Deckwise vs Spreadsheets for Startup Screening
A spreadsheet is where almost every program starts: one row per applicant, a few columns for scores, a shared tab for the committee. It is free, flexible, and everyone knows how to use it. It also has no memory, no verification, and no way to stop a polished-but-false claim from scoring like an honest one. This is an honest look at where the spreadsheet is enough, and where it quietly costs you.
We are not going to pretend spreadsheets are useless. For most teams they are the right first tool. The question is when the spreadsheet stops being a tool and starts being a liability.
Side by side
| Spreadsheet | Deckwise | |
|---|---|---|
| Setup cost | Free, instant | A guided setup per program |
| Scoring basis | Whatever each reviewer types | One basis applied to every candidate |
| Claim verification | None | Every material claim checked against public sources |
| Consistency | Drifts per reviewer and per day | Same basis, same evidence, for all |
| Ranking | Manual sorting | Ranked tiers, wildcards, red flags |
| Defensibility | "See the sheet" | Evidence and reasoning attached to each score |
| Institutional memory | Lost between cycles | Verified evaluations accumulate |
| Collaboration | Shared cells, merge conflicts | Roles, comments, committee export |
| Best for | Low volume, one-off calls | Recurring programs, high volume, claims that matter |
Where the spreadsheet is genuinely enough
Use a spreadsheet, and do not feel bad about it, when:
- You run one small call a year with a handful of applicants you can each read in full.
- You do not need to defend the outcome to a committee, a board, or LPs beyond "we discussed it."
- The claims are not the risk. If you already know every applicant and their numbers, verification adds little.
In those cases a spreadsheet is fast, free and honest. Adding software would be over-engineering.
Where the spreadsheet quietly costs you
The spreadsheet does not fail loudly. It fails in ways you only notice later:
- It has no verification. A cell that says "TAM: 45B" is just text. Nobody checked it. The spreadsheet will happily rank an inflated claim above an honest one, because it treats a sales statement and a verified fact identically.
- It has no shared basis. Reviewer A scores team out of 10 by instinct, reviewer B by a different instinct, and the average is noise dressed as a number. Two candidates on the same sheet were never judged the same way.
- It has no memory. Everything you learned about a company last cycle is gone next cycle. When an analyst leaves, their judgment leaves with them. The spreadsheet does not compound.
- It does not scale. At 300 rows, "read every application" becomes "skim every application," and the sheet gives you no help closing that gap.
- It is hard to defend. "Why this one and not that one?" has no answer in a spreadsheet beyond the final number, and the number hides how you got there.
None of these show up on day one. They show up the day a funded company turns out to have misstated its traction, or the day the committee asks you to justify the shortlist and the sheet has nothing underneath the scores.
What Deckwise adds
Deckwise keeps the thing spreadsheets get right, one row per candidate, comparable columns, and fixes the things they get wrong:
- Verification as a first-class step. Every material claim is pulled out and checked, official registers first, then sourced web search, and comes back verified, qualified, contradicted, or to-confirm. The score reflects what is real, not what is claimed.
- One basis for everyone. Every candidate is scored on the same three questions, is it true, is it a good company, is it a good fit for you, with weights you set once and apply to all.
- A ranked, defensible shortlist. Tiers, wildcards for one-dimension standouts, and red flags that pull a score down so a serious negative cannot hide behind an average. Each ranking carries the evidence and reasoning, exportable for the committee.
- Memory that compounds. Verified evaluations, theses and shortlists persist. The fund's judgment accumulates in one place instead of evaporating between cycles.
- The human still decides. Deckwise is a co-pilot, not a judge, which is also the right posture under the EU AI Act for evaluating people.
Migrating from a spreadsheet
Moving off a spreadsheet is not a rip-and-replace. The usual path:
- Keep your current criteria. They become the scoring basis, made explicit and weighted.
- Import an existing applicant list. The structure you already have maps over.
- Run one real call through both in parallel. Compare the shortlist the spreadsheet produced with the verified, ranked one. The gap is the cost the spreadsheet was hiding.
Frequently asked questions
Is a spreadsheet really that risky? For a small, well-known set of applicants, no. The risk grows with volume and with how much the claims matter. The specific danger is that a spreadsheet cannot tell a verified fact from a confident sentence, so it ranks them the same.
Can I not just add verification to my spreadsheet manually? You can, for a few claims. At scale it is the step that never happens, because no one has time to check hundreds of claims against public sources by hand. That is exactly the work a screening tool automates.
What do I lose by leaving the spreadsheet? Almost nothing you want to keep. You keep one-row-per-candidate and comparable scoring. You lose merge conflicts, drift, and unverified claims.
When should I stay on a spreadsheet? One small call a year, applicants you can each read fully, and no need to defend the outcome formally. Below that threshold, a spreadsheet is the right, honest choice.
The bottom line
A spreadsheet is the correct first tool and a poor last one. It is free and flexible, but it cannot verify a claim, cannot hold a shared basis, and cannot remember. The moment your volume rises or your decisions need to be defended, the spreadsheet stops saving you time and starts hiding risk. Deckwise keeps what the spreadsheet gets right and adds the verification, consistency and memory it structurally cannot.
Related: Manual vs AI Screening · The Complete Guide to Startup Screening