Exclusive: Startup Adapter Launches With $17.8M To Bring New 'Cognition' To AI Tools
Repeat founder Adam Ghetti returns with a startup backed by GV and others to provide better 'cognition' for your AI use -- all while keeping your data safe from labs like OpenAI.
The Deep Dive
Adapter CEO Adam Ghetti didn’t expect to be building another startup after selling his previous venture – the product of a decade’s work – to Twilio five years ago.
Then the transformer emerged, and many of the smartest people he knew in tech started racing towards its obvious applications: text generation and large language models.
“It was obvious to us that 10,000 other people were going to go do the content thing,” he says.
For Ghetti, those “flashiest” uses missed the most important questions: how could we use technology to truly understand our own digital footprint, our own precious data? More importantly: if we got there, how would we control those treasure troves, so that we weren’t putting the big AI labs like OpenAI in charge of our own “personal worlds”?
“Ten years from now, could I sleep well knowing I could have helped and didn’t? I couldn’t,” Ghetti says.
Then, as he puts it, a prominent computer scientist focused on data science, professor David Bader (formerly of Georgia Tech, now at the New Jersey Institute of Technology) and a VC firm that had previously backed him, GV, called his bluff. In November 2022, just days before the public launch of OpenAI’s ChatGPT, they got started on a new startup: Adapter.
Now, after three and a half years of operating in stealth, Ghetti is unveiling Adapter to the world. Based in Austin, Texas, the startup employs 17 people and has raised $17.8 million in capital to date from GV, Bond Partners (an early-stage fund within BOND), and a number of other investors including Eric Schmidt’s family office Hillspire, Byers Capital, the funds of former Lookout founder Kevin Patrick Mahaffey and Shana Fischer, and personal investors including Paul Judge, Zach Sims and Ted Schlein.
What they’re betting on isn’t a new AI model or app, but what Ghetti calls ‘Cognition as a Service’ – a new-look infrastructure layer that is intended to better leverage and control your personal and work data for use by AI agents and applications.
Adapter is making available a developer tool and API, called Adapter Mind, intended for developers and tinkerers to start using immediately to connect to existing applications, and to spin up new ones. Users can drop Adapter’s model context protocol, or MCP, into whatever tools they prefer, like OpenAI’s Codex, Anthropic’s Claude Cowork, or Cursor.
Adapter says it will continue to set up Slack channels and ship best-practices and starter kits in a public library to help early adopters test the limits of what it can do in upcoming weeks.
It’s also shared with test users a consumer-facing harness, called Adapter Life, as an example of a tool that can build upon Adapter to operate like a super-charged chatbot or agentic assistant, communicated with via iMessage or WhatsApp.
“Adapter is designed to adapt to your life, your workflow, your needs,” Ghetti says.
But Adapter is explicitly not a new model company or agent builder itself.
Instead, it’s looking to establish new ‘primitives’ that Ghetti compares in potential impact to Amazon Web Services’ release of S3 online data storage, which helped to kick off a new era of businesses from Dropbox to Zoom.
“We don’t want to produce many things, but the things we produce, we hope will enable a whole new world of new things to do,” he says.
If that sounds wide-reaching, even amorphous, it is: Adapter is an ambitious, big-swing venture that looks headed for binary outcomes: kicking off a whole new wave of AI capabilities and startups if it succeeds; failing to land a killer use case and collapsing in on itself if it does not.
But Adapter is already provocative across three hot-button topics in AI today:
the token economy and efficiency of AI spend
capabilities and true ROI from AI tools
transparency and data sovereignty
On the cost front, Adapter claims its knowledge graph, in large part because it lives online using CPUs, not costlier inference-friendly GPUs, is much cheaper to set up and maintain than spending on model tokens.
From a capabilities perspective, Adapter and its advocates say its graph unlocks context currently lacking even from the leading AI interfaces, because it’s not context limited.
And on the privacy and control standpoint, Adapter says it’s a better “custodian” of your data because it doesn’t access or train models off of it, and because Adapter can then parcel out that info to the model labs and wider web on a need-to-know basis, keeping their hands off, too.
Of note: Adapter’s CEO says it currently partners with a number of the big labs behind the scenes, but explicitly not OpenAI. (“Our entire mission is built around trust, transparency and sovereignty. OpenAI does not appear well aligned to these,” Ghetti says.)
Here at Upstarts, we spoke with several backers and users of Adapter’s tools – and went through the onboarding process ourselves to see it first-hand. Our impressions, and Adapter’s efforts in each of those three areas, below.
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The prime directive
To sign up for Adapter, you give it access to a personal email initially – the idea being that this hub of your data is long-lasting, perhaps more so than any particular job – and then connect a bunch of tools to it, like Google Drive, Notion and Slack. The savvier can set up their own custom connectors to access photos or old file repositories; the more you feed in, the smarter it gets.
Ingesting it all takes a while – for me, 15 minutes or so – and then you can start asking questions. In a demo, Ghetti demonstrates Adapter correctly identifying the earliest touchpoint between us, more than ten years ago when I was at Forbes, despite there being no obvious article link or paper trail of that moment. He’s also used Adapter to write investor updates that know the depth of his relationship with each investor; on the personal side, Adapter Life (his is called Ghetti Life) knows everything about his kids’ pediatrician visits.
Mahaffey, the former Lookout founder and an early backer and user, asks Adapter to serve as a research assistant to update him on any developments in geothermal energy, a current area of focus, as well as for reviews of mountain e-bikes using a new motor he finds promising.
Erik Nordlander, a general partner at GV who previously backed Ghetti’s last startup, Ionic Security, has used it to triage his inbox. His Adapter flags qualified experts and executives in his network to help answer inbound requests from founders, beyond what he says he can do in LinkedIn.
Adapter’s able to do all of it in large part because its system takes the time to understand the “prime intention” behind any task or query, Ghetti explains. (The company has a patent on its prime directive work.)
When using Adapter directly, or when another tool calls the Adapter API, it can then run as few or as many loops of searches as needed to get the answer it believes you need, at the depth it predicts you want. The more you use it, the better your Adapter Mind gets at balancing that.
Unlike an LLM, Adapter doesn’t forget context over time, or inject unwanted context from a past conversation. And unlike many agents that load up a prompt behind the scenes to re-search for all the relevant information, once Adapter knows something, it stays that way.
“You can do this with all the questions you wouldn’t typically think to ask an AI, because it has nuance,” Ghetti says.
Pricing power of CPUs
How is Adapter doing it? The big breakthrough is the knowledge graph, explains Bader.
Bader’s government-level work and Ghetti’s experience in data security allowed them to build with less than $20 million a system they argue might take a corporation hundreds of millions to set up.
Adapter’s graph only has to read your data one time; then it maintains it on a secure, always-updated private website hosted through Cloudflare. Adapter continuously updates that information as new data points (an email, a Slack message, a sale) come in, but once Adapter knows something, it doesn’t necessarily need to call an LLM or search the wider web, only doing that for “truly novel” questions.
That allows Adapter to ingest many gigabytes of data and sift through much of it for just one query, far exceeding the context windows of most AI models, Ghetti argues. And because the data is in memory, performant on CPUs, they can do much of that searching locally, on device, and using smaller parameter models.
The hope for Adapter is that it can then pass on much lower costs to customers, making it a cheaper and more efficient building block for other applications, and making it more practical for prosumer users. (In my personal use case of managing email inboxes, I’ve hit token limits asking AI agent applications to ingest and label the high volume of PR pitches I get.)
It all adds up to “bell curve” cost to unlock “log curve” value, Ghetti argues, which means Adapter is cheaper to operate the longer it works with a customer. “The delta from ‘never’ to ‘now’ is a lot, but the delta incrementally between ‘now’ and ‘what’s next’ is very small.”
Guardians of your galaxy brain
In an era in which AI labs and advertisers are both hungry for your data, Adapter’s supporters describe it as a third option in a brewing battle between secure walled gardens of data that must be entrusted with one company and not shared widely, and open ecosystems where your privacy and data aren’t secure.
Adapter users who access models from Anthropic, OpenAI or elsewhere don’t share their specific data with the LLM, Ghetti notes, because Adapter sends the models an aggregate stream of traffic.
Mahaffey compares it to a data ‘Switzerland,’ sharing your data on a need-to-know basis that could serve as a compromise moving forward for advertising and agent-to-agent communication.
Imagine a model or site communicating with your Adapter graph to share a few versions of an ad, he posits. Instead of the company scraping all of your data and web behavior to target the ad to you directly, Adapter could respond with what ad might resonate best, without sharing any additional info.
“We need a world with less noise, not one with more,” he argues.
At GV, Nordlander argues that even the tech giants can find value in such infrastructure: “I don’t think in OpenAI’s wildest dreams they think you’re going to be doing all of your productivity stuff, and your social stuff, in the model.”
Ghetti, for his part, sees himself as a ‘custodian,’ or protector, of your data. When I note the irony that a startup founder is effectively asking people to trust it – and him – with that data in order to secure it, Ghetti responds that users can verify their own data security cryptographically with Adapter’s documentation.
He’s also exploring legal ways that Adapter can establish itself as a long-term good actor more structurally, and says he has no plans to ever cede board or equity control. “You should have sovereignty over your coal mine,” he argues.
Adapter’s CEO says he’s confident his startup has found a way to build a successful business model ensuring all three.
For it to work, it needs one thing above all: people to try it out.
“When people feel like the information and understanding asymmetry in their life begins to balance in their favor — not just because they come to understand more, but because they come to achieve more simply — when that is true, we are winning,” Ghetti says.