In most mid-market and PE-backed organizations, the record is spread across a CRM, an ERP, a billing platform and a set of unowned spreadsheets, and the digital estate has grown the same way. Each system is internally consistent. The architecture connecting them was never designed, so the consolidated view is not.
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The numbers presented to the board are rebuilt in spreadsheets from exports that nobody owns. Consolidation takes days, and a revision cannot be traced to a change in the underlying source.
The CRM, the ERP, the billing platform and the support desk each hold part of the record. What connects them is a person, a schedule and an export, so the joined view is only as current as the last manual run.
The same account appears four times under four spellings. Enrichment is partial, ownership is unassigned, and segmentation is built on fields that were never validated at entry.
Websites, portals and internal tools accumulate across acquisitions and agencies. Nobody holds the map, so cost, risk and performance are all estimated rather than measured.
Source systems feed one governed data model on a schedule, so the dashboard the board reads is the same figure the operating team works from, produced without manual assembly.
Every report starts with an export and ends in a spreadsheet.
Every flow between systems is instrumented with logging, retries and alerting, so a schema change upstream raises an alert instead of silently corrupting a month of reporting.
Integrations break quietly and are discovered weeks later.
Sites, portals and applications share a documented stack, release process and support cadence, so cost and risk are known rather than discovered during diligence.
Web properties and internal tools are maintained by whoever built them.Engagements begin with an architecture assessment and proceed to implementation on a governed retainer. We design the target architecture, then do the engineering ourselves alongside your team: the records, the pipelines, the models, the dashboards, the automation and the web estate that all of it is delivered through.
Deduplication, standardization and enrichment of customer, product and financial records against a defined schema.
Pipelines and APIs connecting CRM, ERP, billing, support and product systems into one governed flow of record.
A modelled reporting layer and custom dashboards built on metric definitions the business has agreed in writing.
Budgeting, forecasting and unit economics built on the same governed data as the operating reports.
AI strategy and process automation applied where the data is reliable enough to act on, with a human review path where it is not.
Post-merger consolidation of the acquired digital estate: domains, sites, analytics, tracking and brand systems.
Engineering of marketing sites, portals and internal applications on a documented stack with a defined release process.
Continuous maintenance, security and development capacity for WordPress estates at scale.
Interface and design system work grounded in how the product and the funnel actually convert.
We engineer on platforms that hold one architecture intact from source system to board pack, rather than on whatever is quickest to license.
FIVETRAN · DBT · SNOWFLAKE · BIGQUERY
POWER BI · LOOKER · METABASE · PYTHON
CLAUDE AI · WORDPRESS · REACT
Each system, each table and each export is documented with what it holds, who owns it and how current it is. It is the first artefact produced, and the one diligence asks for.
A documented inventory of sources, owners and refresh cadence for the whole estate.
Customer, product, contract and revenue records are resolved into one modelled layer, so Finance, Sales and Operations stop maintaining private versions of the same entity.
A governed data model, versioned and tested, feeding every downstream report.
ARR, churn, gross margin and pipeline each have a documented formula and one calculation path, so two dashboards cannot disagree and a board figure can be defended line by line.
A published dictionary of metrics, tied to the model that computes them.
Pipelines are monitored, releases follow a defined process, and changes to definitions pass through a named reviewer rather than around one.
A standard that survives new hires, new tools and the next acquisition, because it has an owner.
The assessment is the entry point. The source map, data model, metric dictionary and operating standard are what remain in place afterwards.
“When it comes to a high-scale revenue delivery initiative, DevriX gets the job done, period. They quickly jumped in on a partially-started project and led it to completion in record time. They worked with our team to iron out the finer details while building the full picture. Communication, attention to detail, and attentiveness were all 5-star. Stellar work.”
“They bring a spirit of collaboration to their work, as well as taking initiative on virtual project management. It's extremely rare to work with a firm that has both development and business revenue chops. One remarkable thing: I have never met the DevriX team. We have never even spoken on the phone or VoIP. Another member of our team has spoken with them, but the point is I've been able to get projects done with DevriX that involved complex design requirements without ever talking real-time. That requires an exceptional attention to detail. I cannot recommend DevriX highly enough.”
No hand-off to juniors after kickoff. Data engineers, analysts and developers who have worked inside PE-backed portfolios stay on the account and answer to your numbers.
Data is moved by export and spreadsheet. Reporting is reconstructed each period and cannot be reproduced identically twice.
Where most organizations beginPoint-to-point integrations exist between the main systems, built ad hoc. They work until a field changes upstream.
A defined data model sits between the source systems and reporting. Metrics have written definitions and one calculation path.
Pipelines are monitored, definitions carry named ownership, and routine work runs without a person in the loop. Figures withstand external scrutiny.
Where the engagement leadsWhen it comes to a high-scale revenue delivery initiative, DevriX gets the job done, period. They quickly jumped in on a partially-started project and led it to completion in record time. They worked with our team to iron out the finer details while building the full picture. Communication, attention to detail, and attentiveness were all 5-star. Stellar work.
The assessment is fixed in scope and duration. It produces a documented architectural view of your existing data and digital estate, the points at which it diverges, and a prioritized remediation plan. Implementation proceeds from those findings.
The questions raised most often by executive, finance and engineering stakeholders during the first conversation.
Typically capacity and specialization. Internal teams are usually committed to product engineering while reporting and integration work queues behind it. We take the build, run it alongside your team, and hand back a documented and tested estate.
Within them in the substantial majority of cases. The assessment determines whether the current stack can carry the model. A replacement is recommended only where remediation would exceed its cost, and that recommendation is made before contracting.
The assessment is fixed in scope and price. Implementation is scoped against its findings and delivered on retainer, because pipelines, dashboards and web estates require continuing ownership rather than a one-time delivery.
Yes. Most engagements begin with one line, commonly normalization, integration or BI, and expand once the model underneath is dependable. Web development, design and WordPress retainers can run independently of the data work.
Sequenced after the data it depends on. Automation is applied first where records are already reliable, and AI use cases are prioritized against the quality of their inputs rather than their novelty.
Consolidation of the acquired digital estate: domains, sites, redirects and SEO continuity, analytics and tracking, and rollout of the parent brand system, delivered against the deal timetable.
The source map, the data model, the metric dictionary and the operating standard, with named ownership, monitoring in place, and documentation your team can maintain against.
Typically four to eight weeks for a first governed dashboard set, depending on the number of sources and the state of the underlying records.
