Everything agents need to find, read, use, and trust your product
AI agents reach your product four ways: search, fetchers, browsers, and tool calls. Our services map to all four, across your entire application.
Four layers an agent moves through
Every agent interaction follows the same path: it has to discover you, read your content, act inside your product, and you have to verify it all keeps working. Our services are organized the same way.
Discover
AI Discoverability (AEO)
Read
Agent-Readable Content & Docs
Act
Agent-Navigable UI, Native Flows, MCP, and WAF Tuning
Verify
AX Audit & Scorecard
AI Discoverability (AEO)
When a user asks an AI assistant "find me a tool that does X," agents extract and compare capabilities, pricing, and limits across vendors. If yours aren't machine-readable, you're invisible before the evaluation even starts.
- Schema.org / JSON-LD markup: Organization, SoftwareApplication, FAQ, pricing, reviews
- Answer-extractable pricing and feature-comparison pages
- Content architecture structured for clean answer extraction (clear claims, tables, definitions)
- Capability and limits pages written for machine parsing, not just marketing
Agent-Readable Content & Docs
Developers increasingly never read your docs: Cursor, Claude Code, and Copilot do so on their behalf. Cheap fetch-based agents pull pages as markdown and fail completely on JS-only content. If your docs are bloated, unrendered, or undiscoverable, the agent writes broken integration code and the developer blames your product.
llms.txtandllms-full.txtgeneration and maintenance- Clean markdown (
.md) endpoints for every key page - Server-side rendering / pre-rendering of critical content so fetchers don't hit empty pages
- OpenAPI / AsyncAPI specs published at stable URLs
- API discovery via
/.well-known/api-catalog(RFC 9727) AGENTS.mdconventions for your repos and SDKs- Token-efficiency pass: reducing context agents burn
Agent-Navigable Application
Browser-driving agents don't "see" your UI like a human; they navigate a snapshot of your accessibility tree, where every interactive element needs a stable, labeled reference. A dashboard built from unlabeled <div>s with click handlers is effectively invisible to them. This is the layer most providers skip, and it's where multi-step tasks silently break.
- Semantic HTML remediation: real buttons, links, nav, and form elements
- Accessibility-tree integrity across dashboard, settings, and every core workflow
- Labeled inputs, stable
id/name/data-*attributes, standardautocomplete - Machine-readable success and error states (not just color changes)
- Removal of agent traps: hover-only menus, focus-trapping modals, infinite scroll fallbacks
Agent-Native Flows
Beyond a single signup form: can an agent create an account, generate an API key, configure a workspace, connect an integration, invite a team, and export data from start to finish and know that it succeeded? We design these journeys to be agent-completable across your whole product.
- Agent-completable signup, API-key provisioning, and onboarding
- Programmatic alternatives to UI-only steps
- Verifiable success states an agent can confirm before moving on
- Human claim / handoff flows for agent-created accounts and sensitive steps
- Multi-step workflow hardening (config, integrations, team setup, exports)
MCP Server & Action API Development
The most robust agent interaction skips UI automation entirely. Instead of scraping your dashboard, an agent calls structured tools you expose. We design, build, and host an MCP (Model Context Protocol) server (or clean action APIs) that turns your product from a website agents scrape into a tool agents use.
- MCP server design: which actions to expose, with what guardrails and permissions
- Implementation and hosting (or handoff to your infra)
- Action APIs and webhooks for agent-driven workflows
- Auth, scoping, and rate-limit design appropriate for agent callers
- Optional: exposing your product to coding agents (Claude Code, Cursor, Codex)
Agent Access & Bot Policy Engineering
A Cloudflare challenge a human never sees can kill every agent session. Aggressive rate limits break comparison logic. Fingerprint checks reject legitimate cloud-browser and relay traffic. Almost every audit we run finds at least one of these, and they're usually invisible to the company until we show them.
- Audit WAF rules, CAPTCHA placement, rate limits, and fingerprint checks
- Distinguish legitimate agent traffic from abuse, and tune WAF policy accordingly
- Rate-limit design at the API layer instead of blanket blocking
- Recommendations on agent authentication and identity signals
AX Audit
We put fetchers, search-driven agents, snapshot-based browser agents, vision fallbacks, and MCP clients through your critical journeys end-to-end. You get an Agent Readiness Score (0–100) across all four layers, session recordings of every failure, and a prioritized roadmap mapped to the services above.
- Agent Readiness Score with per-layer breakdown
- Recordings of agents succeeding and failing on your real product
- Estimated impact / pipeline at risk per issue
- Prioritized, scoped remediation roadmap
Ready to optimize your application?
Request early access to schedule an agent readiness audit and score your AX.
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