Kohort raises £5.15m to automate campaign optimisation using predictive lifetime-value models
This article covers Kohort, a London-based gaming startup, which has raised £5.15m in a series A funding round led by The Raine Group to build user acquisition agents for mobile game studios. The funding will support development of an agent suite to automate campaign optimisation, on-demand research and reporting using predictive lifetime value models, aimed at mobile game studios and user acquisition teams.
Kohort, a London-based gaming startup, has raised £5.15m (about $7m) in a series A funding round led by The Raine Group to build user acquisition (UA) agents for mobile game studios. The funding will support development of an agent suite that aims to automate campaign optimisation, on-demand research and reporting using predictive lifetime value models.
Why it matters
User acquisition is the largest recurring cost for many mobile game studios and a locus of operational complexity: ad networks change rapidly, short-term return-on-ad-spend conflicts with long-term player value, and teams often lack fast, reliable forecasting. Kohort is pitching its agents as a way to bring more accurate, campaign-level prediction into those decisions. The company says its models were trained on £4.4bn of historical UA spend and deliver daily campaign-specific predictions with 95% accuracy — claims that, if realised in customer deployments, could materially change how studios allocate UA budgets.
Inside the product
Kohort’s agent suite centres on three capabilities:
- Campaign optimisation via Ktrl: a network-specific bidding and targeting engine that supports ROAS, CPI and CPE/CPA campaign types and connects directly to ad networks. Kohort positions Ktrl as a tool to push campaigns towards higher ROAS while accounting for network behaviour and long-term value trade-offs.
- Deep research: an on-demand research function that cross-references a studio’s historical data with benchmarks drawn from the c.£735.4m in annual spend Kohort says flows through its platform. The feature is designed to replicate the investigative work of a senior UA manager at scale.
- Automated reporting: customised decks and reports intended to provide a single source of truth across CEO, product, LiveOps and UA teams.
Kohort also highlights integration and speed: its platform integrates with existing data warehouses and, according to the company, trains client-specific models in under 20 minutes.
Who are the investors?
The round was led by The Raine Group, which previously participated in Kohort’s seed round and has operated a commercial partnership with the company over the past nine months. The Raine Group is a global merchant bank and growth investor focused on TMT sectors; it has offices in New York, London and other global hubs.
In the announcement, John Salter, Partner and Co-Founder at The Raine Group, said:
Kohort has a clear pathway to become a category-defining platform in UA Agents, and solve the biggest problem in mobile apps today. The Kohort team is lean, AI-native and delivers high quality results at speed. We believe that Kohort’s predictive LTV capabilities and UA optimization technology provide the building blocks for future partnership opportunities, including the potential for a new product around user acquisition financing.
The press materials do not list additional named participants in the series A. Raine’s continued backing and commercial ties signal both financial and strategic interest in Kohort’s predictive LTV approach and potential adjacent products.
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Founder perspective
In the announcement, Dan Marcus, CEO at Kohort, said:
We are building agents - not just a Claude wrapper - and the predictions underneath them, to make that possible. User acquisition is one of the most critical and difficult operations for a mobile gaming studio. The best UA teams operate more like high-frequency traders than marketers, and they need agents that act on real context, not vague signals.
Marcus frames Kohort’s work as moving beyond signal-level automation toward context-rich prediction that can be actioned programmatically across ad networks.
The bigger picture
Kohort’s raise arrives as a wave of AI-native tools targets operational bottlenecks across games and consumer apps. Automating UA — an area historically dominated by human judgement and manual optimisation — is a natural test case for agent-style systems because it interfaces directly with ad platforms, player economics and finance. For investors and studios watching unit economics closely, predictive LTV modelling combined with automation is an attractive proposition.
For the UK and European ecosystem, the deal underscores ongoing investor appetite for applied-AI startups in gaming infrastructure and analytics. If Kohort can substantiate its accuracy and deliver integration at scale, it may influence how studios budget for UA and how investors underwrite growth in mobile gaming companies.
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