We built the world's most robust AI memory infrastructure, then we made it free to start. Whether you are validating a startup idea this weekend or re-architecting a Fortune 500 platform, Backboard scales with you.
Build Fast. Scale Fearlessly.
Most devs waste weeks building "good enough" memory connectors. Don't build plumbing. Build your product.
Tinker
(The Garage Phase)
Cost: $0
Goal: Find Product-Market Fit.
Value: Grab an API key and add stateful memory to your prototype in 5 minutes. No infrastructure to manage. Just pip install backboard.
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Validate
(The Growth Phase)
Cost: Pay as you grow.
Goal: Onboard your first 10,000 users.
Value: As traffic spikes, our model routing optimizes your costs, using cheaper models for routine tasks so your margins don't vanish.
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Dominate
(The Enterprise Phase)
Cost: Volume / Enterprise.
Goal: Security, Compliance, and Massive Scale.
Value: Activate VPC peering, SSO, and dedicated support. You don't need to migrate or rewrite code; you just turn up the dial.
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Healthcare & MedTech
The App: A specialized functional medicine assistant.
The "Tinkerer" Build: A simple app where a user chats about their diet.
The "Enterprise" Scale: A HIPAA-compliant platform managing 1M+ patient profiles. Backboard creates a distinct, isolated memory store for every patient, allowing the AI to recall medication history and lifestyle nuances across years of care.
The App: An OSINT (Open Source Intelligence) news aggregator.
The "Tinkerer" Build: A bot that summarizes military news from RSS feeds.
The "Enterprise" Scale: A sovereign intelligence platform. Backboard detects sensitive queries (e.g., "Troop movements") and automatically routes them to air-gapped, on-premise local models, ensuring classified data never touches the public cloud.
The App: An automated Shopify return handler.
The "Tinkerer" Build: A chatbot that reads a FAQ PDF.
The "Enterprise" Scale: A "Zero-Repeat" support agent. Backboard ingests the user's entire ticket history. When they chat, the AI knows their tech stack and past issues instantly, routing complex bugs to reasoning models (Claude 3.5) and simple greetings to fast models (GPT-4o-mini).
The App: A contract comparison tool.
The "Tinkerer" Build: Upload two PDFs and ask for differences.
The "Enterprise" Scale: A Temporal Case Researcher. Ingest millions of discovery documents. Backboard's LoCoMo (Long Context) benchmark allows lawyers to ask, "How did the witness's story change between the email in 2021 and the deposition in 2023?" with perfect citation accuracy.
The App: A math homework helper.
The "Tinkerer" Build: A calculator that explains the steps.
The "Enterprise" Scale: A Lifelong Learning Profile. The AI remembers a student loves "Soccer" but hates "Calculus." It dynamically rewrites math problems using soccer analogies to boost engagement, tracking learning velocity across every subject.
The App: A personal budgeting bot.
The "Tinkerer" Build: A tool that categorizes bank CSV exports.
The "Enterprise" Scale: A Compliance-Aware Wealth Advisor. Backboard logs every piece of advice against the client's stated long-term goals (e.g., "Retire by 50"). It routes math-heavy queries to specialized models to prevent hallucination, ensuring audit-ready financial guidance.
The App: A CLI tool that explains terminal errors.
The "Tinkerer" Build: A wrapper around OpenAI.
The "Enterprise" Scale: A "Project Brain" for engineering teams. Backboard integrates with Git and Jira. When a new hire asks, "Why do we use this library?", the AI retrieves the architectural decision record from a Slack thread two years ago.
The App: A call transcript summarizer.
The "Tinkerer" Build: Send Zoom transcripts to GPT-4.
The "Enterprise" Scale: A Deal Room Intelligence Engine. Backboard maps stakeholders across months of calls. It prompts the sales rep: "The CFO mentioned budget cuts in Q1—make sure you emphasize ROI in today's demo."
Code First. UI Optional.
You don't need a sales call to get started. You need an API key.
Python
import backboard
# The code you write today works for 1 user or 1 million.
client = backboard.Client(api_key="bk_...")
memory = client.add("User_123", "User prefers dark mode and uses Python.")