ulta, and a growing direct-to-consumer channel. The portfolio spans skincare, colour, and fragrance across multiple brands, each managed by a dedicated commercial team.
Despite the revenue scale, the organisation was operating without a consolidated view of its own business. Weekly sales reports took three to four days to produce — assembled manually by a central analyst who pulled exports from each retail partner's portal, reconciled them against the internal ERP, and formatted them in Excel before distribution. By the time the report reached commercial leadership, the data was already several days stale. Decisions on promotional strategy, inventory reallocation, and field coverage were being made on information that no longer reflected current trading conditions.
Field sales visibility was equally limited. Territory managers had no reliable way to see how their accounts were performing between reporting cycles. Inventory imbalances — overstock on legacy SKUs, stockouts on hero franchises — were identified reactively rather than anticipated. New product launches lacked structured post-launch tracking, meaning early sell-through signals were missed. The business was losing share to more agile indie competitors and had limited analytical infrastructure to understand where, at what price points, and through which channels.
The diagnostic phase revealed six distinct data sources with no integration layer connecting them: an SAP ERP for order management and financials, a Salesforce instance used by the field sales team, individual data portal exports from Sephora, Ulta, and the top three department store accounts, a 3PL warehouse management system for inventory positions, and a flat-file feed from the DTC platform updated weekly.
No shared product identifier existed across these systems. The ERP used internal SKU codes, the retail partners used their own item numbers, and the DTC platform used a third scheme. Matching a product across systems required manual lookup tables maintained in Excel — updated irregularly and owned by one person. When that person was on leave, the weekly report was delayed or omitted entirely.
There were no documented metric definitions. "Sell-through rate," "weeks of supply," and "net sales" each had informal working definitions that differed between the Finance, Commercial, and Supply Chain teams. Competitor pricing data existed only as screenshots and ad-hoc spreadsheets compiled by the field team. New product launch performance was tracked in a shared Google Sheet with no consistent structure across launches.
The following covers the principal decisions made during the build and the reasoning behind each.
Fivetran for SAP and Salesforce. Custom Python extraction jobs for the five retail partner portals, each running on its own schedule reflecting the partner's data refresh cadence. The 3PL warehouse system required an SFTP-based file ingestion process — scheduled every four hours given the operational significance of inventory positions. All raw data lands append-only in Snowflake. No transformation is applied at ingestion; the raw schema is treated as the system of record.
The absence of a shared product identifier was the single most consequential structural problem. We built a product master table in dbt that maps ERP SKU codes, retailer item numbers, and DTC product IDs to a canonical internal identifier. This mapping is maintained as a seed file under version control and reviewed monthly. Every downstream model joins through this master — it is the foundation on which cross-channel analysis is possible.
dbt for all transformation logic, structured across four mart domains: commercial (sell-in, sell-out, sell-through by channel and SKU), inventory (weeks of supply, stock cover, overstock flags), field sales (territory performance, account-level trends, coverage metrics), and product launch (week-one velocity, distribution gains, rate-of-sale versus forecast). Metric definitions were agreed jointly with Finance, Commercial, and Supply Chain before models were written and are documented in the dbt project.
A lightweight data pipeline combines retailer pricing data with NPD market intelligence to track competitor pricing, promotions, and product performance. Automated collection and enrichment feed a competitive intelligence mart that enables the commercial team to monitor price positioning, promotional activity, and category dynamics over time—replacing manual tracking by the field team.
Power BI connected to the Snowflake mart layer. Four primary reports: a weekly trading dashboard for commercial leadership, a field sales territory view accessible on mobile for territory managers, an inventory management report for the supply chain team with automated overstock and stockout alerts, and a launch tracker that activates automatically when a new product enters the system and tracks its first twelve weeks of performance against a configurable baseline.
A note on what we would approach differently: We underestimated the time required to establish the product identity mapping across five retail partners. Each partner uses different item taxonomies and several had inconsistencies within their own historical data exports. We treated this as a technical problem initially when it was partly a commercial one — the resolution required input from the category management team, not just the data team. Engaging commercial stakeholders earlier in the identity resolution process would have saved two weeks.
The platform processes data across eight source systems on schedules ranging from four-hourly (inventory) to weekly (DTC flat files). Pipeline runs complete by 5:30am, with the weekly trading dashboard refreshed and available to commercial leadership before the start of business each Monday — a process that previously took three to four days to produce manually.
Territory managers access their field sales dashboard on mobile, with account-level sell-through data and stock coverage visible without contacting the central analytics team. The supply chain team receives automated alerts when any SKU falls below two weeks of supply or exceeds twelve weeks of stock cover across any channel. The launch tracker has been used for six product launches since go-live, with the commercial team using week-one velocity data to make distribution and promotional decisions within the first trading week rather than waiting for the monthly review cycle.
The platform is managed under a monthly retainer — covering pipeline monitoring, incident response, the quarterly competitor SKU list refresh, and ongoing mart development as new analytical requirements emerge from the commercial team.
Anstrom designs and builds data and AI infrastructure that performs reliably in production and produces outputs that do not require technical intermediaries to interpret.
"Well-designed data infrastructure should be unremarkable to operate. The value it creates is in what becomes possible once it is stable."
Sound data infrastructure is not visually impressive. It is accurate, documented, and operational. That is the standard we build to.
We do not propose an architecture until we have a clear picture of where the current state fails and why. Understanding the system is a prerequisite to improving it.
Every component we deliver is designed for live operation — monitored, version-controlled, and recoverable. We do not build prototypes that require a subsequent rebuild to reach production. The reference platform on this site is built and maintained to this standard — the same pipeline discipline, the same documentation, the same quality gates we apply to client work.
We remain engaged after delivery. We operate the system, manage incidents, integrate new data sources, and develop the platform incrementally. The retainer is not a maintenance arrangement — it is the primary mode of the engagement.
We explain what is being built and why in terms a CFO or COO can engage with directly. If a technical decision cannot be articulated plainly to a non-technical stakeholder, that is a signal to reconsider the decision.
Describe the data or reporting problem you are dealing with — what is unreliable, what consumes more time than it should, and what visibility you are currently missing. We will give you a candid view of whether and how we can address it.
We work with companies across the US, UK, Canada, and EU. Most engagements begin with an introductory diagnostic conversation — structured, specific, and without obligation.