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.
We work with companies across the US, UK, Canada, and EU. We work with a focused number of clients at any given time.
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.
A technical hire was made to address the data problem. The result works for them specifically — and is not something the wider organisation can interpret, own, or maintain.
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 — then stays on to operate it. The result is a functioning data team without the cost or complexity of building one internally.
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, and the outputs your team actually uses. No handover to a third party.
Once live, we run it. Monitoring, incident response, new source integration, model updates, and continuous improvement — included.
Each area can be scoped independently, though most engagements draw on more than one. They are designed as a coherent system, not a menu of isolated services.
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.
The following is a detailed account of a platform Anstrom designed, built, and continues to operate. We have described the business context, the technical decisions made and the reasoning behind them, and what the system does in production today.
[Describe the organisation and the specific operational situation — the decisions being made on unreliable data, the practical consequences for the finance or operations function, and why resolving the data infrastructure became a priority. A prospective client in a similar position should recognise their own context in this paragraph.]
Example: The CFO was compiling board packs manually each month — extracting reports from the ERP, reconciling them against CRM data in Excel, then cross-checking against three departmental submissions that never quite agreed. The process consumed four working days each cycle, and the numbers remained subject to challenge at the board table regardless.
[Describe the technical landscape as it existed — the systems in place, how data was moving between them, what was absent. Specificity is what makes this credible: the names of the tools, the actual failure modes, the specific gaps in governance or documentation. Generic descriptions carry little weight with a technical reader.]
Example: Three source systems — [ERP], [CRM], and a logistics platform — each operating on different update cycles with no shared identifier across customer or product records. All transformation logic resided in Excel workbooks maintained by a single analyst in Finance. No metric definitions were documented. The term "revenue" carried a different meaning in Finance, Sales, and Operations — each technically defensible, none shared.
The following covers the principal architectural decisions made during the build and the reasoning behind each. This is not a technology inventory — it is an account of the choices, the trade-offs considered, and why we arrived at the conclusions we did.
[e.g. Fivetran for the two SaaS sources. A custom extraction job for the on-premise ERP, which had no available API — scheduled nightly against a read replica. All raw data lands append-only in Snowflake with no transformation applied at ingestion. This was a deliberate decision: the raw layer is the audit trail, and it must remain unmodified.]
[e.g. dbt for all transformation logic. The first two weeks of the build were spent with Finance and Operations jointly establishing metric definitions — revenue, gross margin, active headcount — before any models were written. Those agreed definitions are codified in the dbt project and enforced at every downstream layer. Every report in the organisation now draws from the same mart.]
[e.g. Airflow on a managed instance. The DAG structure was kept deliberately simple — predominantly linear pipelines with explicit retry logic and channel-based alerting. Orchestration complexity has a maintenance cost that compounds over time. The intent was that an engineer unfamiliar with the project could understand the pipeline structure within a working day.]
[e.g. Looker connected to the governed Snowflake mart layer. The CFO and COO each have a live board-pack dashboard that refreshes overnight. The Finance team continues to run ad-hoc queries — now against the governed mart rather than raw system exports. The four-day monthly reconciliation process no longer exists.]
A note on what we would approach differently: The complexity of establishing a shared customer identifier across three source systems was underestimated at the diagnostic stage. The resolution process took three weeks and delayed delivery of the first mart models. In retrospect, identity resolution warranted dedicated scoping time before the data model design was committed to.
[Describe what the system does in production — which data flows, at what cadence, serving which teams and use cases. Operational specifics carry more weight than outcome summaries.]
Example: The platform processes [X] records daily across [N] source systems, with overnight pipeline runs completing by 6:00am. The CFO reviews a Looker dashboard each morning showing the prior day's P&L, cash position, and key operational indicators. The monthly board pack is now produced in under two hours. Monitoring, incident response, and source system integration are managed by Anstrom under the ongoing retainer.
Anstrom was founded with a precise objective: to design and build data and AI infrastructure that performs reliably in production, is understood by the people who depend on it, and produces outputs that do not require technical intermediaries to interpret.
We work with a small number of clients at any given time, by design. We are not a staff augmentation firm, nor a reporting and dashboard agency. We design systems, build them to a production standard, and assume ongoing responsibility for operating them — which means our interests are aligned with long-term stability, not delivery velocity alone.
[Two to three sentences on your professional background — the roles you've held, the types of systems you've built, and the specific experience that led to founding Anstrom. Prioritise specificity over conviction. A prospective client evaluating whether to trust you with their data infrastructure is asking a straightforward question: who is this, and what have they actually done?]
"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 or proofs of concept that require a subsequent rebuild to reach production.
We remain engaged after delivery. We operate the system, manage incidents, integrate new data sources, and develop the platform incrementally over time. 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.
Thank you — we will respond within one business day.