Use Cases
Where the documented problems converge into the real-world scenarios enterprises are trying to solve—from making APIs reusable and AI context reliable, to governing agents and controlling costs across hundreds of integrations.
"Reuse doesn't scale without discoverability."
Turn reuse into a measurable, repeatable practice -- by making capabilities discoverable.
- Fewer duplicate APIs
- Faster integrations
- Less rework
- Reuse metrics tied to business outcomes
- Discovery where developers make decisions
"AI will figure it out without documentation -- it won't."
Deliver the right context to AI -- less noise, more relevance -- without MCP sprawl.
- Lower hallucination risk
- Smaller prompts, better accuracy
- More predictable agent behavior
- Standardized security review
- Right-sized context, not endpoint dumps
"Governance as a golden path, not a gate."
Agents are inevitable. Chaos is optional.
- Reduced operational bottlenecks
- Controlled exposure to agents
- Repeatable business automation
- Full audit trail for compliance
- Identity you can trust in multi-hop scenarios
"Governance as a golden path, not a gate."
Establish a repeatable, low-friction operating model for API governance across the full lifecycle -- so standards and controls are enforced consistently without slowing delivery.
- Consistent API quality and security outcomes
- Faster delivery via compliant-by-default golden paths
- Reduced shadow APIs and duplicated capability delivery
- Clear accountability for ownership and change impact
- Auditable trail that compliance can trust
"AI integration without the glue code."
Connect AI assistants to your systems through capabilities, so they can access trusted business data and actions without custom glue code.
- One integration layer for AI and API consumers
- No custom glue code required
- Normalized, task-ready payloads
- Schema-validated capability contracts
- Trusted business data access for AI
"Less noise, more relevance."
Expose only the context an AI task needs, reducing noise, improving relevance, and keeping prompts efficient.
- Reduced noise in AI context windows
- Smaller prompts, better accuracy
- Semantic discovery through rich metadata
- Typed output parameters for predictability
- More efficient token usage
"Elevate what you have. Ship what you need."
Wrap a current API as a capability to make it easier to discover, reuse, and consume across teams and channels.
- Existing APIs become discoverable capabilities
- Consistent contracts across teams and channels
- Protocol and format conversion built-in
- Schema-validated elevation contracts
- No changes to upstream APIs required
"One call. Multiple sources. Coherent context."
Combine data from multiple APIs into one capability to deliver richer, task-ready context to AI clients.
- Richer, task-ready context for AI
- One coherent result from multiple sources
- Cross-source orchestration in YAML
- Reusable source adapters
- Fewer agent tool calls, less context pollution
"Simplify what clients see. Keep what works underneath."
Create a simpler capability layer over many microservices to reduce client complexity and improve consistency.
- Reduced client complexity
- Consistent contracts across microservices
- Standardized auth and parameter handling
- Business-oriented capability boundaries
- Hidden service fragmentation
"Extract what matters. Leave the rest in place."
Extract focused capabilities from a broad monolith API so consumers get only what they need for each use case.
- Consumers get only what they need
- Focused, purpose-built capabilities
- No monolith rewrite required
- Selective operation extraction
- Passthrough where transformation is not needed
"Design for AI first. Map to APIs second."
Design capabilities first for MCP clients, then map them to underlying APIs for a clean AI-native integration model.
- AI-native integration model
- Design-first with mocking mode
- Same capability in remote and local mode
- Clean separation of interface and implementation
- No upstream API changes required
"You can't optimize what you can't see."
Costs are unpredictable -- tokens, models, and upstream API usage. You can't govern, secure, or optimize what you can't see.
- Multi-dimensional cost visibility across APIs, tokens, and models
- Budget controls and thresholds to prevent runaway costs
- Cost attribution to teams, projects, and capabilities
- Covers all 3rd-party API spend, not just AI services
- Guardrails for agent runtimes -- spend, compute, and data access
"Start with what you have. Build what you need."
Start from existing APIs and define reusable capabilities on top, so API investments can power new AI and app experiences.
- API investments power new AI experiences
- Incremental capability addition
- Multi-format normalization built-in
- Security and config reuse via external refs
- No rip-and-replace required