Most organisations are not short of data. They are short of a clear answer to a specific question. Codexlava finds it in Python — pandas, NumPy and SciPy — and puts it in a Dash or Streamlit dashboard people actually open.
A dashboard nobody opens is a report nobody read, with extra steps. We build around the decisions you make each week.
Customer and market insight, R&D data and the weekly numbers: the same five kinds of work, in roughly this order.
Spreadsheets, exports and databases pulled together, de-duplicated and reconciled with pandas — the part most estimates underplay, and the part that decides whether anything after it can be trusted.
Trends, distributions and significance tests with NumPy and SciPy, so you know how confident to be — not just what the chart shows.
Filters, drill-downs and Plotly charts in a dashboard your team opens in a browser, with one clear question per view.
Small internal tools — upload a file, adjust a parameter, see the result — in front of users fast.
Reports refreshed from your live systems on a schedule, because a report that needs someone to refresh it by hand stops being refreshed.
Open-source, widely used and well documented — so the analysis is repeatable and whoever comes after us can pick it up.
pandas to load, clean, join and reshape tables; NumPy for the fast numerical work underneath it.
SciPy for statistical tests, distributions and the maths that says how much a difference actually means.
Dash for production dashboards with filters and drill-downs; Plotly draws the interactive charts inside them.
Streamlit for quick internal data apps and prototypes, written in plain Python.
The databases you run and the spreadsheets you actually use, connected rather than exported by hand.
Scheduled jobs that refresh the data and rebuild the reports, so the numbers are current when someone looks.
Containerised and hosted where it suits you, with access control for sensitive datasets.
We build in Dash and Streamlit. If a BI tool you already pay for would do the job, that is the cheaper answer and we will tell you.
| DashWhat we build | StreamlitWhat we build | BI toolsPower BI, Tableau | |
|---|---|---|---|
| Best for | Production dashboards with many users and custom layouts | Internal data apps and fast prototypes | Standard reporting on a mainstream data source |
| Custom analysis | Any Python: pandas, NumPy, SciPy | Any Python: pandas, NumPy, SciPy | Limited to the tool’s own functions |
| Speed to a first version | Quick, with more set-up | Fastest | Fast, for standard charts |
| Licensing | Open source (MIT) | Open source (Apache 2.0) | Typically licensed per user |
| Embed in your own app | Yes | Yes | Depends on the licence tier |
UK businesses say data skills matter and struggle to hire them, while the work itself has consolidated on Python — which is why we build there.
Sources: GOV.UK, Quantifying the UK data skills gap (May 2021); ONS, AI in UK businesses: 2023 to 2026 (businesses with 10+ staff); Stack Overflow Developer Survey 2025. Market context, not Codexlava results.
Each step has something you can look at before the next one starts.
Analysis without a question produces charts, not answers. We start with the decision you are trying to make.
Collection, cleaning and joining with pandas, which is usually the bulk of the work and the part that gets underestimated.
What the data supports, what it does not, and — with SciPy — how confident anyone should be about the difference.
A Dash dashboard or Streamlit app in front of the people who act on it, refreshing itself from your live systems.
Documentation of how the pipeline and the reporting fit together, so your own team can extend it.
If you are in the second column, we will say so on the first call rather than sell you something you do not need.
You approve each stage before the next one starts, and you own everything you paid for.
The decision the work has to inform, written down before any analysis starts.
Joined and reconciled with pandas, with the cleaning steps written down so they can be repeated.
What it supports, what it does not, and how confident anyone should be.
One clear question per view, connected to live systems where possible, with access control.
How the pipeline and the reporting fit together, so your team can extend it.
Dash and Streamlit compared: how each updates, layout control, deployment and the projects each suits, from a team that builds in both.
When off-the-shelf software is the right answer, when a bespoke build earns its cost, and the hybrid most UK businesses end up with.
How fixed-price and time-and-materials software contracts differ, where the risk sits in each, and how to tell which one suits your project.
Tell us the decision you are trying to make. We will come back to you on what the data can and cannot answer.
One call, no obligation. We will tell you what the work involves, roughly what it costs, and whether we are the right people to do it.