White-label engineering, added when the client work called for it
This is a white-label software development engagement: growth agency Part & Sum brought in engineering behind its own brand, delivered as source code Part & Sum owns. Part & Sum, a North American marketing consultancy, needed engineering across three different kinds of client work: geo-incrementality analysis, recurring survey-data processing, and a research platform that could produce branded reports. Autonomous worked behind Part & Sum’s delivery, with the resulting code held in its own GitHub organisation.
The analytical method was not all ours. Part & Sum’s analyst supplied the four-phase logic in the Shiny measurement application. Autonomous packaged that work, repaired data preparation and developed the surrounding analysis and reporting workflow. Preserving that distinction allowed Part & Sum to keep its expertise at the centre while adding the engineering needed to use it.
The relationship remains open and re-engages per project. It is a way to bring technical capability into specific client work without treating every new requirement as a permanent product team.
A reproducible analytics build the whole team can run
In plain terms: we took an analysis that only ran on one analyst’s laptop and turned it into something any engineer on the team could run reliably. The measurement application was packaged in Docker around R, Shiny and GeoLift. A partly pinned scientific dependency chain reduced ambiguity about the environment needed to run it, and the build checked that GeoLift could load before the analysis began.
The preparation work addressed a quieter problem in the data. Analytics exports can omit a date-and-location combination when nothing happened. A geo-experiment needs a complete daily series. The preparation script constructed the full date-by-location grid and made absent combinations explicit, so sparsity was handled deliberately.
Autonomous then supported the test analysis, revision, channel comparison and reporting. The result included a follow-up test design for the question the initial comparison could not settle. The value was a clearer basis for Part & Sum’s conversation with its client, without converting an uncertain estimate into a confident commercial claim.
Workflow: Partner expertise → Engineering workflow → Branded deliverable → Client-owned source.
An automated survey-data pipeline, deployed as code
A monthly task that a person used to run by hand became an automated data pipeline that runs on a schedule with no manual steps. The survey workflow began with a person downloading a workbook from Drive and rerunning a notebook. The delivered pipeline expressed that recurring work as a cloud function with scheduling components, transformation logic and a BigQuery destination.
The implementation included two layers of idempotency so that repeated processing had an explicit design rather than depending on someone remembering whether a file had already been handled. Tests and deployment instructions made the work inspectable by another engineer.
This stream is described as delivered code. Production deployment and recurring execution have not been established in the available record. That boundary keeps a useful engineering deliverable from being presented as months of verified operational use.
A branded reporting platform in Part & Sum’s own voice
We built Part & Sum a branded reporting tool it runs itself, turning raw research data into client-ready PDF and presentation reports. The listening platform brought research inputs into a Django application with background processing and report generation. Part & Sum could work across social and search sources and generate branded PDF and presentation outputs for its own client engagements.
It was deployed, and the source contains eight external-platform integrations. This is a shared agency instrument. Authentication and access restrictions support the agency’s working environment.
The output format matters commercially. The useful end of the workflow is the report Part & Sum can interpret and present, rather than a collection of raw API responses. Engineering connected research collection to the form in which Part & Sum actually delivers advice.
Ownership that is useful at the next handoff
Across the three streams, the source repositories belong to Part & Sum. Containers, deployable code and documentation give another engineer something concrete to inspect and run. Hosting ownership is separate: one deployed service uses Autonomous infrastructure, so source custody alone does not describe the whole handoff.
No end-client revenue improvement or headcount saving is claimed. The delivered result is a set of distinct technical capabilities that fit Part & Sum’s analytical and reporting work, with the partnership available to resume when a new project calls for it.
People behind the work
Questions and answers
Do you offer white-label software development for agencies and consultancies?
Yes. This engagement is a white-label example: we built reproducible analytics, a survey-data pipeline and a branded reporting platform behind Part & Sum’s own client relationship, with the source code owned by the agency. The partnership re-engages per project rather than becoming a permanent product team.
Can you work behind an agency’s own brand?
Yes. This case covers engineering supporting Part & Sum’s own client delivery, including branded research reports and presentation outputs.
Who owns the analytical method and code?
Part & Sum’s analyst supplied the core measurement logic. Autonomous added packaging and surrounding engineering, and the resulting repositories are held in Part & Sum’s organisation.
Does every stream need to become a SaaS product?
No. Here the outputs were a reproducible analytical tool, deployable data pipeline and shared agency research application, each suited to its own task.
Can the partnership restart for a specific project?
Yes. The relationship with Part & Sum remains open and re-engages per project, rather than being described as a continuous retainer or permanent embedded team.