Piloting with Tier-1 global banks

Governed Deal Intelligence
for Investment Banking

Every analyst now has an AI tool. None of them are governed. None of them compound. Deal teams run GPT, Copilot, and Bloomberg in parallel — generating output that gets used once and discarded. Kaylo is the structured mandate layer that changes that: every step traced, every output sourced, every closed deal making the next one sharper.

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The status quo

Three things true of
every deal team today

Not edge cases. The default operating model at every bank running mandates right now.

01
The first days of every mandate are assembly Hours spent pulling the last sector note, locating precedents from a deal that closed two years ago, finding the right client contact history. Context the firm already owns — reassembled from scratch because nothing was captured where anyone could find it.
02
The MD corrects it. The correction disappears. Every senior edit is a signal: the right framing for this client, the narrative standard for this sector, the instinct that took years to develop. It lives in an email reply or a verbal markup. The next team starts blind.
03
AI generates. Nobody can govern it. Every analyst has access to AI. Most output gets taken offline, revised in PowerPoint, and sent without attribution. Compliance has no visibility. The firm has no lineage. Nothing learned. Session closed.

Kaylo is built around all three.

The approach

Other tools give you an answer.
Kaylo runs the mandate.

The distinction isn't speed. It's architecture. When you ask an AI tool for a proposal, you get output. When you run a mandate through Kaylo, you get a governed workflow — the whole team in one workspace, every source attributed, every MD correction captured, every closed deal making the firm measurably smarter.

How other AI tools work
Analyst uploads the brief and asks for a proposal draft
↓
Receives 40 pages in 90 seconds. Spends 3 hours editing in PowerPoint.
↓
MD rewrites it. Analyst takes corrections offline. Deal closes.
↓
Nothing learned. No lineage. No governance. Session closed.
How Kaylo works
Ingest — every source tagged by deal, team, sector
↓
Structure — context assembled under access controls
↓
Draft — centrally governed AI, not open user prompts
↓
Review — MD iterates in-system with full lineage
↓
Export — bank-standard PPT/PDF, source-attributed
↓
Compound — every approved decision feeds firm memory

The difference isn't AI — every platform has AI. The difference is what happens before the first prompt and after the last slide: the governed layer that turns every mandate into compounding firm intelligence.

Days → Hours
Proposal turnaround on the first live transaction run through Kaylo — with full source lineage from day one.
0 blind spots
Every number, claim, and narrative in a Kaylo proposal carries a traceable source — no unattributed outputs, no compliance exposure.
↑ Every deal
Quality compounds with every closed mandate. The firm's memory layer grows. The next proposal starts with everything the last one earned.
The workflow

From mandate intake
to MD-ready in hours

A single structured workspace. Every step governed, every source attributed.

Kaylo — Helvex Pharma AG · M&A Advisory
Ingest
Structure
Draft
Review
Export
Sections
Exec. Summary
Situation Overview
Valuation Analysis
Transaction Structure
Why Now
Executive Summary AI Drafting
Helvex Pharma AG presents a compelling acquisition target in the European life sciences mid-cap segment, with a differentiated CNS pipeline and an 18-month revenue CAGR of 24%.
Bloomberg M&A Sector Brief Q4 6 precedents
See the full workflow
The compounding moat

Every deal teaches
the next one.

Kaylo writes accepted AI outputs, MD edits, and outcome signals back into the bank's own data layer — with full source lineage. That corpus belongs to the firm. It grows with every mandate. It cannot be replicated by any vendor.

01
All deal activity, ingested
Transcripts, emails, models, and historical decks unified into a structured deal foundation with full source tagging.
02
AI drafts. Humans decide.
Every accepted output and MD correction tagged at source level — not just what changed, but why it changed.
03
Written back. Owned by the bank.
Decision rationale and accepted outputs feed the firm's own corpus — not a vendor's model.
04
Sharper every mandate
Narrative quality, client mirroring, and precedent depth improve with every closed deal. The moat deepens from within.
Deal data foundation
Transcripts
Emails
Financial models
Precedent decks
Structured deal workspace
Source-tagged Full lineage
Human-in-the-loop capture
AI Draft
MD Edit
Accepted
"Reference Q3 precedent, not sector average — and sharpen the opening."
Rationale captured · Source: MD comment · Deal 47
Edit reason tagged MD preference logged
Writing to firm corpus
Accepted executive narrative
Deal 47 · Tech M&A · Full lineage
→ CORPUS
MD valuation preference
Preference profile · Precedent-grounded
→ CORPUS
Decision rationale
Source-linked · Bank-owned
→ CORPUS
Not vendor-retained Firm-owned permanently
Narrative quality score · deals completed
Deal 1
18%
Deal 5
42%
Deal 15
68%
Deal 40+
92%
Moat deepens with use Unreplicable by any vendor
Workflow tools Own the UI — not the reasoning. Transactional data only. No capture of institutional judgment or the why behind every decision.
Model vendors Provide raw capability — not firm-owned memory. The bank uses the model. The model learns nothing specific to the bank.
Point AI tools Deliver answers in silos — not institutional compounding. Every accepted augment disappears. Nothing feeds the next deal.
Built for regulated environments

Designed with risk and compliance
in the room.

The questions your legal, compliance, and security teams will ask — answered before the pilot conversation begins.

Where does our data live?
Deployed within your own infrastructure. Kaylo never holds raw deal data on shared cloud. Tenant isolation is a hard architectural constraint, not a configuration option.
How does this handle MNPI?
Kaylo does not retrieve or surface data across information barriers. Context is scoped to the authorised deal team and governed explicitly at query time. No cross-wall retrieval.
Which AI model does this use?
Model-agnostic. You choose the LLM — including private or on-premise deployment for banks with data residency requirements. No dependency on a single provider.
Can compliance review before anything ships?
Yes. Every AI-generated output passes through a configurable approval state before export. Full audit trail with source attribution and user action log at every step.
Who owns the outputs?
The bank. Kaylo does not use client data to train models or share data across tenants. Outputs are governed by your firm's data policies, not ours.
What is the integration footprint?
Kaylo operates as a read layer over your existing systems — no write access to source data. Pilot scope is explicitly bounded, auditable, and reversible from day one.

The governed intelligence layer
for the next era of deal execution.

Piloting with Tier-1 global banks in 2026. One mandate. No enterprise commitment. The evidence makes the case.

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