Finance
Data Agent for Finance

Your analyst desk,
powered by AI.

Upload financials. Set assumptions in plain language. Get DCF valuations, comp tables, sensitivity matrices, and M&A models — without rebuilding the same spreadsheet for the hundredth time.

Try free with your data See what it does
The problem

Financial modeling is 80% plumbing, 20% judgment.

The formulas are well-known. FCFF, WACC, terminal value — every analyst learns them in week one. The time sink is wiring cells, checking cross-references, formatting outputs, and rebuilding the same model for every new target. The judgment is the 20% that matters. The plumbing is the 80% that takes all night.

01

Excel is powerful, but fragile

One broken cell reference and the whole model is wrong. Circular references are expected. Version control is "Final_v3_REAL_final.xlsx". The tool is 40 years old and it shows.

02

Bloomberg is expensive, but rigid

$24K per seat per year. Powerful for data, but the analysis layer is fixed templates. Custom modeling still requires exporting to Excel and building from scratch.

03

ChatGPT knows the theory, but forgets the data

Ask it to run a DCF and it will — on made-up numbers. No persistent data, no real computation, no dashboards. The conversation resets every session.

How it works

From raw financials to valuation in three steps.

1

Upload

Bring your data in any format — Excel exports from Wind or Capital IQ, CSV downloads, or just type the numbers into the conversation. The agent reads column headers, maps fields, and stores everything in a persistent database. Your data stays between sessions.

Excel / CSV Wind export Capital IQ Manual input Database
2

Model

Tell the agent what you want. It knows the standard IB workflows — DCF, comps, merger model, LBO screening — and walks you through assumptions instead of making you wire formulas. Change any parameter and the model reruns instantly.

"Run a DCF with 12% revenue growth declining to 8%" "What's the implied valuation at sector median multiples?" "Is this deal accretive at a 20% premium?"
3

Keep

Every analysis becomes a reusable asset. Save the DCF as a dashboard that updates when you refresh the data. Save the comp table as a script you can rerun on the next target. Save the sensitivity matrix as a report your team can reference.

Valuation dashboard Reusable model script Scheduled market report Alert / watch
In action

Two conversations. Two real outcomes.

These are the kinds of questions IB analysts actually ask — and the kind of answers that used to take an afternoon in Excel.

14:30 · Tuesday afternoon
Sarah Chen
Associate at a mid-market
investment bank

Sarah just received a mandate to value a SaaS target for a potential acquisition. The MD wants a preliminary DCF by end of day. She uploads the target's last 3 years of financials and starts the conversation.

What she keptA DCF dashboard with live sensitivity matrix, reusable for the next target.
project-alpha · target valuation
Tuesday · 14:30
Sarah Chen
I uploaded the target's financials. Run a DCF — revenue growth starting at 15% declining to 8%, EBITDA margins stable at 27%, WACC 10%, terminal growth 3%.
target_financials.xlsx · 3 years · revenue, EBITDA, CapEx, NWC
Tablize
✓ sql.query · read target_financials, 3 years x 4 metrics 142 ms
✓ python.analyze · DCF: FCFF projection + PV + terminal value 1.2 s
Based on your assumptions, here's the DCF output:
2026E2027E2028E2029E2030E
Revenue8059019911,0701,136
EBITDA217243268289307
FCFF148168186201214
PV of FCFF135139140137133
Enterprise Value: 2,834M
PV of projected cash flows: 684M
PV of terminal value: 2,150M (75.9% of EV)
Implied EV/EBITDA: 13.1x on 2026E
Sarah Chen
Terminal value is 76% of EV — that's high. Show me the sensitivity matrix: WACC 8–12%, terminal growth 2–4%.
Tablize
✓ python.analyze · sensitivity matrix 9x5 680 ms
Sensitivity matrix (Enterprise Value in M):
WACC ↓ ⁄ g →2.0%2.5%3.0%3.5%4.0%
8.0%3,8124,1024,4384,8425,345
9.0%3,0583,2583,4923,7704,105
10.0%2,5302,6712,8343,0273,258
11.0%2,1422,2432,3602,4962,658
12.0%1,8471,9222,0072,1042,216
Base case (10% WACC, 3% growth) highlighted. The range is 1,847M to 5,345M. At the sector-standard WACC band of 9–11%, the range narrows to 2,142M–3,492M.
09:15 · Monday morning
James Liu
VP at a growth equity fund
evaluating SaaS targets

James screens 15–20 potential targets per month. For each one, he needs a quick comparable analysis against public peers. He used to spend 30 minutes per target in Excel. Now he uploads a comp set once and reruns it on each new target.

What he keptA reusable comp script that runs on any new target in under 60 seconds.
deal-pipeline · comp analysis
Monday · 09:15
James Liu
Here are 8 SaaS comps with market cap, EV, revenue, and EBITDA. Calculate all standard multiples and tell me the implied valuation range for a target with 700M revenue and 190M EBITDA.
saas_comps.csv · 8 companies · 4 metrics each
Tablize
✓ sql.query · load 8 comps x 4 metrics 98 ms
✓ python.analyze · multiples + descriptive stats + target implied range 540 ms
Trading multiples for your peer set:
MetricMeanMedian25th75th
EV / Revenue5.8x5.2x4.1x7.0x
EV / EBITDA18.4x16.8x14.2x21.5x
P / E32.1x28.5x22.0x38.6x
Implied valuation range for your target (700M revenue, 190M EBITDA):
EV/Rev
2,870M – 4,900M
EV/EBITDA
2,700M – 4,085M
At median multiples: EV = 3,192M–3,640M. The EBITDA-based range is tighter and more conservative.
Capabilities

Six things every deal team needs.
All through conversation.

No modules to buy. No templates to wire. Tell the agent what you need — it builds the model, runs the analysis, and formats the output.

01
DCF valuation

From historicals to enterprise value in one conversation.

Upload 3–5 years of financials. The agent walks you through assumptions — revenue growth, margins, CapEx, WACC — computes free cash flow year by year, discounts to present value, adds terminal value, and delivers an enterprise value with a full sensitivity matrix. Change any assumption and the model reruns in seconds.

Try this prompt "I have 3 years of financials for a target company. Help me run a DCF with WACC around 10% and see how the valuation changes if revenue growth drops to 8%."
python.analyzesql.queryapp.dashboard
02
Comparable analysis

Peer set, multiples, and valuation range — without the spreadsheet.

Provide a list of comparable companies with their financials. The agent batch-computes EV/EBITDA, P/E, P/S, and EV/Revenue, then gives you median, mean, 25th and 75th percentile. Apply the range to your target and get a valuation bracket. Add or remove comps and the range updates instantly.

Try this prompt "Here are 8 comparable companies with market cap, revenue, and EBITDA. Calculate trading multiples and give me the implied valuation range for my target at 700M revenue."
python.analyzesql.queryapp.dashboard
03
Sensitivity analysis

The matrix your MD actually reads.

Pick any two assumptions — WACC and terminal growth rate, revenue growth and margin, entry multiple and exit multiple — and the agent generates a two-dimensional sensitivity table. Rendered as a heatmap dashboard with the base case highlighted. Change the parameter ranges and it rebuilds in seconds.

Try this prompt "Generate a sensitivity matrix for my DCF: WACC from 8% to 12% in 0.5% steps, terminal growth from 2% to 4% in 0.5% steps. Highlight the base case."
python.analyzeapp.dashboard
04
Quick multiples

The back-of-envelope calculation, instant.

In the middle of a meeting, someone asks "if revenue is 500M and the sector trades at 8x, what's the EV?" You type it. The agent answers. No spreadsheet, no calculator. It handles EV-to-equity bridge, diluted share count, and net debt adjustment if you provide them.

Try this prompt "Revenue is 700M, sector median EV/Revenue is 4.2x, net debt is 120M, diluted shares 50M. What's the implied share price?"
python.analyze
05
M&A accretion / dilution

Will the deal create or destroy value for shareholders?

Model an acquisition: provide acquirer and target financials, deal structure (cash, stock, or mix), and financing terms. The agent computes pro-forma EPS for 3 years, shows whether the deal is accretive or dilutive at various exchange ratios, and identifies the breakeven synergy level.

Try this prompt "Acquirer has EPS of $2.50, target earns $80M net income. Model an all-stock deal at a 25% premium. Is it accretive in year 1? What synergies make it accretive?"
python.analyzesql.queryapp.dashboard
06
Automated reports

The weekly market update that writes itself.

Weekly sector performance, monthly portfolio review, quarterly comp updates — configured in one conversation, delivered on schedule. The agent pulls your data, computes the metrics, formats the output, and sends it. No BI tool. No analyst spending Friday afternoon on formatting.

Try this prompt "Every Monday at 8 AM, generate a report comparing my watchlist companies' latest trading multiples to their 52-week averages. Flag any that moved more than 1 standard deviation."
script.savejob.schedulereport.save
How it
compares

Not a replacement for Excel.
The analysis layer that makes you faster.

Tablize doesn't replace your pitch book or your formatting skills. It handles the computation and iteration — the part that takes all night.

Tablize Excel Bloomberg BI Tools
Setup time Same day Hours (template) Days (training) 2–4 weeks
AI analysis Built in — Limited —
Natural language Core interface — Command line Limited
Custom models One conversation Formula by formula Fixed templates Drag-and-drop
Scenario analysis "Change WACC to 9%" Manual cell edits Limited Preconfigured
Data persistence Full database File-based Session-based Data warehouse
Starting price $20/mo $20/mo $24K/yr $5K+/yr
Where it fits

The AI computation layer
between your data
and your judgment.

Tablize doesn't write your pitch book or replace your financial intuition. It sits between your data sources and your decision — handling the computation, iteration, and visualization that used to take all night.

Tablize AI modeling + computation + dashboards + scheduled reports
reads from
Your data sources Excel exports, Wind, Capital IQ, CSV, manual input
feeds into
Your deliverables Pitch books, memos, board presentations — Tablize never touches these
Who it's for

If one of these sounds like you.

IB Analyst Bulge bracket or boutique

You spend 60% of your time formatting Excel models someone else built. Tablize handles the computation and formatting — you focus on the assumptions and the story. One conversation replaces two hours of model building.

PE Associate Fund of any size

You evaluate 20 targets a month and need quick DCFs on each. Tablize runs the standard model in minutes, so you can spend your time on the deals that actually matter — not on rebuilding the same spreadsheet 20 times.

Independent advisor Solo or small team

No Bloomberg terminal, no analyst pool. Just you, your laptop, and a client who needs a valuation by Thursday. Tablize is the analyst you can't afford to hire — and it works weekends.

Corporate development In-house M&A team

You need to screen acquisition targets, run quick valuations, and present to the board. Tablize sits between your data exports and your PowerPoint — turning raw numbers into decision-ready analysis.

Get started

Your financials are already
in a spreadsheet. Ask them
something.

Upload an Excel model, a Wind export, or a CSV of last quarter's numbers. Ask for a DCF. See what comes back in two minutes — and whether it changes how you work.

Try free with your data See pricing