Debt Advisory Term Sheet Search AI: Find Precedent Deals Fast
Debt Advisory Term Sheet Search AI: Find Precedent Deals Fast
A debt advisory partner was mid-negotiation on a £4.2M development finance deal.
The borrower pushed back on the arrangement fee.
The advisor KNEW he'd seen a comparable structure six months ago. Similar asset class. Similar LTV. Similar developer profile. He needed that term sheet to hold the line.
He spent 47 minutes looking for it.
It was in a folder called "2025 Q3 Misc."
Sound familiar?
The Hidden Cost of a Messy Term Sheet Library
Here's the thing: debt advisors are not disorganised people.
You know your deals. You know your lenders. You've got years of transaction history sitting in your files.
The problem is that history is LOCKED.
It's locked in PDFs named after whatever you felt like typing that afternoon. Locked in deal folders that made sense at the time. Locked across three different drives, your inbox, and possibly a USB stick from 2022.
McKinsey research shows knowledge workers spend nearly 20% of their working week just searching for information. For a debt advisor handling five active mandates, that's a day a week. Every week.
And unlike a solicitor or accountant, you're not billing for that time.
You're just bleeding it.
The specific pain for debt advisors isn't general document management. It's this moment: "I've seen this before - where the hell is that term sheet?"
Comparable LTV structures. Lender appetite for specific development types. Arrangement fee benchmarks. Senior debt pricing on schemes with planning risk.
That institutional knowledge lives in your files. But when you need it, you can't get to it.
What Debt Advisory Term Sheet Search AI Actually Does
I'm not talking about a chatbot.
Not a SaaS tool with a 100-seat minimum and an enterprise contract.
What we're building at Oloxa is a system that makes your existing deal library searchable in plain English.
Think of it as Google for your term sheets.
You ask: "Show me all deals where senior debt was priced below 7.5% on residential schemes over £3M in the last two years."
The system searches across every term sheet, deal memo, and lender correspondence you've ever saved.
It returns the actual documents. With the relevant sections highlighted.
In seconds.
The wild part? You're not changing how you work. You're not adopting a new platform or training your team on new software. Your files stay exactly where they are. You just get to find things in them.
That's Phase 2 of what we do at Oloxa. Phase 1 is automating the paperwork - document collection, classification, assembling data rooms. Phase 2 is making everything you've collected searchable with AI.
For debt advisors, Phase 2 is where it gets genuinely useful.
Debt Advisory Term Sheet Search AI: Find Precedent Deals Fast
A debt advisory partner was mid-negotiation on a £4.2M development finance deal.
The borrower pushed back on the arrangement fee.
The advisor KNEW he'd seen a comparable structure six months ago. Similar asset class. Similar LTV. Similar developer profile. He needed that term sheet to hold the line.
He spent 47 minutes looking for it.
It was in a folder called "2025 Q3 Misc."
Sound familiar?
The Hidden Cost of a Messy Term Sheet Library
Here's the thing: debt advisors are not disorganised people.
You know your deals. You know your lenders. You've got years of transaction history sitting in your files.
The problem is that history is LOCKED.
It's locked in PDFs named after whatever you felt like typing that afternoon. Locked in deal folders that made sense at the time. Locked across three different drives, your inbox, and possibly a USB stick from 2022.
McKinsey research shows knowledge workers spend nearly 20% of their working week just searching for information. For a debt advisor handling five active mandates, that's a day a week. Every week.
And unlike a solicitor or accountant, you're not billing for that time.
You're just bleeding it.
The specific pain for debt advisors isn't general document management. It's this moment: "I've seen this before - where the hell is that term sheet?"
Comparable LTV structures. Lender appetite for specific development types. Arrangement fee benchmarks. Senior debt pricing on schemes with planning risk.
That institutional knowledge lives in your files. But when you need it, you can't get to it.
What Debt Advisory Term Sheet Search AI Actually Does
I'm not talking about a chatbot.
Not a SaaS tool with a 100-seat minimum and an enterprise contract.
What we're building at Oloxa is a system that makes your existing deal library searchable in plain English.
Think of it as Google for your term sheets.
You ask: "Show me all deals where senior debt was priced below 7.5% on residential schemes over £3M in the last two years."
The system searches across every term sheet, deal memo, and lender correspondence you've ever saved.
It returns the actual documents. With the relevant sections highlighted.
In seconds.
The wild part? You're not changing how you work. You're not adopting a new platform or training your team on new software. Your files stay exactly where they are. You just get to find things in them.
That's Phase 2 of what we do at Oloxa. Phase 1 is automating the paperwork - document collection, classification, assembling data rooms. Phase 2 is making everything you've collected searchable with AI.
For debt advisors, Phase 2 is where it gets genuinely useful.
Three Moments Where This Changes the Deal
1. Rate benchmarking mid-negotiation
You're talking terms with a lender and the rate feels off. You want to sense-check it against comparable structures. With a searchable term sheet library, that check takes 20 seconds. Without it, you either go from memory or drop the call to dig through folders.
Going from memory is how advisors lose negotiating credibility.
2. Building bank submission packages
Assembling a lender pack for a new mandate always involves referencing old deals. What structure did you use for that hotel conversion last year? What did that bridging lender accept on exit terms?
Right now, building those references in takes 2-3 hours of archaeology.
With AI document search, it takes a query.
3. New partner onboarding and deal review
When a new advisor joins your team, their entire learning curve is your deal history. How you've structured deals. What lenders you've worked with. What terms you've achieved on different asset classes.
That's a knowledge transfer problem that most firms solve with shadowing and hoping for the best.
A searchable deal library turns your firm's institutional knowledge into something that actually transfers.
Why Enterprise Tools Don't Work for Independent Debt Advisors
You've probably seen the platforms targeting larger firms. Hebbia. Datasite. Tools built for M&A teams at bulge brackets.
They're IMPRESSIVE.
They're also £600 to £10,000 a month with minimum seat requirements and implementation timelines measured in quarters.
The independent debt advisor - running 15 to 30 active mandates a year, a lean team, and a deal library built up over a decade - doesn't fit that model.
You need something custom-built for your document types, your volume, and your workflow.
That's what we build. Not a SaaS tool. A professional service. We assess your data, we build the ingestion pipeline, we configure the search layer, and we make your specific term sheets, deal memos, and lender correspondence queryable.
We co-develop the retrieval architecture with JT, who has seven years of enterprise-grade document retrieval experience at scale. The quality of what you get isn't enterprise-lite. It's enterprise-grade, built for your firm's size and budget.
I'll be honest: this isn't a plug-and-play weekend installation. It takes a few weeks. But you end up with something that compounds every time you close a deal, cause every new term sheet you save makes the library richer and the search better.
Frequently Asked Questions
What types of documents can debt advisory term sheet search AI handle?
The system handles any document format your firm already uses - PDFs, Word documents, Excel models, email attachments, scanned documents. Term sheets, deal memos, lender correspondence, bank submission packages, property valuations, and planning documents are all searchable. Documents stay in your existing storage. The system indexes them and makes them queryable in plain English.
How is this different from just using Ctrl+F or a keyword search in Google Drive?
Keyword search finds exact matches. AI document search understands meaning. If you search for "residential development deals with planning risk," it finds relevant term sheets even if those exact words don't appear together. It also synthesises across multiple documents - so you can compare terms across ten deals simultaneously rather than opening files one by one.
Do I need to change how my team saves documents?
No. The system works with your existing folder structure and naming conventions. There's a short data assessment at the start where we inventory what you have and where it lives. After that, new documents you save are indexed automatically. Your workflow doesn't change.
What's the realistic time saving for a debt advisory firm?
Based on IDC research, knowledge workers lose between 2.5 and 5 hours per week searching for information. For a debt advisor with a substantial deal library, the saving is concentrated in specific high-value moments: rate benchmarking, building submission packs, and precedent-finding during negotiations. Those moments add up. Across a team of three advisors, reclaiming even two hours each per week is meaningful when your time is billed or it's compounding deal quality.
Is my deal data secure?
Your documents don't go anywhere. The indexing happens in your own infrastructure. We don't store or access your deal data beyond the initial setup. This is a critical point for regulated firms, and we build accordingly.