Give your AI context about any brand
Turn any website into structured context about its products, audiences, positioning, messaging, voice, and more.
Six questions that define brand context
Brand Context gives your AI a predictable representation of the company behind a URL, instead of making the model interpret raw website content on every request.
- 01Who are they?
brand - 02What do they sell?
products - 03Who is it for?
audiences - 04Why should someone choose them?
positioning - 05What do they repeatedly say?
messaging - 06How do they sound?
voice
Need to know what they look like too? Logos, colors, and fonts come from the Brand Identity API. Building something that writes as the brand? See the Brand Voice API.
What Brand Context returns
Predictable, structured data rather than an open ended AI summary. The same fields, in the same shape, for every brand.
brand
Name, description, category, and tagline.
products
The products and services the brand offers, each with a description and URL.
audiences
Who the brand appears to be speaking to.
positioning
Value proposition, market position, and differentiators.
messaging
Key messages, themes, claims, and the phrases the brand repeats.
voice
Traits, a description, formality, technicality, and energy scores, plus do and avoid guidance.
Start with their website, not a questionnaire
Instead of asking every user to explain their business, products, audience, positioning, and voice, start with their website and give your AI the context it needs.
JSON for applications. Markdown for models.
Both formats come from the same Brand Intelligence result. The JSON is what gets cached; Markdown is rendered from it when you ask for it.
JSON for applications
format=json- Applications
- Structured workflows
- Field level access
- Automation
- Programmatic decisions
Markdown for models
format=markdown- System prompts
- Agent context
- Model grounding
- Inserting directly into prompts
- RAG and context building
# Asana
Work management and collaboration software · asana.com
> The OS for human-agent teams
Asana is a work management platform where teams and AI agents coordinate critical workflows on shared plans, with connected context, automation, reporting, and enterprise governance.
## Products
### Agentic Work Management
A platform for people and AI agents to run critical workflows together, including AI Teammates, Asana Dash, AI Studio, and AI Connectors and MCP.
https://asana.com/product/ai
### Asana Service Management
An AI-native service management platform for IT, HR, facilities, and legal teams.
https://asana.com
### Asana Client Management
An AI-native platform for teams delivering client work, including agencies.
https://asana.com
### Command by Asana
A work management product for developer teams to ship faster with humans and agents in sync.
https://asana.com
### StackAI by Asana
A no-code platform for creating agentic workflows connected to business applications with enterprise governance.
https://asana.com
### AI Teammates
Prebuilt AI agents that work alongside teams inside real workflows, with shared memory, governance, and work context.
https://asana.com/product/ai
### AI Studio
A no-code automation tool for intake, task classification, routing, alerts, and other structured work processes.
https://asana.com/product/ai
### Asana Dash
An AI chief of staff that surfaces next actions and routes work to the appropriate AI Teammate.
https://asana.com/product/ai
### Goals and reporting
Capabilities for connecting team work to company objectives and monitoring progress through goals, portfolios, and real-time reporting dashboards.
https://asana.com/features/goals-reporting
### Admin and security
Administrative, access-control, data protection, compliance, and security capabilities for managing Asana across organizations.
https://asana.com/features/admin-security
## Audiences
- **Cross-functional teams** — Teams coordinating shared workflows, projects, goals, and dependencies across functions.
- **IT teams** — IT organizations managing service delivery, enterprise applications, procurement, operations, intake, and approvals.
- **Service teams** — Teams in IT, HR, facilities, and legal that manage structured service requests and workflows.
- **Developer teams** — Developer organizations that need to coordinate work and ship faster with humans and AI agents.
- **Marketing and operations teams** — Teams using AI agents, automation, project management, and reporting to manage cross-functional work.
- **Agencies and client-service teams** — Agencies and teams delivering work for clients and managing ongoing client relationships.
- **Enterprise organizations** — Large organizations requiring governance, security, compliance, administration, and scalable work management.
## Positioning
**Value proposition:** Asana helps teams and AI agents run critical workflows together from one shared plan, connecting people, tasks, projects, goals, dependencies, and operational context.
**Market position:** An enterprise-scale work management and agentic work platform for cross-functional teams, with governance, security, integrations, and AI built in.
**Differentiators**
- Work Graph® connects people, agents, tasks, projects, goals, and dependencies in one shared context.
- AI Teammates, AI Studio, Asana Dash, and AI Connectors support coordinated human-agent workflows.
- Enterprise governance includes permissions, approvals, audit trails, usage controls, and centralized administration.
- Broad integrations connect Asana with existing business systems rather than requiring rip-and-replace.
- Strong enterprise security, privacy, compliance, and data-residency capabilities.
## Messaging
**Key messages**
- Bring humans and AI agents together on one shared plan.
- Connect strategy to execution with visible goals, projects, owners, deadlines, and dependencies.
- Automate routine work while keeping people in control of approvals, exceptions, and decisions.
- Replace scattered work, stale status updates, and unclear ownership with a coordinated system of record.
- Scale work confidently with enterprise-grade security, governance, integrations, and reporting.
**Themes:** Human-agent collaboration, Shared context and memory, Visibility and coordination, AI-powered productivity, Enterprise governance, Security and trust, Strategic alignment, Automation and scale
**Claims**
- 85% of Fortune 100 companies choose Asana.
- Asana integrates with 270+ apps.
- Asana’s AI partners do not use customer data to train their models and are required to delete customer data after each query.
- Asana offers a 99.9% uptime commitment to Enterprise customers.
- More than 100,000 enterprises trust Asana to keep their data safe.
**Common phrases**
- The OS for human-agent teams
- Where humans and agents run critical workflows together
- one shared plan
- AI that works the way your team works
- Your AI chief of staff
- Get your team and your agents working together
- See the big picture at a glance
- Keep work moving
## Voice
Asana communicates in direct, benefit-led language that combines practical workflow outcomes with ambitious AI and enterprise positioning. It emphasizes clarity, speed, visibility, control, and human oversight.
**Traits:** Clear, Confident, Outcome-oriented, Enterprise-minded, Helpful, Action-oriented, Technically credible
Formality 0.65 · Technicality 0.62 · Energy 0.72
**Do**
- Lead with a clear operational benefit.
- Frame AI as working alongside people, with humans retaining control.
- Use concrete workflow language such as owners, deadlines, dependencies, approvals, and next actions.
- Reinforce visibility, alignment, speed, and measurable outcomes.
- Pair innovation claims with security, governance, and trust assurances.
- Use concise calls to action such as “Get started,” “Learn more,” and “Explore.”
**Avoid**
- Presenting AI as autonomous without human oversight.
- Positioning Asana as requiring customers to replace their existing tools.
- Vague productivity claims without a workflow or business outcome.
- Ignoring enterprise security, permissions, governance, or compliance concerns.
Need something outside the standard schema? Ask for it.
A fixed schema covers what most applications need, but AI products often need something more specific. Ask brand specific questions in the same request instead of waiting for a new field.
Up to three questions per request. Answer types: string, boolean, number, string[], or object.
"questions": [
{
"key": "buying_triggers",
"question": "What situations would make someone likely to buy from this company?",
"type": "string[]"
},
{
"key": "enterprise_focus",
"question": "Does this company actively target enterprise customers?",
"type": "boolean"
}
]"custom": {
"buying_triggers": [
"Work is scattered across tools and teams need a shared view of tasks, owners, deadlines, and dependencies.",
"Projects are slipping, blocked, or at risk because progress and status updates are not visible in real time.",
"Teams need to automate repetitive work, coordinate AI agents, or embed AI into existing workflows."
],
"enterprise_focus": true
}Use the standard Brand Context when it fits. Ask for more when your application needs it.
OpenGraph researches the brand so your AI doesn’t have to
More useful than putting a homepage into a model: OpenGraph picks the pages that describe the brand, analyzes them together, and returns one normalized result.
Need explainability? Add include_meta to see the source pages behind each field and how it was produced. The normal response stays compact.
observedRead directly from the site, such as links and social profiles.derivedComputed from the homepage markup and styles, such as colors and fonts.inferredWritten by the analysis from the site content, such as positioning and voice.Where brand context makes AI output better
AI content generation
Generate copy using the company’s actual products, audience, positioning, messaging, and voice.
AI agents
Give an agent enough context to understand the company it is acting for or talking to.
Sales AI
Understand a prospect’s offerings, positioning, customers, and messaging before generating outreach or recommendations.
Website generation
Generate pages and copy grounded in the company’s existing messaging and positioning.
Advertising
Create campaigns based on real differentiators, audiences, claims, and products.
Presentations and video
Give generative systems consistent company and brand context before they produce anything.
AI powered onboarding
Derive context from a company domain instead of asking users to describe their organization.
One Brand Intelligence endpoint. Request only the context your AI needs.
Brand Context is a way of using the Brand Intelligence endpoint. Turn off identity, visual, and links and those sections are skipped entirely.
curl -X POST "https://opengraph.io/api/1.1/brand/https%3A%2F%2Fasana.com?app_id=YOUR_API_KEY" \
-H "Content-Type: application/json" \
-d '{
"format": "markdown",
"sections": { "identity": false, "visual": false, "links": false }
}'format- json (default) or markdown.
sections- Turn off any section you do not need, such as { "visual": false }.
questions- Up to three custom questions, answered under custom.
include_meta- Adds provenance: source pages and a basis for each field.
cache_ok- Set to false in the query string to force a fresh analysis instead of a cached result.
Skip the brand research pipeline
Every step below is work you would otherwise build, tune, and maintain just to ground a model.
We build the brand understanding pipeline so you don’t have to.
Brand Context is powered by OpenGraph Brand Intelligence
Brand Intelligence returns the complete structured representation of a brand, including semantic context, identity, visual information, logos, colors, fonts, and more. Brand Context focuses on the information most useful for grounding AI systems and agents.
Explore Brand Intelligence API