scanned May 8, 2026

Interfaze

interfaze.ai

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Interfaze is an AI model built for deterministic developer tasks such as OCR, web scraping, and speech-to-text

43/100

Tier 3 · Agent-Accessible

Content answers38/100
Protocol plumbing56/1009 of 16 checks pass
Re-audit2961h ago· 1 credit

Scored by asking 15 questions a buyer of a ai-ml product asks, then grading this site’s own pages: answered, hedged (partial or vague), or silent (no page answers it). How scoring works

15 buyer questions · graded against the site’s own pages

1 of 15 come back answered. 8 get a hedge. 6 meet silence.

1 answerable8 half-answers6 silent

Goes silent · technical · high importance

Your web scraping endpoint times out on complex pages — what's the hard timeout ceiling and does it return partial data or fail completely? I need to know for retry logic design.

Goes silent · pricing · high importance

I'm comparing plans for a high-throughput OCR pipeline — what's the actual concurrent request limit on the Starter vs Pro tier? The pricing page mentions 'higher limits' but doesn't specify the numbers.

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The fix queue

57 points sit between interfaze.ai and 100: 14 open questions and 7 missing protocol checks, ordered by estimated payoff.

Point estimates are per fix under scoring v2. They are not additive to a promised total.

01technical · importance highGoes silent+7 content pts est.

Your web scraping endpoint times out on complex pages — what's the hard timeout ceiling and does it return partial data or fail completely? I need to know for retry logic design.

What the pages say

No page on the site addresses this.

The fix

Add a technical reference page documenting API limits including hard timeout ceilings, partial data behavior on timeout, and recommended retry strategies for the web scraping endpoint.

confidence high · grounding synthesized · weight 0.00 · Absent

02pricing · importance highGoes silent+7 content pts est.

I'm comparing plans for a high-throughput OCR pipeline — what's the actual concurrent request limit on the Starter vs Pro tier? The pricing page mentions 'higher limits' but doesn't specify the numbers.

What the pages say

No page on the site addresses this.

The fix

Add a dedicated pricing/limits comparison table showing Starter vs Pro concurrent request limits, or clarify in the pricing FAQ whether the 50 req/s rate limit varies by tier or if concurrency limits are separate from rate limits.

Where we looked: /docs/limits, interfaze.ai, /pricing

confidence high · grounding synthesized · weight 0.00 · Absent

03pricing · importance highGoes silent+7 content pts est.

If I hit my plan's included credits mid-month, are overages billed at the same per-token rate or is there a penalty multiplier? Need this for budget forecasting.

What the pages say

No page on the site addresses this.

The fix

Add a dedicated pricing/FAQ section explaining plan structure (if any), included credits per tier, and overage billing terms—specifically whether overages are billed at the same per-token rate or at a premium.

confidence high · grounding world-knowledge · weight 0.00 · Absent

04technical · importance mediumGoes silent+7 content pts est.

The GUI detection docs mention it works on screenshots but don't specify accuracy metrics — what's the mAP or precision@0.5 for common UI elements like buttons vs text fields?

What the pages say

No page on the site addresses this.

The fix

Add a benchmarks or evaluation section to the GUI detection documentation with mAP, precision@0.5, and per-class accuracy metrics for common UI elements (buttons, text fields, dropdowns, etc.) on standard datasets.

confidence high · grounding synthesized · weight 0.00 · Absent

05technical · importance lowGoes silent+7 content pts est.

Your guardrails feature flags NSFW content — what's the documented false positive rate on medical imaging or artistic content? I need to know if human review is mandatory.

What the pages say

No page on the site addresses this.

The fix

Add a section to the guardrails documentation covering accuracy benchmarks, false positive rates by content category (medical, artistic, etc.), and whether human review is required or recommended for flagged content.

confidence high · grounding world-knowledge · weight 0.00 · Absent

06technical · importance lowGoes silent+7 content pts est.

Your translation endpoint supports 100+ languages but doesn't mention formality levels — can I specify formal vs informal 'you' for languages like German or Japanese where this matters?

What the pages say

No page on the site addresses this.

The fix

Add documentation about formality level controls to the translation API reference, or explicitly state if this feature is not supported. Consider adding a parameter like 'formality' or 'tone' to the translation endpoint documentation.

Where we looked: /docs/translation, /docs/supported-languages

confidence high · grounding world-knowledge · weight 0.00 · Absent

Hedged · 8 of 15 questions

A buyer gets something, then has to guess the rest. Expand any row for the evidence and the fix.

07The streaming docs show SSE examples but don't clarify — when streaming structured outputs, do you send partial JSON chunks or buffer until valid? This affects my parser design.technicalHedges+7 pts est.

The streaming docs show SSE examples but don't clarify — when streaming structured outputs, do you send partial JSON chunks or buffer until valid? This affects my parser design.

What the pages say

No page on the site addresses this.

The fix

Add explicit documentation on the streaming page clarifying whether structured output streaming sends partial/incomplete JSON chunks that may fail parsing, or if chunks are buffered server-side until valid JSON is available. Include guidance on recommended client-side parsing strategies.

Where we looked: /docs/streaming

confidence high · grounding synthesized · weight 0.00 · Vague

08Processing large PDF batches for OCR — what's the maximum single file size and total payload size per request? The handling files section mentions 'large files' without hard caps.limitsHedges+2 pts est.

Processing large PDF batches for OCR — what's the maximum single file size and total payload size per request? The handling files section mentions 'large files' without hard caps.

What the pages say

For file uploads via the Interfaze API, there are specific file size limits: 20 MB for binary file objects and base64-encoded files, and 80 MB for URLs provided in prompts. However, the chunks do not specify whether these limits apply per individual file in a batch or as a total payload size for batch requests, nor do they clarify maximum limits specifically for OCR/PDF processing versus general file handling.

The fix

Add explicit documentation for OCR batch processing limits: clarify whether the 20 MB file size limit applies per PDF in a batch, define any total payload size limit for batch requests, and distinguish between general file handling limits and OCR-specific constraints.

Where we looked: /docs/handling-files, /docs/limits

confidence medium · grounding synthesized · weight 0.65 · Vague

09Speaker diarization is critical for my multilingual call center use case — which of your 100+ supported languages actually support speaker diarization vs just basic transcription?technicalHedges+2 pts est.

Speaker diarization is critical for my multilingual call center use case — which of your 100+ supported languages actually support speaker diarization vs just basic transcription?

What the pages say

Interfaze claims speaker diarization has "Over 100+ languages with multilingual support" and provides a general supported languages page listing 162 languages, but does not specify which individual languages support speaker diarization versus basic transcription only. The speaker diarization documentation repeatedly references a "View all supported languages" link, but the linked page appears to be a generic language list without diarization-specific annotations.

The fix

Create a dedicated speaker diarization language support matrix that explicitly lists which of the 100+ languages support diarization versus transcription-only, or annotate the existing supported languages page with feature badges (diarization ✓, transcription-only) per language.

Where we looked: /docs/audio/stt-speaker-diarization, /docs/audio/stt-speaker-diarization, /docs/supported-languages, /docs/audio/speech-to-text

confidence medium · grounding synthesized · weight 0.65 · Vague

10Your n8n integration page exists but doesn't list which Interfaze features are exposed as nodes — can I access structured outputs and function calling through n8n or just basic prompts?integrationHedges+2 pts est.

Your n8n integration page exists but doesn't list which Interfaze features are exposed as nodes — can I access structured outputs and function calling through n8n or just basic prompts?

What the pages say

The n8n integration page only documents basic setup using the OpenAI Chat Model node for 'generate content, analyze data, or make decisions' — it does not explicitly list which Interfaze features are exposed as nodes. The chunks do not confirm whether structured outputs or function calling are available through n8n specifically. However, Interfaze does support structured outputs and function calling through other SDKs (OpenAI SDK, LangChain SDK, Vercel AI SDK) as shown in other documentation pages. The n8n integration appears to be a generic OpenAI-compatible connection, suggesting advanced features may work if configured through the standard OpenAI Chat Model node's parameters, but this is not documented.

The fix

Expand the n8n integration documentation to explicitly list which Interfaze features are available as n8n nodes or parameters, with specific examples showing how to configure structured outputs (response_format) and function calling (tools) through the OpenAI Chat Model node interface.

Where we looked: /docs/n8n-integration, /docs/n8n-integration, /docs/structured-output, /docs/function-calling, /docs/langchain-sdk, /docs/openai-sdk

confidence medium · grounding synthesized · weight 0.65 · Vague

11Using precontext to ground responses in our documentation — is there a token budget or character limit for the context window, and does it get charged at input token rates?operationsHedges+2 pts est.

Using precontext to ground responses in our documentation — is there a token budget or character limit for the context window, and does it get charged at input token rates?

What the pages say

The site documents a general 'Max context window' limit of 1 million tokens (from the Limits page), but does not specify whether precontext specifically has its own token budget or character limit, nor does it explicitly state whether precontext is charged at input token rates. However, the FAQs indicate that 'Infrastructure usage is converted to tokens and added to both input and output tokens,' suggesting precontext task outputs are likely counted as tokens in the overall context window and charged accordingly.

The fix

Add explicit documentation on the Precontext page about whether precontext consumes tokens from the context window budget and how it is charged (input token rate or otherwise).

Where we looked: /docs/limits, /docs/faqs, /pricing, /docs/precontext

confidence medium · grounding synthesized · weight 0.65 · Vague

12Considering Pro for production workloads — what's the actual SLA response time for P1 incidents and is there a dedicated support channel or just email? The pricing page says 'priority support' without specifics.supportHedges+2 pts est.

Considering Pro for production workloads — what's the actual SLA response time for P1 incidents and is there a dedicated support channel or just email? The pricing page says 'priority support' without specifics.

What the pages say

The pricing page mentions 'Priority 24×7×365 and private support channel' for Pro tier, but does not specify actual SLA response times for P1 incidents. The Terms of Service explicitly states 'we do not guarantee any specific service levels' and that 'Any SLAs will be separately agreed upon in writing.' For support channels, Pro appears to get a private support channel (not just email), though the exact mechanism isn't detailed beyond the 'private support channel' mention.

The fix

Add specific SLA response time commitments (e.g., 'P1: 15 minutes initial response') to the pricing page or a dedicated support/SLA page. Clarify what 'private support channel' means (dedicated Slack, phone hotline, etc.).

Where we looked: /pricing, /legal/terms

confidence medium · grounding synthesized · weight 0.65 · Vague

13The Postgres LLM page mentions it's open source — what's the minimum Postgres version required and do you provide a Helm chart or just Docker compose for Kubernetes deployments?operationsPage missing+2 pts est.

The Postgres LLM page mentions it's open source — what's the minimum Postgres version required and do you provide a Helm chart or just Docker compose for Kubernetes deployments?

What the pages say

The chunks confirm Postgres LLM is open source and point to the GitHub repository (https://github.com/JigsawStack/postgres-llm) for full details. The installation requires the http, hstore, and pg_cron Postgres extensions. However, the chunks do not specify the minimum Postgres version required, nor do they mention whether a Helm chart or Docker Compose is provided for Kubernetes deployments. The GitHub repository is referenced as the source for 'full readme' and 'all the details,' suggesting deployment specifics may be found there rather than on the marketing site.

The fix

Add a dedicated deployment/operations page covering: minimum Postgres version, container orchestration options (Helm chart vs Docker Compose), and Kubernetes-specific setup instructions. Currently this information appears to live only in the external GitHub repository.

Where we looked: /docs/opensource/postgres-llm, /blog/introducing-postgres-llm-run-ai-natively-inside-postgres, /blog/run-llms-inside-postgres

confidence medium · grounding synthesized · weight 0.65 · Page missing

14We're evaluating the code sandboxing feature for user-submitted scripts — what container runtime do you use and is there network egress filtering? SOC 2 docs would help but I don't see them linked.securityHedges+1 pt est.

We're evaluating the code sandboxing feature for user-submitted scripts — what container runtime do you use and is there network egress filtering? SOC 2 docs would help but I don't see them linked.

What the pages say

The code sandboxing feature runs in "isolated secure environments" with "<100ms spin up times" and "massive parallelism with thousands of concurrent executions" for Python and TypeScript. However, the specific container runtime (e.g., gVisor, Firecracker, Docker) is not stated, and there is no mention of network egress filtering. SOC 2 documentation is not linked or mentioned in any of the retrieved chunks.

The fix

Add technical details to the code sandboxing documentation specifying the container runtime technology (e.g., gVisor, Firecracker, Kata Containers) and network security controls including egress filtering policies. Link to SOC 2 Type II report or trust center.

Where we looked: /docs/code-sandboxing

confidence high · grounding stated · weight 0.80 · Vague

What the pages already answer cleanly

1 of 15. Answered with enough specificity for a buyer to act on. This is the bar the queue above is chasing.

migrationAnswered

Migrating from OpenAI's GPT-4V — does your vision endpoint accept the same base64 image format and message structure, or do I need to rewrite my payload construction?

What the pages say

Interfaze's vision endpoint accepts the same base64 image format and message structure as OpenAI's GPT-4V. The API is OpenAI-compatible: you encode images as base64 data URLs using `data:image/jpeg;base64,{b64}` format and pass them in the `image_url` content type within the messages array, exactly matching OpenAI's structure. Example from the Nemotron 3 Nano Omni model shows: `{"type": "image_url", "image_url": {"url": image_url}}` where `image_url` is a base64 data URL. No payload rewrite is needed for migration.

Source: /models/unslothnvidia-nemotron-3-nano-omni-30b-a3b-reasoning-gguf, /models/nvidianemotron-3-nano-omni-30b-a3b-reasoning-nvfp4, /models/nvidianemotron-3-nano-omni-30b-a3b-reasoning-bf16, /docs/handling-files

confidence high · grounding synthesized · weight 0.95 · Answered

Protocol plumbing · 56/1009 of 16 checks pass · each fix +6 protocol pts est.

The other half of the score: 16 checks for the files and headers agents look for. The 7 below are installs, not judgment calls, and most are an afternoon. Expand any for the snippet and the standard it follows. They sit after the queue because none of them changes what your pages say.

Content signalAccess+6 pts est.
Install snippet
User-agent: *
Content-Signal: search=yes, ai-input=yes, ai-train=no
Allow: /

StandardCloudflare proposalVendor proposal

Clean crawlAccess+6 pts est.

StandardSitedex metricSitedex metric

Markdown negotiationRendering+6 pts est.

StandardRFC 9110 + 7763IETF RFC

MCP cardInteraction+6 pts est.

Sitedex generates this file from your crawl. Grab it in Files from this audit below.

StandardModel Context ProtocolCommunity spec

OpenAPI specInteraction+6 pts est.

StandardOpenAPI SpecIndustry standard

WebMCP widgetInteraction+6 pts est.

Sitedex generates this file from your crawl. Grab it in Files from this audit below.

StandardW3C WebMCP draftW3C / WHATWG

Sitemap lastmodDiscoverability+6 pts est.

Standardsitemaps.orgIndustry standard

Already passing 9 of 16: robots.txt, sitemap.xml, llms.txt, AI crawler access, Server-rendered content, Canonical URLs, Meta descriptions, HTML lang attribute, Organization schema.

Ask this site’s index

Sitedex already serves interfaze.ai as an MCP endpoint. Ask interfaze.ai anything an AI agent might ask, and see what its index returns. (To score your own site, use the form below.)

Snippets & configs

For developers and the engineer-on-call: copy these into your tools or your site.

Files from this audit

Built from this crawl. Download or copy each, then install it at the path noted.

llms.txt

Built from this crawl. Install at /llms.txt so agents start here.

organization.json

Organization JSON-LD, pre-filled from this crawl. Wrap in a ld+json script.

server-card.json

MCP server card built from this crawl. Host at /.well-known/mcp/server-card.json.

webmcp.json

WebMCP discovery manifest built from this crawl. Host at /.well-known/webmcp.json.

MCP endpoint

https://mcp.sitedex.dev/s/interfaze-ai/mcp

The URL anyone's agent points at. Read-only; safe to share.

Claude Code

claude mcp add interfaze --transport http https://mcp.sitedex.dev/s/interfaze-ai/mcp

One command, then the agent has it.

Cursor / Continue

{
  "mcpServers": {
    "interfaze": {
      "url": "https://mcp.sitedex.dev/s/interfaze-ai/mcp"
    }
  }
}

Drop into mcp.json.

WebMCP: two parts

WebMCP-capable browsers run the widget at runtime. Crawlers without JS rendering need the discovery manifest to find your tool surface. Install both.

1 · Widget script

<script async src="https://sitedex.dev/widget.js"></script>

Drop in <head>. WebMCP-capable browsers (Chrome 146+ Origin Trial) call navigator.modelContext.provideContext() via this script.

2 · Discovery manifest

{
  "$schema": "https://wellknownmcp.org/schemas/webmcp.json",
  "name": "interfaze.ai",
  "tools": [
    { "name": "search", "description": "Search interfaze.ai's indexed content." },
    { "name": "get_page", "description": "Fetch a page from interfaze.ai as markdown." }
  ]
}

Host alongside the script at /.well-known/webmcp.json. Crawlers that don't render JS rely on this.

Your turn

See which of these questions your site goes silent on.

Free, about 5 minutes. We crawl your site, test it against the buyer questions your category asks, and name what’s vague, contradictory, or missing, plus the files AI agents look for.

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