Research into how investment teams can move faster without losing rigour, and the hard questions that sit underneath it.
Ghostmap is an internal research and development programme exploring how investment and real estate teams assess decisions more consistently. It is research, not a product.
It is easy to make a tool that fills a template. It is far harder to make one an investment committee can trust. The difference is the reasoning underneath, and some of the hardest parts of that reasoning are still unsolved research problems. Project Ghostmap is where we study them, so the investment systems we deploy are built on solid ground rather than clever prompts.
A script repeats steps. It has no judgement of its own. The judgement has to be researched, then designed in.
Consistent data, trustworthy numbers and lasting context are genuine technical problems, not things a prompt solves.
Committee decisions have to be both fast and defensible. There is little room for output that only looks right.
Can a team move faster without losing rigour?
For most investment teams the constraint is not pipeline, it is capacity. Underwriting eats senior time, packs vary in quality between people, and the committee has to be able to defend every figure. Speed and rigour are usually treated as a trade-off. The research question is whether they can reinforce each other instead.
These are the foundations. None of them are fully solved, and most of the easy answers do not hold up under real deal conditions.
Every deal arrives differently: data rooms, PDFs, spreadsheets, emails, and no two are structured the same. Before anything can be reasoned about, that mess has to become consistent. The translation is more fragile than it looks.
How to turn varied, inconsistent source material into a clean, dependable structure a system can reason over, without losing the detail that changes a decision.
Consistency is the foundation of trust. If the inputs are not dependable, nothing built on top of them can be.
Language models are good with words, not arithmetic. Getting consistent, defensible figures out of an analytical model is a real research problem, and the obvious approaches drift.
How reasoning and computation can work together so a figure stays the same everywhere it appears and holds up to committee scrutiny.
An investment committee has to trust every number. Output that merely looks right is not good enough to sign off.
Most tools forget context the moment a session ends. A real investment decision evolves over weeks, across people, documents and changing assumptions.
How a decision can carry its assumptions, reasoning and history over time, so context never has to be rebuilt from scratch.
Less time re-explaining the deal, more time on the judgement that actually moves it. Memory is where rigour compounds.
When a number cannot be traced back to where it came from, it cannot really be trusted, and rigour quietly erodes under time pressure.
How to keep a clear, automatic line from any figure or claim back to the document and assumption behind it.
Traceable work is defensible work. It is what lets a team stand behind a recommendation with confidence.
Project Ghostmap is early-stage applied research. We share the questions we are working on, openly. The methods we develop to answer them stay in the lab, because that work is what will power what we do next.
It is reshaping which parts of underwriting are manual and which are not, faster than most teams can adapt.
Deal flow rewards teams that can assess more of what matters, sooner, without adding headcount.
Committees and investors expect more rigour and clearer evidence, not less. Both pressures bite at once.
The point of the research is not the research. It is to make the systems we deploy genuinely dependable: built on consistent data, figures a committee can trust, and context that lasts. When the hard problems beneath a decision are taken seriously, the system stops being a clever assistant and starts being something an investment team can actually rely on.
See how investment teams use us →The questions on this page do not stay on the page. They feed the reasoning layer of the operational systems we deploy, beneath the work a team actually uses.
Every finding is sourced, scored, explained and ends in a recommendation a committee can defend.
Project Ghostmap is open research into the reasoning beneath faster, defensible investment decisions. We publish the questions we are wrestling with, the patterns that recur across real deployments, and the principles that hold up under pressure. We do not publish client data, or the parts of the method still being worked out. It is research, not a product: the questions are the interesting part, and the answers are earned in the work.
We work on the hard problems first, so the systems we deploy hold up where it counts.