The Platform Engineering Approach to VC

In his post, The Full Stack Venture Capitalist, Roberto outlined the three types of venture firm when it comes to the application of a technology strategy to their organisation. These different types of organisational design need to be aligned to the firm’s investment strategy and require different technology approaches.

For a Full Stack VC like InReach Ventures, having data and technology at the centre of your strategy and organisation means you have to re-think how you ‘do’ VC from the ground up, even your organisational structure and culture. You can’t approach this as a data problem; it forces you to employ a Platform Engineering Approach.

What is the Platform Engineering Approach?

Platform Engineering enables product developers and data scientists to spend their time on what delivers value to the business. It’s about building an automatically managed platform that allows them to do what they need in a self-service manner, without having to worry about security, scaling, or infrastructure concerns.

This isn’t as simple as signing up to a cloud provider or choosing Kubernetes, nor does it mean that Platform Engineering is completely separated from application development. At its core, Platform Engineering requires a deep understanding of the building blocks of the business, and the principles on which they should be architected, only then can you provide them as services to your developers.

Translating this to InReach as a Full Stack VC, the Platform Engineering Approach requires rethinking the venture capital value chain to have data and technology at its core, across three logical architectural layers: Data, Intelligence, and Workflow.

Live Data Platform

The Platform Engineering Approach applied to the Data layer maximises the capabilities of the platform through coverage, depth and freshness. For InReach and our platform DIG, this can only be achieved through being a Live Data Platform that is architected for extensibility (adding any data source is trivial) and is constantly, automatically growing.

The European startup ecosystem is fragmented; companies and founders can pop up all over the continent. For later-stage VC firms looking to harness data to support their investment process, a subscription to Pitchbook, Crunchbase or Dealroom is more than enough. For InReach, investing in early-stage founders, the goal is to find the next Spotify before anyone else has heard of them, and at this stage the fragmentation carries on to deal-sourcing through data.

DIG’s Data layer is where the rubber hits the road, data-engineering-wise. It gathers information from hundreds of different data sources of all shapes and sizes, tracking constant updates for millions of companies and founders.

The Constantly Scoring, Extensible Intelligence Layer

The Platform Engineering Approach applied to DIG’s Intelligence layer is about architecting around two principles:

Constant scoring, constantly learning

This is needed for the alignment of the Data and Intelligence layers. There’s no point having a Live Data Platform if it takes a month to re-score the updated data. As important is the concept of constant learning and creating a virtuous cycle between the Data, Intelligence and Workflow layers to constantly improve training data and models.

Extensibility and composability

In the same way that it needs to be trivial to add data sources to the Data layer, the Intelligence layer has to be architected so that new algorithms, models and AI technologies can be plugged in and provide meaningful results quickly. Rather than having one monolithic ML model to find great European startups, DIG is built around combining different models, techniques and technologies, all for discovery.

This Platform Engineering approach to the Intelligence layer is also directionally sound. The trend towards larger and larger language models is pushing us to tackle data science as an engineering discipline. As LLMs become more accessible, affordable, and perform well on a wide range of zero-shot learning tasks, the challenges of the Intelligence layer shift from traditional data science (model creation and evaluation) to purely engineering and implementation tasks.

Automating the Workflow Layer

Across the VC value chain, applying the Platform Engineering Approach to the Workflow layer means looking at what is traditionally done at each stage and rethinking how to productise and automate it. For an early-stage European VC like InReach, the focus is on discovery.

Building on the Live Data Platform and the constant scoring of the Intelligence layer, the goal for the Workflow layer is to build an automatic, self-populating CRM. Reaching out to and engaging with entrepreneurs from across Europe is the last piece needed to achieve DIG’s stated goal: talking to the next Spotify before anyone else knows they exist.

Conclusion

Being a Full Stack VC necessitates taking a Platform Engineering approach to the logical architectural layers of Data, Intelligence and Workflow, applied to each stage of the VC value chain.

At InReach we are obsessed with finding the next Spotify before anyone else. For this reason, DIG’s focus since the beginning has been one thing: discovery. As a result, InReach’s Platform Engineering Approach is key to solving this strategic challenge, through the architectural themes of live, constant and automatic, and extensible and integrable.