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AI that turns insurance and finance document packages into checked, structured records · United Kingdom

Parsewise: Product, team and how it was built

Small teamFundedB2BInsurance

Parsewise (AI that turns insurance and finance document packages into checked, structured records, United Kingdom): Three named insurance and mortgage customers, November 2026. Evidence grade E: no revenue figure was checked. Team: 2 co-founders in 2024; 6 people in November 2026 after Y Combinator funding. Launched 14 May 2025; still running.Added 5 November 2026 · updated 6 November 2026

Outcome
Three named insurance and mortgage customers, November 2026
Evidence
E no revenue figure was checked
Launch
14 May 2025
Status
Still running
Team
2 co-founders in 2024; 6 people in November 2026 after Y Combinator funding
Outside funding
Y Combinator, Spring 2025 batch; round size not verified
Main channel
Not stated
No revenue figure

What is not verified

No revenue figure was found. The only statement about money is a Slush 100 judge's write-up of 20 November 2025: "Live revenue, daily usage inside financial institutions, and strong retention", without an amount. The funding amount is also unclear: VCBacked says US$1.6M, FundedIQ says US$500K and StartupHub says US$2M, and no announcement by the company or an investor was found. The UK register shows no share-allotment filing for the current company. A reader could look for a funding announcement, a filed set of accounts (the first are due by 28 May 2027) or a founder interview with figures. This story is grade E: the business is real and its product, price and customers are described from public pages, but no revenue figure was checked. Aiolum shows it as an idea worth studying, not as proof that it makes money.

Parsewise is a London company that does multi-document extraction: a program reads many documents about one case at once, takes out the values a team needs (a reserve, a deductible, a flood limit), and compares them across the documents. Each value keeps a source trace, a link to the page and the box on that page where it was found, so an underwriter or claims handler can check it. Its customers are commercial insurers, MGAs (managing general agents, firms that underwrite on behalf of an insurer), brokers and investment teams.

Max Hofer and Greg Csegzi met studying computer science at Oxford in 2017, the company's Y Combinator page and launch post say. They founded Parsewise in 2024, joined Y Combinator's Spring 2025 batch, and posted the launch on 14 May 2025, then "live with pilot customers". In November 2025 the company was runner-up at the Slush 100 pitch competition, where a judge's write-up reported "live revenue" without a figure. In November 2026 the Y Combinator page lists 6 people, and the company's site names three customers: Compre, Thinksurance and HypoHaus.

No revenue figure was found in any source, so this story is grade E: the headline is a count of named customers, not money. The funding amount is also not verified: three startup directories give three different amounts.

Parsewise: Three named insurance and mortgage customers, November 2026. AI that turns insurance and finance document packages into checked, structured records, United Kingdom, team of 2 to 10, evidence grade E.
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01

How much revenue does Parsewise make?

Each row shows the evidence grade and the source numbers from the list at the end of this page. The grades are explained in How Aiolum verifies.

A two-founder Y Combinator company with named enterprise customers and no published revenue figure
FactValueGradeHow we knowSources
Named customers, November 2026Compre, Thinksurance and HypoHausCThe company's customer-stories page describes how each one uses Parsewise. The Y Combinator launch post of May 2026 also names UBS; the company's own customer page does not.[7, 5]
Revenueno figure foundCA Slush 100 judge's write-up of 20 November 2025 says: "Live revenue, daily usage inside financial institutions, and strong retention." No amount is given anywhere.[6]
FoundersMax Hofer (CEO) and Greg Csegzi (CTO)BThe UK company register lists the two as the only directors of Parsewise Ltd, both appointed 28 August 2025. Y Combinator lists them as the founders.[10, 5]
Founded2024CY Combinator's company page. A first Parsewise Ltd was incorporated on 4 December 2024 and dissolved on 27 May 2025; the current Parsewise Ltd was incorporated on 28 August 2025.[5, 10]
Team, November 20266 peopleCY Combinator page, team size 6. The company's about page lists six people: the two co-founders, two founding engineers, a founding go-to-market lead and a forward-deployed engineer. The blog also has posts by one more author.[5, 4, 9]
FundingY Combinator, Spring 2025 batchCY Combinator page. The round size is not verified: VCBacked says US$1.6M pre-seed in April 2025, FundedIQ says US$500K pre-seed in April 2025, and StartupHub says US$2M seed. No company or investor announcement was found.[5, 11, 12, 13]
Pitch resultSlush 100 runner-up, November 2025BUpNext World's recap of the Slush 100 finals, 20 November 2025.[6]
API priceUS$0.005 a page to parse, US$0.005 a page per field to extractCAPI page, read 5 November 2026. US$50 of free credits for a new account.[3]
Platform plansGrowth, Business, Enterprise; one-year contracts, no list priceCPricing page, read 5 November 2026. Onboarding takes three weeks on Growth and Business, two months on Enterprise.[2]
AI models usedGoogle Gemini 3 Flash and Gemini 3.5 FlashCCompany blog on the OfficeQA benchmark, 30 June 2026: Gemini 3 Flash for text extraction, Gemini 3.5 Flash for reasoning in Navi. Google Cloud's customer story on Parsewise describes the same setup.[8, 15]
Benchmark claim58.65% correct on Databricks OfficeQA, against 57.90% for the best frontier modelCCompany blog, 30 June 2026. The company also reports a "revised" score that counts 15 of 133 questions where it says its answer is better than the official key.[8]
Security and hostingSOC 2 Type II, GDPR, hosting and model inference in EuropeCHome page, 5 November 2026.[1]
Time from launch post to the customer count18 monthscalcCalculated: May 2025 to November 2026.[5, 7]

How to read this table: a grade C value is the company's own statement (its site, its Y Combinator launch posts) or a third-party directory. A grade B value comes from an independent record: the UK company register or a judge's write-up. A "calc" value was computed on this page. No value is grade A.

Parsewise prices read on 5 November 2026 from the company's pricing page and its API page (grade C)
PlanPer month, billed yearly (US$)Per month, billed monthly (US$)Limit
API, Free / pay as you go00US$50 free credits, then US$0.005 a page to parse and US$0.005 a page per field to extract; up to 2 UI seats
API, Enterprisecustomcustomvolume discounts, as many agents and users as needed
Growthquote, one-year contractWorkflows seats, up to 500 pages per run, three weeks of onboarding
Businessquote, one-year contractadds Analytics credits and single sign-on
Enterprisequote, one-year contractadds dedicated engineering time and a private cloud option

The three platform plans have no list price: each is a one-year contract scoped to the team and its expected usage, plus a one-time onboarding fee. Analytics usage is charged in credits by page count and extraction complexity, with extra usage billed automatically. The API is in US dollars and charged per page: parsing costs US$0.005 a page, and extraction costs US$0.005 for each page times each field. New API accounts get US$50 of free credits, which the company says is enough for about 1,600 pages with 5 agents.

02

When did Parsewise launch, and how did it grow?

From a 2024 founding to a six-person team selling to insurers, in about two years. Each entry carries its grade.

  1. 2017CMax Hofer and Greg Csegzi meet studying computer science at Oxford. Hofer later works at Bain and does a PhD; Csegzi builds AI use cases at Palantir, including for insurance. [5]
  2. 2024BParsewise is founded. A first Parsewise Ltd is registered in London on 4 December 2024. [5, 10]
  3. 14 May 2025CLaunch post on Y Combinator, Spring 2025 batch: extraction from complex documents with a source for every value, "live with pilot customers". [5]
  4. 28 August 2025BThe current Parsewise Ltd is incorporated, with the two founders as directors. [10]
  5. 20 November 2025BRunner-up at the Slush 100 finals. The judge's write-up reports live revenue and daily use inside financial institutions, with no figure. [6]
  6. 23 March 2026CNavi, a chat assistant that sets up extraction agents from plain-language questions, is announced. [9]
  7. 26 May 2026CSecond launch post: a pay-per-page API for multi-document processing. [5, 3]
  8. November 2026CSix people. The site is aimed at commercial insurance and names Compre, Thinksurance and HypoHaus as customers. [5, 1, 7]
03

Why did Parsewise work?

Four causes, in the order they mattered. Each one is a reusable lesson.

01

Domain experience

The CTO had built document pipelines for insurance clients at Palantir; the CEO had done more than 20 private-equity due diligence projects at Bain, the launch post says. They knew that the buyers' problem was trust in each value, not reading speed.

02

A source for every value

Each value links to its page and position, and values that disagree across documents are flagged. A regulated insurer can then check and defend a decision, which a plain chat answer does not allow.

03

Read everything, not a sample

The product reads the whole document set, so a bordereau is checked row by row. The company argues that sampling misses the errors that cost money.

04

One narrow market

The May 2025 launch post named investment management, reinsurance and life sciences; the 2026 site speaks to commercial insurance. Pages per workflow (claims, underwriting, bordereaux) match the work of one kind of buyer.

How it was built

These details come from the company's own site, blog and Y Combinator launch posts. They are grade C. [8, 9, 1, 2, 3]

  • Google Gemini models inside the product: Gemini 3 Flash for text extraction and Gemini 3.5 Flash for reasoning, per the June 2026 benchmark post.
  • A data engine that the company says can parse thousands of documents per query and run millions of model calls, with agents that each extract one topic.
  • Hosting and model inference in Europe, or inside a customer's own cloud on AWS, Azure or Google Cloud.
  • SOC 2 Type II and GDPR compliance, which enterprise insurance buyers ask for.
  • Each customer gets a configured setup: the team agrees fields, rules and checks, and tests on real cases during a three-week or two-month onboarding.
  • An API with free credits and per-page prices, launched in May 2026 for developers who want to call the engine from their own product.

Tools named in the sources

Each one opens its page in the Tools tab, with every story that uses it.

04

Who tried this before Parsewise, and why did they fail?

Three companies in our database of failed startups promised to take the paperwork out of a document-heavy financial process. The values below are from that database and are not independently verified.

Died 2016 to 2019Burned US$500K

Lendsnap

The promise

Automatic collection of borrowers' financial documents for mortgage lenders.

Why it died

Broader mortgage platforms such as Blend and Roostify offered the same step inside an end-to-end product, and it could not win enterprise partners.

Competition
Died 2021 to 2024Burned US$1.5M

Opkit

The promise

Automated health insurance verification and claims processing.

Why it died

Hospitals were slow to adopt new software, integration was hard, and Epic offered bundled services. It failed to secure long-term contracts.

Ran Out of Cash
Died 2011 to 2017Burned US$17M

Canvas

The promise

Turn paper forms and documents into digital workflows for businesses.

Why it died

Microsoft and Adobe added form and document features to products customers already had.

Competition

The failed startups come from our database of 1,749 dead startups, retold here in our own words. Its entries are AI-assisted summaries of public sources, so these cards carry no evidence grade.

What changed

The three predecessors automated one step of a paper process and were overtaken when larger platforms put the same step inside a broader product. The same risk exists here: the model makers and the big document-AI services sell extraction too.

What differs is that large language models can now read documents of any layout without a template, so one small team can cover policies, claims and loss runs together. Parsewise sells the part the models do not provide: cross-document checks, a source for each value, and setup with each customer's own rules.

05

Can one person copy Parsewise now?

Hard to copy alone.

The product needs enterprise sales, security certification and onboarding work with each customer, and the founders brought years of insurance and due-diligence experience. A solo founder could copy the narrow version: a per-page API for one document type in one industry, with a source trace for each value.

Where it is still open: Other regulated document work with the same shape: trade finance, construction claims, clinical trial files, public tenders.

  1. 1Pick one document-heavy job in one industry that you know from your own work, for example bordereau checks or mortgage application review.
  2. 2Return every value with the page it came from, and flag values that disagree across documents.
  3. 3Offer a self-serve API with free credits and a per-page price, so developers can test it without a sales call.
  4. 4Get the security certification your buyers ask for before you sell to large companies.

What could go wrong for Parsewise?

The risks are the mirror image of the causes above.

What is not verified

No revenue figure was found. The only statement about money is a Slush 100 judge's write-up of 20 November 2025: "Live revenue, daily usage inside financial institutions, and strong retention", without an amount. The funding amount is also unclear: VCBacked says US$1.6M, FundedIQ says US$500K and StartupHub says US$2M, and no announcement by the company or an investor was found. The UK register shows no share-allotment filing for the current company. A reader could look for a funding announcement, a filed set of accounts (the first are due by 28 May 2027) or a founder interview with figures.

Team size and funding

Two co-founders started it in 2024. Y Combinator lists 6 people in November 2026 and the about page shows the same six; the blog has posts by one more author. It has outside funding from Y Combinator.

Trust signals

The May 2025 launch post quotes two customers by job title only ("Head of Family Office", "Lead Data Analyst at a Large Asset Manager"). The May 2026 post names UBS as a customer, which the company's own customer page does not list. The benchmark lead rests on the company's own test, and its higher "revised" score counts answers it judged better than the official key.

Depends on Google's models

The June 2026 benchmark post says all its results use Gemini 3 Flash and Gemini 3.5 Flash. Google Cloud's own customer story on Parsewise confirms the setup. A price or terms change from Google would reach the product directly, although the post says the company wants to try larger models as well.

Competition

Document extraction is a crowded market: the model makers, cloud document services and many startups sell it, and on this site PDF Vector sells a document parsing API to developers, while Parsewise sells mainly year-long contracts to insurers.

Sources

The grade next to each source is the best grade that the source can support. "Read in full" means that the text of the page was downloaded and the numbers were checked in it.

  1. C Parsewise home page, read 5 November 2026
    Read in full. Source for the product, the target buyers, the security claims and the European hosting.
  2. C Parsewise pricing page, read 5 November 2026
    Read in full. Source for the three contract plans, the one-year term and the onboarding fee.
  3. C Parsewise API page, read 5 November 2026
    Read in full. Source for the per-page API prices, the free credits and the deployment options.
  4. C About Us, Parsewise, read 5 November 2026
    Read in full. Source for the six people listed on the team.
  5. C Y Combinator company page: Parsewise, Spring 2025
    Read in full, including the page data with the two launch posts of 14 May 2025 and 26 May 2026. Source for the founders, founding year, batch, team size and customers named by the founders.
  6. B UpNext World, 20 November 2025: Inside Slush 100: Diffraqtion Wins It All, Plus the Finals and Standout Founders
    Read the section on Parsewise. Source for the runner-up place and the traction statement without a figure.
  7. C How teams put Parsewise to work, customer stories, read 5 November 2026
    Read in full. Source for the three named customers and how each uses the product.
  8. C Parsewise blog, 30 June 2026: SOTA results on Databricks OfficeQA
    Read in full. Source for the benchmark claim and the Gemini models used.
  9. C Parsewise blog index and the post of 23 March 2026: Navi: A New Interface to Parsewise
    Read the index and the Navi post in full. Source for the post dates, the authors and the data engine description.
  10. B UK Companies House: PARSEWISE LTD, 16677569 (overview, officers, filing history) and 16117326 (dissolved)
    Read the overview, officers and filing history pages of both companies. Source for the directors, incorporation dates and the absence of share-allotment filings.
  11. D VCBacked: Parsewise Funding & Investors
    Read in full. A directory; its round size disagrees with the two other directories.
  12. D FundedIQ: Parsewise: Funding, Investors & Team (Nov 2026)
    Read in full. A directory; it also gives the wrong city (San Francisco).
  13. D StartupHub.ai: Parsewise
    Read in full. A directory; it says Parsewise was founded in 2025, which disagrees with Y Combinator's page.
  14. A Google Cloud customer story: Parsewise, read 5 November 2026
    Read in full. The model vendor's own account of how Parsewise uses Gemini models and Google Cloud.
  15. C Database of failed startups, local export
    Local export. AI-written summaries, not independently verified. Source for the failed predecessors.

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