Most acquirers see AI as a cost story. They run what they buy with less, often rebuilding working systems from the ground up, a gamble their customers cannot afford.
vs.
We see a growth story. We unlock software already in production for its next generation of users, repackaged for agents, without disrupting the pathways today’s users rely on. We believe this opens a far larger market at a minimal added cost.
Systems
One system, from the networklayer through the businesslogic.
A reference implementation of the complete software supply chain, built and operated by General Agentic. Multi-tenant, cloud-agnostic, and model-independent from the ground up.
1 / 3
Network
Our own network, compute and data fabrics lead to fixed-cost compute. Owning the root of the stack keeps the economics predictable as agentic usage grows.
Supply chain
The machinery that moves software to its new users: continuous integration with real-time delivery, identity, connection APIs, permissioning and governance, and an SDK for seamless handoff. One integration at the core, not one per platform.
Business logic
The validated logic of the software we acquire, served to agentic systems at full fidelity. We do not rebuild it, we deliver it.
Our founders run the firm on this system, including the application that built this very page. We were its first users and the companies we partner with are its next.
Partnership
We partner with companies through acquisition.
Our process
We acquire established software companies and take majority control. Your name stays on the door, your customers keep the product they rely on, and when their agents arrive, it is still your program powering their work.
Your customers bring their agents, you bring the logic they trust, and General Agentic brings the technology that connects them.
In this era, software splits into a rewrite or a repackage. We buy where the natural path is a repackage. We keep the system of record, we do not rebuild the core, and we connect your product, as it exists, to every agentic workspace your customers use.
1 / 3
Phase 1 (First and Second Quarters)
Connect identity, leave the product alone
Nothing changes for your current customers. We connect your identity system and APIs to a governed distribution layer, and together we configure permissioning so an agent can work at the same fidelity a human user can. You control what is exposed, and to whom. People keep the sign-in they already have.
Phase 2 (Third and Fourth Quarters)
Open a governedroute into the workspaces they already use
Your product becomes reachable from ChatGPT, Claude, and the other surfaces where work is starting. To users, it's the same product, under the same entitlements, just delivered through a new channel. Usage starts to climb, and we can test pricing models that are a win-win with the customers rather than only seats.
Phase 3 (Second Year Onward)
Ship the roadmap through that route
Development continues as you planned. Every new feature can reach those workspaces without a second architecture. Your team inherits the tooling to do that at product velocity. Your customers bring their own agents and pay their own inference. The roadmap starts growing where the new users are.
The results show up where it counts: new users on the same product, more revenue per customer, and a channel you can price, because the work now starts where your customers already work.
How we help
Growth is our focus, efficiency is our engine. We study the frontier of Silicon Valley and Wall Street, prove it out on ourselves, and install what works in the companies we own. We relentlessly source and test tactics and technologies that help our companies grow faster, more efficiently.
Growth
Build
Product Development & Velocity
We help you build for agents, faster. This requires new surfaces, new permissions, and new features designed for agentic users. Your team starts from our working system, not a blank page.
Sell
Sales, GTM & Customer Success
New users deserve new pricing. Agents don’t buy seats, so we help you price what the product actually delivers: usage, tiers, and outcomes without breaking contracts. Buyers increasingly choose software their agents can reliably use, and every agent they deploy expands the account while tying your product deeper into their work.
Efficiency
The stack
Software Supply Chain
Success in the agentic channel multiplies usage, and metered infrastructure bills you for it. Over time the supply chain centralizes onto the one system we operate, the way a well-run fleet is renewed: opportunistically, piece by piece, as each part reaches the end of its useful life.
The business
Finance, Accounting & Back Office
Every company carries work that has nothing to do with its product. Finance, accounting, payroll, and reporting benefits as our portfolio scales. Your team’s time goes back to making the best products for your customers.
What we don’t change
You spent years earning your brand and your customers’ trust. That is the asset we are buying. We keep the core product, the name on the door, and the management that built it. Although we take majority control, we do not hire a new general manager and our structure allows additional partners to participate as the platform scales. Our technology and our capital exist to compound what you built through the next platform shift.
Harnessing the raw power of intellect and combining it with repeatable tools proves value.
By and · September 2026 · New York, NY
When someone hears “Einstein” they think “smart”. It is one of those words that has perfect association, just like Uber for a taxi, Goldman for finance, or Kleenex for tissue.
Much of the AI era thus far has been about the improvements in how “smart” or “capable” a model is in its most basic form. Anthropic highlighted that in Claude Opus 4.6, the model thinks more deeply and plans up front before it acts versus calling tools from an early state. The industry, it seems, is incessantly focused on giving the world “a million Einsteins” all working with superhuman intellect to deliver solutions to previously unsolvable problems.
The issue is apparent with that very sentence: unsolvable problems. Solving an unsolvable problem wins the Nobel Prize, it does not build a profitable business. Extending this analogy, if we were to drop the singular Albert Einstein himself into a Goldman Sachs bullpen, there is no reason to think he would make a good analyst.
The analyst’s intellect is innate. The harness is discipline. The tools are the canvas of Excel and the data of Bloomberg. Take away Excel and that complicated three-statement model goes from an everyday chore to a work of art. Analysts differentiate by showing what they can do given the same bench of tools. Goldman Sachs profits by creating a set of incentives that enables the analyst to push themselves to the edge of the efficient frontier.
As we explored in our article Agent in the Loop, we view AI Agents as a form of Labor (L) and software tools as a form of Capital (K). The Goldman analyst is a worked example of that map: the agent’s intellect is innate to the LLM that is chosen. The harness, in this analogy, scopes this raw intellect down with discipline directed towards an objective. The tools are the software systems that are accessible to the agent (today that is in the form of MCPs and APIs). Agents can differentiate by showing what they can do given the same bench of software tools, because they have a different configuration of intellect (based on the model) and harness (based on the system of discipline).
Evaluation is the natural extension of this analogy. An analyst is evaluated on quality and speed. The best analysts can serve as a source of stability for a team of about one hundred people, playing almost any role needed on short notice. All analysts in the investment banking division start off in the same staffing queue. Proving oneself on each successively harder project earns the right to be staffed on the next one. By the end of the two-year analyst program the difference in opportunity is vast. The basis of this evaluation rests on the interchangeability of analysts and deals. No ask is a true re-invention of finance. The quality benchmark is defined. The timelines are known in advance. You can swap one analyst for another analyst with little notice to test their capabilities.
The same will apply for agents once timelines are known and quality benchmarks are defined. And at that point, evaluations will move from esoteric eval loops that are more akin to laboratory experiments, to performance reviews that are today recognized as 360° reviews.
Intelligence isn’t the frontier. It is about harnessing the raw power of intellect and combining it with tools to provide value.
