Crunched vs. Claude for financial work | Case study

Crunched vs Claude the AI layer you don't want to build yourself

Claude is one of the strongest models in the world. So is GPT-5.5. So is Gemini 3.1 Pro. The frontier moves every quarter. The question for an advisor isn't "which one model." It is three questions.

  1. 01_Which model_, on which task, at which moment.
  2. 02Who is building, verifying, and maintaining the AI operating system between those models and your deliverable.
  3. 03What review layer verifies the output before it leaves the firm.

The real choice

Most "Crunched vs. Claude" conversations start in the wrong place. They compare two products that look similar in a screenshot – a sidebar in Excel, a chat box, a "create a model" button – and ask which one is better. The right comparison sits on three ideas.

First – staying at the AI model forefront. Picking the best AI for advisory work isn't a one-time choice between vendors. It's a continuous routing problem: which model is best for PDF extraction this quarter, which is sharpest at long-context spreadsheet reasoning today, which one verifies a finance model most reliably right now. Going with Claude alone (or any single model) collapses that decision into one provider's roadmap. Crunched routes per task, and re-routes as the frontier moves – across Claude, GPT, Gemini, and others.

Second – building the widely adopted AI workflows. Underneath the routing sits real work: workflows tuned for finance, eval infrastructure that proves they hold up on real cases, integrations into your templates, ongoing tuning as models evolve. Going with Claude alone means your firm is signing up to build, verify, and maintain its own AI layer. Crunched delivers all three as a managed service.

Third – verifying human and AI output. AI has moved where the hard work happens. Content creation is no longer the bottleneck – a frontier model can produce a 15-tab workbook or a 100-page deck in an afternoon. The bottleneck is review. The hard, valuable, career-defining work of advisory is making sure AI-generated output is correct before it leaves the firm. A chat model is excellent at producing. It is not built around the problem of reviewing what it produced. Crunched is.

Crunched is multi-LLM. Claude is one of the engines.

A quick clarification, because we get this question a lot. Crunched is not built on Claude. Crunched is built on a routing layer that picks the right model for the right task – and updates that routing as new models ship.

In practice, that means a single workflow inside Crunched might use one model to extract numbers from a PDF, a second to build the linked DCF, a third to draft the market report, and a fourth to cross-check the result. Each leg goes to whichever frontier model is best for that specific job, today. Tomorrow that allocation changes – quietly, on our side, without any work on yours.

We use Claude a lot. Anthropic profiled us as a customer because we use Sonnet and Opus well, particularly for finance-specific reasoning and large-context spreadsheet work. But Claude is one of several engines under the hood. The reason a firm should buy Crunched isn't that we use Claude. It's that we use the right model for the right task – and we keep doing that as the frontier moves.

What "build, verify, maintain" actually means

Going with Claude alone (or any single model) means your firm is signing up to build, verify, and maintain its own AI layer. Crunched delivers all three as a managed service.

Build – workflows are not prompts

A real M&A workflow is not a prompt. A custom LBO build, a rent-roll extraction, a sector comp set, a market-sizing model – each of these is three to six pages of prompt logic, plus eval infrastructure that proves the workflow works on real cases, plus integrations into the templates and data sources your firm actually uses, plus the routing logic that decides which model handles which leg.

Going with a chat tool alone, the work to build all that lands on your associates – at night, between deals. Crunched does it for you, with 100% focus on this exact problem and global visibility into what is working across the industry. The workflow library is the product, not the model behind it.

Verify – a chat model can't check its own homework

This is the differentiator we expect to matter most over the next two years, and it's the one we flagged in the opening.

AI made production easy. The bottleneck moved. The hard part of advisory work is no longer building the model – it's reviewing it with confidence, fast enough to deliver. And there's a structural problem with asking a single chat model to do that review: the model that generated the errors shares the blind spots that produced them. It is not well-positioned to catch its own mistakes.

Crunched reviews work in a multi-LLM system – different models, with different blind spots, cross-checking each other – wrapped in a visual auditing layer that traces every cell in the model back to its source PDF, including formula precedents and dependents. Your reviewers see what changed, why, and where the underlying number came from, end to end. That is the layer that lets a firm scale AI-generated output without scaling AI-generated risk.

A general assistant can be asked to review work. It is not designed around the review problem.

Maintain – the right model keeps changing

Two months ago, the obvious choice for finance reasoning was one provider. Last month, another release pulled ahead on revenue and on long-context spreadsheet performance. Last Friday, GPT-5.5 retook the lead on reasoning benchmarks. Gemini 3.1 Pro now leads on several specific tasks. None of these statements will be true a quarter from now – and the right model for, say, PDF extraction may not be the right model for sensitivity analysis, even on the same day.

Going with a single-vendor tool means your firm is locked to one provider's roadmap, no matter how the relative strengths shift task by task. Crunched re-evaluates the workflow library every time a new model ships, and re-routes per task and per use case as performance moves. Customers feel the upgrade automatically. They don't migrate prompts, switch tools, or renegotiate procurement. They stay on the frontier without doing the work to stay there.

This is the one most firms underestimate. AI in 2026 is not "pick a model and run with it." It is a moving target across a dozen tasks, and someone has to chase it on each. Crunched does that, so your team doesn't.

Two more things general AI doesn't ship

Excel features designed for live deal work

A chat model in Excel can read and edit a workbook. Crunched ships the things finance teams need to actually use AI on a live deal model:

A model in a sidebar can do impressive things. None of these are them.

Adoption – the thing that decides ROI

You can buy any AI tool. Adoption is what makes the spend pay back. A chat model alone is software you license and hope the team uses. Most of the firm doesn't. The senior partner who only opens Excel to review won't learn a new chat. The associate sprinting toward Friday's IC won't experiment mid-deal. Six months in, you have a few power users, a procurement bill, and not much to show for it.

Crunched ships with a structured adoption programme – onboarding by team, prompt libraries built around your templates, office hours, and ongoing workflow tailoring. We measure activation by team, not seat count, and we close the gaps. The reason a customer like Mile Marker Advisors reports 60–90% time savings on core modelling workflows isn't that they have access to a better model. It's that the partner, the VP, and the analyst all use Crunched on the same workflows.

So which one should you actually buy?

If your team needs a great generalist for research, drafting, and ad-hoc analysis across the firm, Claude is excellent and we'd encourage you to use it. We use it ourselves, alongside other frontier models, every day inside Crunched.

If your team's deliverable is a model and a deck, and the bar is investment-grade, Crunched is what we built for that. The build, verify, and maintain work happens here, with the right model picked for the right task at the right time – and so does the review layer, the part that matters most now that production is no longer the hard problem. Your firm focuses on delivering the work and the client service, not on running an internal AI lab.

For most advisors, the right answer is both. Claude across the org for general work. Crunched in Excel, where the deliverable lives, and where the layer between the model and the deal has to be built right.

Crunched is an AI Excel analyst built by and for Excel power users in investment banking, private equity, and management consulting. Backed by First Round Capital, Y Combinator, 20VC and others, Crunched integrates frontier AI models directly into Excel to automate modelling, error-checking, research, and templated outputs – without breaking existing workflows.