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KENNAN · DISPATCH · 2026.10.22 · NYC · LON · FRA

Use cases

Built for in-house finance, deal teams, and advisory.

One workspace for the work that ships every cycle — the actuals, the model, the deliverable. We meet finance teams where the work already happens: in the memo, in the IC pack, in the deck.

Who Kennan is built for

01
CFOs · finance directors
corporate development

The board pack, the budget cycle, the strategic memo.

Cycle work that used to span Excel, email, and a Google Doc lives in one workspace. The actuals get cleaned and consolidated. The budget runs as a model with sources cited. The board commentary writes alongside the numbers, not after them.

Deliverables
  • Monthly close → board pack
  • Budget and forecast cycle
  • Strategic memos for the audit committee
  • Acquisition diligence from the seller’s Box or Dropbox data room
02
Deal teams · investment committees
portfolio operations

The IC memo, the LP update, the portfolio review.

From the data room to the IC memo without the version sprawl. Kennan reads the CIM and the financials straight from Box or Dropbox; the model and the writeup live in the same workspace. The sensitivity grid is on the deliverable when the partner asks the “what if” — not in a follow-up two days later.

Deliverables
  • Buy-side diligence from a Box or Dropbox data room
  • IC memo from CIM
  • Add-on diligence and modeling
  • Quarterly LP update materials
  • Portfolio company quarterly reviews
03
Coverage · product
M&A advisory

The pitch book, the comp tables, the football field.

Sector research, comp sets, and valuation work happen in the workspace. The deck exports to .pptx for the client meeting. Multiple pitches in parallel without the version sprawl of a Friday-night drafting room.

Deliverables
  • Sector pitch books
  • CIM drafting from the client’s files in Box or Dropbox
  • Football fields, comp tables, valuation summaries
  • Fairness opinion supporting analysis

The work itself

Three steps. One workspace. Real output.

Across all three workflows, the work breaks down to the same three steps: clean and consolidate the data, build the model, produce the deliverable. Below, real output from a captured Kennan workspace — Klaviyo (KVYO), public filings.

§ 01 · Data cleaning + consolidation

The messy export becomes structured data, with sources cited.

An earnings press release table, a multi-sheet Excel actuals export, a PDF pulled from a Box or Dropbox data room — parsed, normalized, and joined into one schema. Every figure traces to a source. No retyping, no reconciliation between sheets, no “where did this number come from” on the eve of the board meeting.

Raw inputklaviyo-q4-press-release.pdf · p. 7
Selected Financial Data (in thousands, except per share)†
FY22FY23FY24FY25
Revenue472,718698,193937,5001,237,500
Cost of revenue123,402181,776230,891279,452
Gross profit349,316516,417706,137948,438
Operating loss‡(50,180)(45,910)(8,612)29,418
Adj. EBITDA(28,402)19,872120,418218,041
† Includes deferred revenue. ‡ GAAP. FY25 restated per 8-K filed Mar 12, 2026.
Workspace · sourced tablekvyo_financials.xlsx
FY22FY23FY24FY25
Revenue ($M)472.7698.2937.51,237.51
Cost of revenue ($M)123.4181.8230.9279.51
Gross profit ($M)349.3516.4706.1948.41
Gross margin (%)73.974.075.376.6c
Operating income ($M)(50.2)(45.9)(8.6)29.42
Adj. EBITDA ($M)(28.4)19.9120.4218.01
Adj. EBITDA margin (%)(6.0)2.812.817.6c
1 press release · 2 8-K restated · c computed7 rows · 5 columns · schema locked

Illustrative · Klaviyo FY22–FY25 actuals normalized from filings · every figure traces to its source

§ 02 · Modeling

The forecast runs as a program. The sensitivity is on the deliverable.

Assumptions become a stored Python program. Change a single input and every scenario reruns. The sensitivity grid is part of the output, not a separate sheet that someone has to maintain in parallel.

Assumptions5 inputs
FY25 revenue (anchor)$1,237.5Mfilings
Net revenue retention (NRR)110%driver
Customer growth FY2615% YoYtrend
FY28 EBITDA margin (base)20.3%derived
FY28 revenue growth (base)17% YoYtrend
revenue_forecast.py · stored program
5 scenarios emitted in one run
deterministic · re-runs on any assumption change
FY28 NRR sensitivity$ M
NRR 100%
NRR 105%
NRR 110%
NRR 115%
NRR 120%
Revenue
1,715
1,957
2,220
2,505
2,814
EBITDA
302
370
450
542
647
EBITDA mgn
17.6%
18.9%
20.3%
21.6%
23.0%
Each 5pp of NRR moves FY28 revenue by ~$275M. The deal thesis hinges on this row.

Captured Kennan workspace · KVYO forecast · one program, 5 scenarios, one run

§ 03 · Presentations

The chart, the table, the prose — formatted into the deliverable.

The workspace renders to .pptx or .xlsx. Charts and tables come out of the same data that produced the model — no re-typing, no chart-image-paste, no Friday-night reconciliation between the deck and the underlying numbers.

Pklaviyo-pitch.pptx— PowerPoint
FileHomeInsertDesignTransitionsAnimationsSlide ShowReviewView
Section IV · ScenariosConfidential · Draft

Scenario summary

Revenue build and FY28 EBITDA across the NRR sensitivity — base case 110%
Revenue forecast
$ millions · FY24A–FY28E
938
FY24A
1,238
FY25A
1,518
FY26E
1,822
FY27E
2,131
FY28E
ActualForecast
FY24A–FY28E CAGR: 22.8%
FY28 adj EBITDA · NRR sensitivity
$ millions · scenario marked
NRR 100% · bear
$302M
NRR 110% · base
$450M
NRR 120% · bull
$647M
$0M$200M$400M$600M$800M
Source: Company filings, Kennan workspace · as of 7/2/2026
12
Slide 12 of 24English (United States)100%

Illustrative slide layout · the numbers are the captured model's · renders to .pptx — chart, table, prose, no re-paste, no reconciliation

Bring a real deliverable. We’ll wire it up in one workspace.

A board memo, a CIM, a pitch draft — bring the actual work and we’ll show you how it runs in Kennan.

Try KennanTalk to the team