Summary

Agents for every business. Praxis helps businesses embed agents in their operations and serve their own customers.

We choose to serve businesses

An agent is AI that can use tools and carry out tasks within an assigned role. We choose to build a supply-side platform: helping businesses that supply goods and services reach their customers directly, while leaving those relationships with the businesses.

Ben Thompson distinguishes platforms from aggregators: platforms help businesses build customer relationships, while demand-side aggregators control access to users. Applying that distinction, Amazon’s shopping destination, Google’s search entry point, and ChatGPT’s personal interface are positions we do not pursue; Shopify, Stripe, and AWS help businesses operate their own services, the direction we choose.

For Praxis, the relationship is platform–business–end customer, or B2B2C: we serve a business, which serves its customers. Stripe Connect offers a useful example of that relationship; the capability Praxis intends to provide is agents.

What businesses need

Imagine an online shop adding a customer service agent to its website. This is an illustrative example, not a customer case: the owner can prepare an agent that answers questions, but still has to handle overnight operation, customer data separation, usage records, failure takeover, and version updates.

Each shop should not have to assemble this from scratch. Businesses and developers need a platform that runs agents over time, paid for through actual resource use; more businesses offering agent services create more need for this layer.

What we provide

The platform design has two parts. Resource services are intended to give developers access to models, sandboxes, search, and compute. Models provide AI capabilities for understanding and generating information, a sandbox is an isolated environment for running tools, and compute is the processing capacity used to execute tasks. Customers connect through a common address and an API key, the credential used to call a service.

The other part is our own agent system, covering building, learning, publishing, running, and operating. Personal Agents act as production employees, helping developers and businesses integrate, build, and operate Service Agents; Service Agents are embedded in the business and serve its customers. Personal Agents are not a mass-market personal assistant product.

We bet on specialization and collaboration: an agent can handle only a limited amount of information at once, so agents should develop specialties and work together. Our agent system runs individuals built through conversation within Praxis and does not accept third-party runtimes. An individual is an agent with its own responsibilities and experience, and a runtime is its execution environment; external agents migrate their prompts, or instructions to the AI, and memory, the information and experience they have retained.

Our three commitments are easy to connect, affordable to use, and dependable in operation: follow the docs or delegate integration to an agent, pay for actual usage with costs falling as scale grows, and keep services running with a record of each call and its cost. These guide what we build; delivered capabilities are listed in Product & traction.

How we can build it and earn revenue

We use AI to build AI: people make decisions, while AI handles much of development, testing, operations, and maintenance. This is our AI-native production method; written rules and repeatable processes reduce the cost of building the platform.

Early revenue comes from the difference between resource selling prices and purchasing costs. Gross margin is the selling price less purchasing or direct operating costs. Model margins and margins on our own compute are resource income; selected services and platform fees are possible later sources of revenue. Tools, documentation, sales, and partners help businesses bring real work onto the platform, rather than creating a separate business competing for personal users.

How advantages accumulate

Useful services earn trust, which brings customers and partnerships; usage and purchasing relationships lower costs, funding both lower customer prices and further development. This reinforcing cycle is called a flywheel: the result of one step helps the next.

In the diagram, “price umbrella” means the room between market prices and purchasing costs for savings and margin; “purchasing premium” means better purchasing terms. “Counter-positioning,” from Helmer’s 7 Powers, means an incumbent’s existing business may discourage it from copying a new approach; “asset accumulation” and “compounding systems” mean accumulated ways of working and continuing improvements through rules, tests, and documentation.

Our view is that selecting and managing suppliers for customers can conflict with large companies’ existing supplier relationships. Agent experience and the team’s working methods also take time to build; a moat is an advantage that makes it hard for competitors to keep taking a business’s customers. These are paths toward an advantage, not an established moat.

Where we are and what comes next

The model service and Praxis App have launch and usage records. We are filling out the platform in the order of agents, sandboxes, and search, then deepening each service. Expansion is based on existing financial data rather than large R&D spending; the funding amount, detailed uses, and forecast appendix remain to be supplied.

The plan starts with what businesses lack between trying an agent and reliably serving customers.