We develop AI capabilities for knowledge management for urban, social, and economic development. We organize information so planners, decision-makers, and analysts can work more effectively. Our tools integrate documents, analysis, spatial data, stakeholder engagement, and reporting into unified workflows that support evidence-based planning and participatory decision-making across the project lifecycle. We focus on contexts where technology can meaningfully expand capacity for teams working in health, infrastructure, environmental, and social development.
Our tools are built by a team with deep expertise spanning multilateral urban development, humanitarian field research and evaluation, and software engineering with a focus on health technology and quantitative analysis. We emerge from the urban development, humanitarian and technology sectors, bringing firsthand understanding of real-world workflows and challenges to the products we build.
Team
Ayesha Khalil
Co-founder
Multilateral urban development specialist with experience across international agencies on sustainable infrastructure initiatives. Leads strategy and partnerships.
Jonah Rudlin
Co-founder
Urban and humanitarian development research specialist with field research and multi-national evaluation experience. Leads product and AI agent design.
Kieran Tilley
Co-founder
Software engineer with background in quantitative analysis and health technology. Leads architecture, engineering, and security.
Origins
Layered began based on a series of ongoing conversations between the co-founders on how new technologies could be made useful within the urban development and humanitarian sectors.
Advent of LLM technology
With the release of OpenAI's first Large Language Model, we recognised the huge benefits it could bring to ordering and analysing the large amounts of information needed in planning infrastructure and upgrading sustainably. We developed our first pilot with the Mayor of London using public social media data as a proxy for wellbeing at a city scale.
Partnership phase
With the improvement in LLMs, agent-chain technology became viable for automating complex tasks, beginning our partnership phase with multilaterals to build custom automated agent chains.
Going forwards
We aim to integrate learnings from our partnerships into a broader platform that will scale and increase the accessibility of proven agent chains to more organizations, whatever their size.
Contact
Interested in partnership? We're seeking decision-makers and teams ready to co-create custom AI capabilities for evidence-based planning and project intelligence.