A production-ready econometrics app that makes your R or Python models accessible to consultants, analysts and executives. Multi-user, validation flow and regulator reporting. No longer a loose Shiny prototype, but an environment you can roll out, sell or scale up internally.
The models come from the minds of your quants: statistical time series, Bayesian priors, regression tree ensembles, sometimes reinforcement learning or agent-based models. But as soon as these models need to be used by someone who does not open an R prompt, the real problems begin: parameter validation, version control, authorisation per scenario, reproducibility of last month's run, and an audit trail that DNB or the AFM will accept.
We build econometrics apps for quant consultancies, policy and research institutes, insurers with an actuarial team, banks with PD/LGD/EAD models, pension funds with ALM questions, energy and utility companies, and advisory firms that deliver their scenario models white-label to clients. Not a clickable prototype that half does what it should, but a production environment that fits into your modelling, validation and governance workflow. For enterprise asset managers, we do not replace Domino Data Lab or Databricks; we build the specific links the platform does not cover, such as a client-facing model front end, a reporting engine for supervisors, or an API layer through which your own clients can consume the models.
The market below us — small research groups and individual consultants — relies on R Shiny, Streamlit or Dash. That works well for one researcher with one model. The market above us — large banks, Robeco, ASR — builds in-house on SAS, Domino or a bespoke JupyterHub stack. In between sits a group for whom Shiny is too limited and building from scratch is too expensive: mid-market quant consultancies, research institutes that want to open up their models more widely, and insurers or pension funds that want to take their actuarial tools to production-grade UX. That is our sweet spot.
In practical terms, we work for audiences such as quantitative consultancies that put R and Python models into production for clients; policy and research institutes in the style of CPB, NIBUD, RIVM or PBL that want to make their scenario models accessible to policy officers and stakeholders; insurers with an actuarial team working on Solvency II pillar calculations; banks that want to expose their PD, LGD and EAD models for IRB purposes; pension funds and implementing bodies facing asset-liability questions; energy and utility companies with demand and price forecasting models; advisory firms delivering impact assessments and scenario analyses; universities that want to make their research output self-service for partner institutions; B2B data providers that want to sell their models as a service; marketing and media agencies running media mix modelling; and sports analytics vendors working in the Hudl or Oodle context. Not every project calls for the same approach. An MMM platform for a media agency is a different beast from an ALM tool for a pension fund, but the underlying technology stack and governance challenges overlap considerably.
It depends on who uses the model and what governance applies to it. We advise which variant fits during the first conversation. The value often lies in combining a production-grade UI, an API layer and a sound compliance overlay.
Your quants have a model that works in a notebook or Shiny prototype, and you want to roll it out to internal users without them having to open RStudio. We take the script, refactor it into a clean module, build a React or Vue front end around it, and run the calculations in a cloud runtime. Includes parameter validation, scenario storage and exports to Excel or PDF.
For research teams and actuarial departments where several analysts work with the same models. Role-based access per scenario, model version management, a review workflow for who may publish models, and an audit trail that records every parameter change and every calculation. Suitable for IRB models, Solvency II pillar calculations or policy impact analyses where reproducibility is a hard requirement.
For consultancies and data providers that do not just use their models internally but sell them to clients. A REST or gRPC API with OAuth authentication, tenant isolation and rate limits, plus a white-label front end per client with its own branding. Suitable for media mix modelling agencies, sports analytics vendors, or B2B data providers that want to offer their model output as SaaS.
A production-ready econometrics app, plus the building blocks you need to manage, validate and further develop the models yourself.
Four patterns in which we guide clients. If you recognise one of them, we are happy to talk further. Building bespoke is not always necessary; sometimes a well-built Shiny or Streamlit extension is enough, and we will tell you so.
Your model has been running in a Shiny app for months, but the users are no longer just quants. Marketing, finance or the board want access too, and they drop off because of the interface, the loading times or the lack of mobile access. You need production-grade UX without giving up the R stack.
DNB, AFM, ECB or an internal model risk department wants to demonstrably see which model version produced which outcome, when, and who changed what. A notebook and an Excel export are not enough. You need version control, change logs and reproducible runs.
What worked for one analyst on a local laptop does not scale to ten analysts running scenarios at the same time on enterprise datasets. You want multi-user access, shared scenario libraries and compute that scales with demand, without every quant managing their own JupyterHub instance.
You are an advisory or consultancy firm and your model is your unique selling point. Clients pay for access. You then need a white-label frontend, tenant isolation, a clean REST API and an onboarding flow, not a separate Streamlit app per client.
Your model pulls data from a data warehouse, writes results back into a transaction system, and triggers workflows in your CRM or risk engine. A standard Shiny app with a CSV upload cannot do that. You need an application that lives as a first-class citizen in your enterprise architecture.
You want end users to be able to talk to the model: asking for an explanation of an outcome, getting anomalies summarised, or generating alternative scenarios through natural language. That calls for an LLM layer on top of your econometric core, with retrieval augmentation, prompt engineering and cost monitoring per query.
With OneDayBuild, we turn your idea into something tangible in one day for €1,150, so you can see whether further development is worth the investment. Decide to go ahead with the full build? Then we credit the full cost.
Explore OneDayBuild →We choose the stack that suits your model, your team and your governance, not the other way round. A brief overview of what we typically use and when a commercial package is the more honest option.
Python with FastAPI or Django for the API layer, R with Plumber for R-native models, and Julia with Genie where performance demands it. For the models themselves: scikit-learn, statsmodels, PyTorch and TensorFlow in the Python world; base R with tidyverse and the mixed-modelling packages in the R world. Bayesian stacks via Stan or PyMC, time series with Prophet or statsmodels, and scenario simulations sometimes in a custom Monte Carlo engine.
React or Vue for the production front end, with Plotly, Bokeh or D3.js for the model visualisations: interactive plots that show scenario output directly. Data sits in PostgreSQL for relational structures and ClickHouse for heavy analytical workloads, with Parquet or Arrow as the exchange format with the models. Compute runs on AWS Batch, Lambda or Cloud Run or, for longer-running models, on JupyterHub or Posit Workbench alongside the app.
R Shiny, Streamlit, Dash or Voilà for internal tools where multi-user governance isn't required. Tableau or Power BI if you only need visualisations without modelling. Domino Data Lab or Databricks if you already run a large enterprise data science platform and don't want to replace the UI layer. SAS, IBM SPSS, Stata or EViews for classic econometric workflows. MATLAB for engineering quant work. We're honest when one of these alternatives is the better fit: your budget and your team matter more than our revenue.
A conversation to understand which model is central, who will use it, which data flows go in and out, and which compliance context applies. We often ask your quants to demonstrate the model live in their current tooling, whether Shiny, a notebook or a SAS script.
A workshop with your team plus interviews with three to five end users. At the end: a concrete scope, technology choices (FastAPI versus Plumber, React versus Vue, compute backend), the first screen flow and a data architecture. Includes a decision between building and extending Shiny where that is the more honest option.
A working build every two weeks that you and your quants can test, as can end users. The model runs along in every sprint, so you can be certain the production output matches the notebook output, reproducible bit for bit. The first tier is ready after a few sprints.
Phased rollout to your user base, training sessions, and ongoing management of security, model drift detection and new model versions. Your quants can then push models through the pipeline themselves; we stay behind the scenes for the heavier refactors.
What clients usually want to know before we start.
A no-obligation introductory call of half an hour. We listen to your models, your users and your governance context, ask pointed questions, and give you direction you can act on, even if that direction is "stay with Shiny".
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