Salisbury Ventures supports research-driven founders in finding product-market fit for their prototypes.

Since 2015 I have been meeting technical founders and been helping them to iterate on their prototypes for free.

With our portfolio companies we not only invest into them, but also spend 3h-5h per week with and supply them with other resources that they may need. 

Areas of interest

We invest in exceptional technical founder teams working on top of potential platform shifts - usually ahead of them showing any product-market fit.

In terms of potential platform shifts Salisbury Ventures is currently interested in:

  • AI, but mostly conversational AI

There is a very popular argument that suggests the big (US and Chinese) internet platforms will essentially “own” multi-industry A.I. markets because “A.I. is an industry in which strength begets strength: The more data you have, the better your product; the better your product, the more data you can collect.”  In our view

  1. this is true to a certain extent for application in image recognition, self-driving cars, speech-to-text, and basically anything where everyone tends to agree on the ‘right’ answer. But there is not always just one answer.
  2. If, however you are trying to build something new with ML, or learn something new about your data by applying it, then this is a completely different game. There will be many vertical winners.
  3. But even in specific vertcials, there's also many data types such as text which we "data network effects" VCs are keen on unfold in a very different way than generally talked about and we are very bullish about that.
  4. There's also all kind of trends towards horizontal plays.

You can read more about our related thoughts and observations in the article I linked to above. 

  • the rise of MPP databases such as Redshift or Bigquery

Lars's post here points to many of the reasons why I find MMP databases interesting. I also wrote how Redshift is the twin brother of the rise of Facebook revenue ("Face-shift").


SV Resources 

Initial product is not taking off and the team is stuck in iterating on a weak product-market fit 

SV tool box - broad cohort testing - Very early on tech-intensive teams should not go for incremental changes. Instead we support you with testing various substantially different cohorts very quickly to get signs of 10x opportunities. We show you how to do this and then spend time with you doing it, week by week."

The team has a rough prototype, but no data to test or train it

Access to data - If we like your idea we supply you with access to APIs and database 

No clients to test your prototype

Access to clients - We use our network to find some initial clients to test out your crappy beta product. We help you to overcome the problem of not yet having a reputation and convincing a customer to try something really new. 

No cash & no traction to raise


Grants & referrals to trusted accelerators such as TechStars & Y Combinators - We frequently refer companies we like. Referred startups in the past include and We also invest ourselves and also help our portfolios to get additional grants to get the "traction" that validates your "deep" tech startup in the eyes of VCs.

Engagement model and how we de-risk our investments 

At SV we don't accept pitch decks or Excel sheets that show traction. However, as every investor we have our own model to de-risk our investments. We invest in technical teams that show us signs that they can ship code fast to potential clients.

Monthly lunch - 1h/month

If you do anything which is interesting to us (such as AI or something interested with MPP databases) we are happy to go for lunch with you regularly.  

SV has, on average, usually 7-9 startups at this stage it talks to each month.

Lunches & Initial Resources to help you prototype better, faster - 5h/month 

Typically, at this stage, we understand your personas and use cases. Once we see you doing so much more, we are happy to give you access to some of our resources. We do this still for free #givefirst.  The aim of this stage is to help you prototype better and quicker. We aim to learn with you and also see whether we are a fit to help you. If all parties agree that we are able to help, then this may morph into an Advisory relationship. 

SV has, on average, usually 3-4 startups at this stage it talks to each month.

Portfolio - 3h to 5h per week 

At some point we may bump up the relationship even more and invest into you alone or we even organise a whole round. Doing technical startups outside of the Valley is a little bit more difficult, though. When we came to Berlin in '10 we counted 16 resource types that were missing for technical startups with the list now down to 7. That's why spend a lot of time every week with you to compensate.   

SV has, on average, usually 1-3 startups at this stage.


Case Study - From Treev to Rasa

Treev was a productivity plugin Alex and Alan worked on when we met them. Their initial view on product-market fit was that "Treev, similar to Dropbox, is for everyone." Together we did a user cohort analysis which identified 4 distinct user types and +10 user jobs. We then engaged in structured PMF process with over 200 user interviews across over 2 months.

The results were:

  • A deep understanding that there was no market for Treev
  • An founder team with a skill set and speed to iterate on future PMF  



Alexander Weidauer, Co-Founder & CEO, RASA

"Early on SV helped us to demystify the concept of product-market-fit, iterate much quicker and get and assess feedback to our high-tech prototypes. Today, two years into running a "deep tech" startup, I see that this is very rare. There's tons of public know-how and angels for operational startups, not that much for high-tech ones. Most of my peers don't understand how valuable frequent and quick feedback is when you try to apply "deep" technologies. Before we met Matthaus, we didn't think of our users as different cohorts of people with different needs and different problems. In one of our first sessions, we setup a well structured cohort spreadsheet that allowed us to better understand why certain people used our product and why others didn't. Together, we developed new assumptions, tested them with different cohorts (e.g. product managers) and iterated every week on that. Matthaus went through this process three times with us before we started what became RASA and we shipped RASA NLU. Matthaus has been building products for "emerging" technologies and markets for over 7 years. This means, for example, that when we aimed to build and launch an open-source NLP tool vis-a-vis the big platforms, he was there for us to discuss and implement tactics - because he had done similar things before. On top of his toolbox, he gave us access to data, customers, people from his network that help us set up operations and other resources like grants that are super important as an early stage company. After meeting so many startup people through Techstars, I can assure that Matthaus is very unique with his hands-on and no-bullshit attitude. Working with him genuinely challenges us every day to go the extra mile and results in better product and processes."

"In the engineering department at Cambridge I spent years collaborating with very good people in computational physics and machine learning, and checking results carefully became second nature. When you want to make a claim in science the evidence has to be really solid, so I would always check all my parameters were converged, test for artifacts in the data, compare to baselines, etc. SV taught me a leaner way to be data-driven that's key for startups. One of the most valuable things founders can do to improve is making their feedback loops faster. We now often go from idea to shipping code to customer feedback to making an important product decision within two weeks. I work on building deep learning models, often in uncharted territory, and this rhythm forces me to focus on improving the things that really matter and dropping 'nice-to-haves'. I have to be willing to make decisions with only 50% of the data I'd like to have. I just can't spend 6-9 months and hours of discussion with collaborators to come to a conclusion. I still test things systematically, but I've learned to do it fast and to see when 1-2 data points are enough to prove a point. SV is a constant sounding board in this process. Also, as a CTO I have to be careful where I invest resources, because I just don't have dozens of students to try every permutation. SV moved me very far ahead on this learning curve."

Alan Nichol, Co-Founder & CTO, RASA