AI CO-PILOT FOR STRATEGIC FINANCE

Finance rigor, at software speed.

Get instant, rigorous analysis of all your financial models - enabling your team to make better decisions, faster.

Join the waitlistSee how it works
Works with your existing Excel & Google Sheet files - no migration needed
Your models stay yours - never used to train foundation models
Purpose-built for finance, by finance
FY25 -Operating Plan v3.xlsxScanning · 0%
Plan
Assumptions
Cohorts
Benchmarks
JanFebMarQ1AprMayJun
2
REVENUE
3
New MRR
142,400
168,900
191,200
502,500
214,800
238,100
265,400
4
Expansion MRR
38,200
44,100
48,900
131,200
54,600
61,200
68,800
5
Churned MRR
(18,400)
(21,100)
(23,800)
(63,300)
(26,200)
(28,900)
(32,100)
6
Net New MRR
162,200
191,900
216,300
570,400
243,200
270,400
302,100
7
Ending ARR
14,200,000
16,502,800
19,098,400
19,098,400
22,016,800
25,261,600
28,887,800
8
9
ASSUMPTIONS
10
Gross margin
78.0%
78.0%
78.0%
78.0%
78.0%
78.0%
78.0%
11
Net revenue retention
118%
118%
118%
118%
118%
118%
118%
12
Sales efficiency
1.4x
1.4x
1.4x
1.4x
1.4x
1.4x
1.4x
13
CAC payback (months)
14
14
14
14
14
14
14
0.0s / 7.0s
● 0 errors   ● 0 to review
Error detected
Sheet 'Plan' · cell E7
Q1 total points to a deleted row on the Assumptions tab. Should sum B7:D7.
Error detected
Sheet 'Plan' · cell F8
Apr ending ARR is typed, not formula. Breaks the compounding chain from March.
Assumption flag
Sheet 'Plan' · row 12
118% NRR held flat 12 months. Public SaaS median is 107%. Worth a downside case.
What it does

The evaluation layer for your finance team

Your operational and strategic decisions deserve purpose-built software, not a general-purpose LLM. Claude and Copilot makes it faster to build models. Pythia is the layer above: the evaluation, the pressure-test, the second pair of eyes a VP of Finance would give a model before it drives a decision. On every model. Every time.

01

Error detection

Hardcoded cells, inconsistencies across models, mislinked sheets -found in seconds, before the deck is circulated.

Cohort retention · %0 / 2 flagged
Cohort
M0
M1
M2
M3
M4
M5
‘24 Q1
100%
=B3*$R$2
=C3*$R$3
=D3*$R$4
=E3*$R$5
=F3*$R$6
‘24 Q2
100%
=B4*$R$2
=C4*$R$3
82%
=E4*$R$5
=F4*$R$6
‘24 Q3
100%
=B5*$R$2
=C5*$R$3
=D5*$R$4
=E5*$R$5
=F5*$R$6
‘24 Q4
100%
=B6*$R$2
=B6*$R$2
=D6*$R$4
=E6*$R$5
=F6*$R$6
Hardcoded values0 / 1
Mislinked sheets0 / 1
Broken references0 / 0
02

Assumption quality

Every model is a bet on a handful of assumptions. Pythia makes sure you are making the right ones by answering the question a good CFO asks: what do we have to believe for this to work -and which key assumptions are most worth stress-testing?

Assumptions worth stress-testing
Impact−£90M FY28 revenue
Plan assumes 20% of subscribers adopt the higher tier by FY28 (+£180M). Current conversion is 10% and has been flat for three quarters. No price elasticity modelled. Held at 10%, FY28 group revenue drops by £90M.
03

Benchmarking

One repository for every model. Compare across business units, time periods, and external benchmarks.

This plan
34%
Competitor A
22%
Competitor B
18%
Competitor C
12%
Source: public filings
04

Automated reporting

Turn the numbers into the narrative. Pythia drafts your weekly trading update and monthly business review -KPIs, commentary, and full-year implications -in the format your team already presents in.

Weekly trading update · W28Auto-generated
Revenue
£4.82M
+6.2% vs plan
Orders
38,410
+4.1% vs plan
AOV
£125.4
+2.0% vs plan
Gross margin
38.9%
−120 bps vs plan
New custs.
6,140
−8.3% vs plan
Repeat rate
54.1%
+80 bps vs plan
Revenue £4.82M, +6.2% vs plan, driven by stronger AOV and repeat-rate in Tier 1 cities. New customer acquisition softened against a heavier paid-social comp week.
Export \u2192 .pptx · .pdf · SlidesReady in 90s
Who it's for

Built for teams being asked
to do more with less.

For post-Series B companies where the scope is expanding and every decision needs a defensible number behind it.

01 · Role

VP Finance

Running a team of 6–12. Every board cycle brings more scope, same headcount.

  • Ship the board deck in half the time
  • Catch errors before the CFO does
  • A shared workspace with version history, audit trails, and granular access controls.
02 · Role

CFO

The one who has to defend every number. Needs conviction at speed.

  • See which assumptions actually move the outcome
  • Benchmark against real peer data
  • Trust the model without re-modeling it
03 · Role

FP&A Lead

Deep in the models. Holds the keys to the kingdom and the bugs.

  • Audit any inherited model, instantly
  • Easy to collaborate with your team and stakeholders
  • Complete your weekly trading update or monthly business review in minutes
Built for teams

One workspace. Every model.
Every version.

Finance doesn't work in isolation. A financial model moves across members of finance, needs input from product, gets reviewed by the exec sponsor etc. Claude and other LLMs are designed around a single user and a single chat. Pythia is built around the team and the model.

Shared repository
Every financial model your team has ever built, in one place. Searchable, comparable, versioned.
Version history
Every change, every comment, every reviewer -tracked. See how assumptions evolved between the v1 board deck and the one that went out.
Granular permissions
Control who sees, comments on, and reviews each model.
Audit trail
Every Pythia review is logged with its inputs, findings, and who acted on them. The institutional record your finance function needs, by default.
Security & trust

Your models never leave your control.

Finance data is the most sensitive in the business. Pythia is architected so you can use it without asking your security team for forgiveness.

Fully encrypted
All files are fully encrypted -at rest and in transit.
Private by default
Your models never train foundation models. Ever.
Regional data residency
EU, US, and UK hosting. SSO and SCIM included.
Read-only access
Pythia reads your models. It never writes back without approval.
Questions

You're probably wondering.

General-purpose assistants like Claude and ChatGPT are powerful -and your team should absolutely use them. Pythia works at a different altitude. Think of it as the difference between an analyst and a VP. Claude will help you build models faster. Pythia is the evaluation layer above -the review that applies the same structured rigor to every model, independent of who ran it or what they prompted. It draws on a curated context of your business, a deliberate framework for which benchmarks apply to which models, and a record of every review you’ve done before. That consistency is what finance leaders need to trust the review.
No -and it’s not designed to. Pythia is built to give finance teams leverage across every level. Analysts get a second pair of eyes on the models they’re building. Leads can review the team’s work faster and with more consistency. Heads of Finance and CFOs get confidence that every model has been through the same rigorous lens. The teams we’re working with are using Pythia to spend less time on mechanical review and more time on the decisions that matter.
Pythia flags and explains -it never edits your model or makes changes without you. Every finding comes with its reasoning and a reference to the specific cell, sheet, or assumption it’s drawn from, so your team can verify in seconds. We also combine deterministic, Python-based spreadsheet analysis with LLM reasoning: the mechanical checks (hardcoded values, broken references, formula consistency or other mistakes) don’t rely on the model’s judgment at all. Where judgment is needed, Pythia shows its work.
No. Pythia sits on top of the Excel and Google Sheets files your team already works in. No migration, no new modelling syntax, no parallel system to maintain. If you stopped using Pythia tomorrow, your team’s workflow would be unchanged.
Pythia is built for growth-stage companies -typically post-Series B through pre-IPO -where the finance team is being asked to cover expanding scope without proportionally expanding headcount. If your Strategic Finance function is being stretched, and the business wants to move faster, you’re the right fit.
Benchmarks come from public company filings, industry research, and licensed external datasets. Every benchmark surfaced by Pythia comes with its source and methodology, so your team can judge how much weight to give it.
Your models never train foundation models. Ever. Data is encrypted in transit and at rest, hosted in your region of choice (EU, US, or UK), and access is controlled by SSO and SCIM. Pythia operates read-only by default -it can’t modify or delete your files without explicit approval. Full details in the Security section above.
Most customers are scanning their first model within 20 minutes of connecting their drive. A full rollout -including custom business context and team workflows -typically takes 1–2 weeks with our forward-deployed team.
Why I'm building Pythia

I've spent the last decade in the seat I'm now building for - first as an investment banker, then in finance leadership at high-growth tech companies. Everywhere I looked, the same pattern: as companies grow, finance teams are asked to do more analysis, with more rigor, across more of the business - with hiring as the only lever available.

Strategic Finance, at its best, is about helping a business make better operational and strategic decisions. That's a judgment job. AI won't replace that - but it should finally give the people doing it the leverage they've never had.

Pythia is the tool I wanted when I was in the job. If any of this sounds familiar, I'd like to talk.

Scale your judgment

Join the waitlist. We're onboarding a select group of finance teams.

No credit cardFounder-led onboarding