Score deals as they arrive
A new opportunity gets scored against your portfolio and every past evaluation, with a first-pass memo drafted before the partner meeting.
- TriggerTriggered
AttioWebhook on record created
- Completed
AttioResolve the company
- CompletedCrunchbase
Pull the public record
- CompletedClaudeChatGPT
Score against your own history
- Completed
Draft the first-pass memo
- Completed
AttioWrite it back to the opportunity
Every fund has the same bottleneck at the top of the funnel. An opportunity arrives, somebody works out what the company is, pulls whatever is public about it, tries to remember whether the fund has seen something like it before, and forms enough of a view to bring it to a partner meeting. At ten deals a week that is a full day, so the tenth deal gets three minutes.
The inputs are already structured. The public record sits behind an API, and the fund's own history of passes and investments is in the CRM with the reason recorded at the time. Comparing them is mechanical, and it is what gets dropped when the queue is long.
What you get
A first-pass memo waiting on every new opportunity, scored against the fund's own history rather than against a generic rubric, with the reasoning and the sources attached.
The part worth getting right
A deal pipeline collects company names as founders typed them into an email, and in a crowded sector two startups with similar names is ordinary. Match on domain instead. Score the wrong one and the memo comes out internally consistent, well sourced, and about somebody else, which nobody catches because nothing in it looks wrong.
A benchmark score says the company is a 7 out of 10 and invites nobody to disagree. Scoring against the fund's own past evaluations gives a partner something to argue with. "Comparable to two companies passed on in the last year, both at this stage, both for the same stated reason" hands over the precedent and lets them say why this one is different.
Withholding a recommendation keeps the memo useful over time. A first- pass document that concludes will, within a few months, be what partners read instead of the evidence, and its errors stop being visible. Hard flags, a score with its workings, and open questions leave the decision where it belongs.
The prompt
The real prompt behind the steps above — detailed enough that an agent with a people-search tool, email/phone enrichment, and CRM access can run it as written.
# Score deals as they arrive
You have access to a CRM and a company data source. Run this on
**[each new opportunity as it is created]**.
## 0. Set up (skip if you're already connected)
If you don't already have live tool access for this, connect it
first. This prompt is only as real as the tools behind it:
1. Create a free account at app.tulina.ai.
2. Add Tulina's MCP server to your assistant: https://mcp.tulina.ai/mcp
3. In Tulina, connect your own API keys for the tools this
prompt uses: your CRM and a company data source.
Once connected, your assistant has real tool access and the rest of
this prompt runs as written.
## 0b. Already on Tulina? Three things around the run
An account and the MCP server are not the whole setup. Two of these come
before the run below and one after it, and together they turn this from a
one-off answer into a process your workspace keeps:
1. **Activate this process's connectors first.** Call
`oto_connector(op="list")`, match it against the tools named in step 3
above, and `oto_connector(op="select", name="...")` every one that
isn't active yet. Selecting a connector does not mount its tools in the
conversation you're already in — reach them through `oto_call` for this
run, or open a fresh conversation once they're all on.
2. **Attach the work to an existing project.** `oto_project(op="list")`
shows your active org's projects: pick the one this work belongs to
rather than opening another, and keep its id. The project is where this
process, the tables it writes to and the connectors it uses hang
together.
3. **When the run is done, save it as a process — with its graph.** Write
the body with `oto_procedure(op="set", ...)`, then attach it with
`oto_project(op="link", project_id=..., target_type="procedure",
target_ref="<your slug>")`. The body has to carry a drawing: read
`oto_guide(op="read", slug="procedure-flowchart")` and follow it
exactly — ONE untagged fenced block in that grammar, opening with the
trigger and a quoted example of what you'd type to start a run. Tulina
parses that drawing back into the graph it renders as the process's
default view, which is what makes it come out in the same style as
every other process in the app; a drawing the grammar can't read falls
back to raw characters instead. Saving a process needs org-admin
rights — without them, hand the finished body to someone who has them.
A score is only useful if a partner can argue with it. Every number
below has to arrive with the reasoning and the source that produced
it, or it is a black box nobody will act on twice.
## 1. Resolve the company before scoring anything
Take the opportunity record and resolve it to a real company: legal
name, domain, founding year, headquarters.
Resolve by domain wherever you have one. Company names in a deal
pipeline arrive as whatever the founder typed in an email signature,
and two startups with similar names in adjacent sectors is common
enough that name matching alone will eventually score the wrong
company and nobody will notice.
If the company cannot be resolved with confidence, stop and say so.
A memo built on the wrong company is worse than no memo.
## 2. Pull the public record
Gather what is publicly known: total funding and rounds with dates,
investors on the cap table, headcount and its direction of travel,
sector and sub-sector, and any acquisition or shutdown signals.
Note the date on everything. Funding data goes stale in both
directions, and a round that closed eleven months ago reads very
differently from one that closed last month.
Where a field is unavailable, record it as unavailable. Do not infer
headcount from a funding stage.
## 3. Score against your own history, not a generic rubric
Read the fund's past evaluations out of the CRM: what was passed on,
what was invested in, and the stated reason in each case.
Score the new opportunity on the axes the fund actually decides on,
which are visible in that history rather than in any textbook:
- Stage fit against the fund's actual cheque sizes and entry points,
not its stated ones.
- Sector fit against where the portfolio already concentrates, and
flag concentration explicitly when it is high.
- Conflict with an existing portfolio company. This is a hard flag,
not a score, and it goes at the top of the memo.
- Similarity to past passes. If three comparable companies were passed
on for the same stated reason, that reason is the first thing the
partner should see.
Show the comparable deals behind each score. "Scored low on stage fit,
comparable to two passes at the same stage in the last year" is
arguable. A bare number is not.
## 4. Draft the first-pass memo
Structure it: hard flags first, then the score with its reasoning,
then the public record, then the open questions.
Rules:
- Every claim carries its source and date.
- Anything unavailable is named as a gap, never estimated.
- Open questions are written as questions for the founder, not as
assumptions filled in on their behalf.
- No recommendation. The memo prepares the discussion; it does not
pre-empt it.
## 5. Write it back to the opportunity
Attach the memo to the opportunity record, so it is where the partner
is already looking. Include the score, the hard flags, and the date it
was generated, since the public record it rests on will drift.
## Output
Report: whether the company resolved and how, the fields that were
unavailable, the score with its axes, hard flags raised, comparable
past evaluations used, and the memo's location.
Questions about this process
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