For hardware, lab and climate teams
Keep your grant and RFP writers on the cutting edge of AI.
We test new AI on your own past grants, RFPs and reports, set up what wins in your documents, and train each writer on it. When better AI ships, we do it again.
See it in 2 minutes
How Orcapo works, start to finish.
Why teams fall behind
AI gets better every month. Your team’s tools stay the same.
01
Switching is a project.
Every new tool means rebuilding automations and changing how people work.
02
Everyone has to relearn.
Each switch means training people again, so most teams never make it.
03
Hype is hard to spot.
New AI ships every week. Nobody has time to test what is real on your own work.
Where we start
The work we test first on teams like yours.
We confirm yours in the first round.
01
Grant applications
Today
Each section written from scratch.
After a round with Orcapo
Sections drafted from your past applications, then edited by your team.
02
RFP and security questionnaires
Today
Hundreds of questions answered by hand.
After a round with Orcapo
Answers drafted from your approved past answers, checked by a person.
03
Test reports and customer briefs
Today
Results rewritten for each audience.
After a round with Orcapo
Drafts from your lab results, checked against the data.
How it works
We work in rounds. Each one keeps your team on the best AI.
- 01Map
- 02Test
- 03Roadmap
- 04Set up
- 05Train
- 06Next round
01 · Map
We learn how your team works today.
It starts with one email. You connect your tools as read-only, and each person fills out a 5-minute form. We map every role and find where the time goes.
Company
An example deep-tech team, 6 writers
Tools you pay for
Your rules
Approved tools only
No customer data in public AI
Roles
Time on repeated tasks
People
AI experience, person by person
Program manager
New to AI · owns two grants
Program manager
Uses ChatGPT for edits · owns RFPs
From a 5-minute form each person fills out.
02 · Test
We test new AI on your own past work.
Every week we go through new AI tools, models and research. We run the best options on your past work, in a safe copy of your tools. An option passes only if it beats how you work today, and your team picks the better result without knowing which tool made it.
Test · Grant applications
50 past examples- Baseline
Today’s way
How your team does it now
- Not better
Option A
A new AI agent
- Beat today
Option B
A newer model, set up for you
- Too many errors
Option C
A startup’s new tool
Blind vote
Your team picked Option B 9 times out of 12, without knowing which tool made each result.
03 · Roadmap
You approve every change first.
The winners go into a roadmap. Each change shows the test it won. Nothing goes live until you say yes.
Roadmap · An example deep-tech team, 6 writers
Waiting for your approvalThe main idea: each change won its test on your own past work. Nothing starts until you approve it.
- Weeks 1 and 2 Program managers: Grant sections ClaudeGoogle Docs
- Weeks 3 and 4 Engineers who write: Test reports ClaudeNotion AI
- Weeks 5 and 6 BD: Security questionnaires ChatGPTGoogle Docs
- Weeks 7 and 8Automations you approve
Needs your OK
Which tools may see customer data.
Which AI seats to add, and which nobody uses.
04 · Set up
We set it up in the tools you already use.
The automations and agents run in your own accounts. A person checks the work before it reaches a customer.
Set up from what won
Keeps a library of your approved answers
Runs in your own Docs
Drafts answers to new questionnaires
A person checks every answer
Turns lab results into report drafts
Checked against your data
Approvals
Before anything runs
The owner approves anything that touches customer data.
05 · Train
Each person learns on their own work.
Each person gets a short guide made from their own work. Then they practice on a safe copy of their tools, with an AI coach that answers questions. It is not a generic course.
Practice lab
Sample data- Sample call: project narrative
- Sample call: budget justification
- Sample call: data management plan
Draft the section from the sample past application.
Paste the call and your past section into the setup.
Done. The draft reads well.
Check the numbers. Does the draft match the past budget?
Every number in a grant comes from your own documents, never from the draft.
Live coach · next to Google Docs
OnStep 2 of 3: draft the section
Can I reuse the old team bios?
Yes, after you update titles and dates.
Show me onceor do it yourself
06 · Next round
When better AI ships, the next round starts.
Every month we flag new AI worth testing, and you pick up to three. If one wins its test on your work, it replaces the old tool. If nothing wins, nothing changes.
Monthly check-in
New AI worth testing for your team
Using this round’s change
Pick up to 3 to test
- A newer model for grant sections
- A new tool for test reports
- An agent for security questionnaires
If one wins its test, it replaces the old tool. Nothing changes until you say yes.
Before and after
What changes for your team.
Before
Tools picked years ago, and never revisited
AI seats that few people use
Guessing which new tools are real
Training that is not about their work
After
The tool that won on your own work, for each task
Set up in the tools you already use
Each person trained on their own work
A new round whenever better AI ships
Who we are
Two Berkeley founders who build with AI every day.
Google and MIT
Our CTO is first author on AI research with both.
Databricks
Our CEO was an AI and machine learning consultant there.
Berkeley SkyDeck
Supported by Berkeley SkyDeck, Founders Inc. and UC LAUNCH.
Abhiraam Eranti
Co-founder and CTO
First author on AI research with Google and MIT. Technical lead on a U.S. Treasury app for 100,000+ blind and low-vision Americans.
Waleed Chaudhry
Co-founder and CEO
AI and machine learning consultant at Databricks. UC Berkeley Haas School of Business.
We also built LessonOrca, an AI tutoring company that writes a plan for each student and rewrites it after every session. Orcapo uses the same system for teams at work.
Questions
Questions
Is this a course?
No. We find, test and set up the tools, then train each person on their own work, in the tools they already use. There is no course library.
Why not just buy ChatGPT or Claude seats?
Seats give your team a tool. Nobody picks the tasks, tests the results or checks that people use it. An AI vendor will also recommend its own tools. We recommend whatever wins on your own work, even when that is a rival’s tool.
How often do our tools change?
Only when something wins its test on your work and you approve it. Most months, little changes. We keep the screens your team knows wherever we can.
What happens to our data?
You connect your tools as read-only, and we test new AI in a safe copy, so nothing real can break. Automations run in your own accounts, and you keep everything we set up.
Who approves changes?
You do. Every change shows the test it won, and nothing goes live until you say yes.
How much time does it take each person?
A 5-minute form at the start, a short vote on test results, and about 15 minutes of training for each new tool.
What happens on the audit call?
A short video call. We ask how your team works today and show you where better AI would save the most time. There is nothing to sign.
Our promise
We only recommend a tool if it beats your current one on your own work.
Nothing changes until you say yes.
Book a free AI audit.
Pick a time