Understand How
AI Experiences
Your Product.
Glintbase evaluates how AI systems discover, understand, reason about, and operate your software across documentation, APIs, SDKs, onboarding, and developer workflows — revealing hidden friction that traditional software audits never measure.
Illustrative sample assessment
Authentication guidance is fragmented across four documentation surfaces.
Your software works for humans.
But does it work for AI?
Software has historically been designed for one class of user: a human being who reads, clicks, infers, and recovers.
AI introduces a fundamentally different class of user. Coding agents, autonomous systems, and AI assistants do not browse your product. They retrieve, traverse, execute, and infer. They cannot tolerate ambiguity the way a skilled developer might.
This changes what good software means. A product can be visually polished, well-documented, and technically sound — and still be structurally hostile to the AI systems increasingly operating on your behalf.
Six domains of agent operability.
Structure, discoverability, context quality, canonical references, and fragmentation across all documentation surfaces.
Endpoint discoverability, authentication clarity, workflow continuity, and execution readiness across all API surfaces.
Installation flows, dependency resolution, example reliability, runtime validation, and onboarding continuity.
End-to-end agent journey simulation across sign-up, authentication, first API call, deployment, and integration.
Context fragmentation, duplicate information, token overhead, and retrieval efficiency across your product surface.
OpenAPI spec quality, MCP compatibility, llms.txt presence, semantic entrypoints, and structured context surfaces.
Six research-grade deliverables.
- Agent Readiness Score
- Primary Risk Areas
- Business Impact Mapping
- Strategic Priorities
- Observation evidence
- Benchmark comparisons
- Journey simulations
- Runtime validation logs
- Prioritised recommendations
- Environment configuration
- Dependency resolution
- Execution status
- Log transcripts
- Confidence scoring
- Goal state definition
- Navigation trace
- Reasoning waypoints
- Dead-end identification
- Success path & hallucination markers
- Token waste quantification
- Context fragmentation map
- Retrieval bottleneck analysis
- Ambiguity surface identification
- Priority & business impact
- Engineering effort estimates
- Readiness improvement forecast
- Phased implementation sequencing
Seven stages, end to end.
Platform intake, stakeholder context, and scope definition. We understand your product surface before we assess it.
Systematic mapping of all agent-accessible surfaces: docs, APIs, SDKs, onboarding, and support.
AI agents attempt real-world tasks across your product. Every navigation decision is recorded and analyzed.
Code examples, API calls, and SDK flows are executed in isolated environments. Failures are documented with evidence.
All collected data is analyzed against our Agent Readiness framework. Findings are triangulated and confidence-scored.
A 40–60 page research report is produced with observations, evidence, benchmarking, and prioritised recommendations.
Findings delivered to your leadership team. Strategic roadmap reviewed, questions addressed, and next steps defined.
What a finding looks like.
Representative observations in the exact format your report uses — every finding ships with impact analysis and a concrete recommendation.
Authentication guidance is fragmented across four documentation surfaces.
Agents frequently retrieve incomplete authentication flows, causing execution failure or hallucinated credentials.
Consolidate authentication guidance into a single canonical entrypoint referenced across all surfaces.
Runtime examples depend on environment prerequisites that are not documented.
Execution confidence decreases significantly. Agents generate code that fails in real environments.
Introduce validated executable examples with explicit prerequisites and dependency declarations.
Documentation uses inconsistent terminology for the same concepts across sections.
Retrieval ambiguity increases. Agents cannot determine which term is canonical and generate inconsistent outputs.
Establish a canonical terminology reference and enforce consistency across all documentation surfaces.
Strategic questions, not feature lists.
Why do coding agents abandon our onboarding before completing setup?
Why do AI assistants hallucinate our API parameters and authentication flows?
How much unnecessary context do agents consume to complete a basic task?
Can autonomous agents successfully deploy our SDK without human intervention?
Where does our product create hidden reasoning costs that slow agent execution?
What prevents AI from completing our most common developer workflows?
Built for engineering leadership.
Products where AI agents are primary or emerging users of your APIs and tooling.
Teams whose revenue depends on how reliably AI systems can discover and operate their APIs.
Organisations embedding AI workflows into products used by enterprise engineering teams.
Internal engineering platforms consumed by AI-assisted developer tooling at scale.
Companies building tooling that AI agents depend on for reasoning, retrieval, or execution.
DX-focused teams responsible for making software understandable and operable by both humans and agents.
Built for enterprise scrutiny.
A mutual NDA is executed before any platform access or scoping conversation takes place.
We never modify your systems. Assessments run against public surfaces and explicitly granted read-only access.
Collected artifacts are destroyed at the end of the assessment window. Nothing is retained beyond delivery.
Reports are delivered directly to your team — never shared, resold, or folded into public benchmarks.
Common questions.
A standard Enterprise Assessment takes 3–4 weeks from platform access to final report delivery. Larger or more complex platforms may require additional time, which we scope during discovery.
No. We assess agent-accessible surfaces — the same surfaces AI systems would encounter in production. Source code access is not required unless you wish to validate internal documentation pipelines.
Yes. We can conduct assessments against staging environments, behind VPNs, or within restricted networks. Setup requirements are discussed during the discovery call.
Yes. We sign mutual NDAs before any platform access or information sharing. A standard NDA is provided at the point of engagement.
All platform data is handled under strict data handling protocols. We do not retain any proprietary platform data beyond the assessment window. Full data handling terms are provided at engagement.
Yes. For organisations with strict data residency or air-gap requirements, we offer on-premise assessment execution. This option is available for enterprise engagements.
The Strategic Roadmap deliverable includes prioritised recommendations with implementation guidance. We also offer optional implementation partnership engagements for teams requiring deeper support.
From the Glintbase Research Lab.
The conceptual foundation behind the Enterprise Assessment.
The Internet Was Built for Humans. AI Agents Need a Different Interface.
Why AI Doesn’t "Use" Software the Way Humans Do
Agent Readiness: The Missing Metric in Modern Software
The next generation of software will not only be judged by how well humans use it.
It will also be judged by how confidently AI systems can understand and operate it.
That future begins with understanding how your software appears to machines. That is the work Glintbase is here to help you begin.
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