Model Use and Training

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The commitment

Customer Content sent to Graphor is never used to train any AI model — neither Graphor’s own models nor the models of any subprocessor in the inference chain. This is a contractual commitment, not just a best-effort promise. It rests on three layers:

1. Synapse Terms of Service — “Synapse does not train AI models using User Content.”
2. Per-provider contractual no-training clauses — every AI subprocessor in the Graphor production chain (AWS Bedrock, OpenAI, Cerebras) has a published no-training policy that Graphor relies on by contract. Verbatim citations are in §4 below.
3. Operational controls — Graphor does not opt in to any provider’s training programs, does not maintain a fine-tuning pipeline on customer corpora, and routes traffic only through provider tiers with the no-training default.

This page is the canonical reference for that commitment. The Subprocessors page lists who sees customer data; this page documents what they are allowed to do with it.

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1. What gets to an AI provider

Two distinct moments send Customer Content (or content derived from it) to an external AI provider. Both are governed by the no-training commitment above.

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1.1 Indexing time (once per build)

When you ingest a new source (or rebuild an existing one), Graphor produces a knowledge representation for retrieval. This involves two AI providers: The vectors are stored in Graphor’s own managed graph store. After embedding, no further indexing-time traffic to either provider occurs for that build.

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1.2 Query time (per sources.ask or /data-extraction request)

When a user asks a question or runs a structured extraction, Graphor routes the request to one of two tiers based on the thinking_level parameter the caller provides. What is sent on each request:

• The user’s question text
• A bounded set of context retrieved from your indexed sources (relevant snippets, with provenance metadata)
• The system prompt and conversation history for that session
• For /data-extraction requests: the JSON Schema describing the expected output shape

What comes back: the model’s textual answer (or structured extraction matching the schema), plus token usage metadata for billing and observability. Customer Content does not leave the Graphor production environment except as required to fulfill one of these two flows. There is no batch upload to provider training endpoints, no nightly export, and no cross-customer aggregation.

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2. Customer-controllable knobs

The following parameters and project-level settings give the customer direct control over what touches an AI provider and how: The customer cannot opt in to any model-training program through Graphor’s API — there is no flag, parameter, or setting that would cause Customer Content to be used for training.

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3. What Graphor uses Customer Content for

For transparency, here is the complete enumeration of how Customer Content is used inside the Graphor production environment:

1. Storage — the raw source you upload is stored in Graphor’s project-scoped object storage in us-central1 for as long as the source exists in your project. Deleted on customer DSR action per Data Retention.
2. Partitioning — the source is parsed into structured elements (pages, sections, tables, images) by Graphor’s document parsing service, running on Graphor’s own infrastructure.
3. Chunking — partitioned text is split into retrieval-sized chunks by Graphor’s own code.
4. Enrichment — chunks are annotated with per-page and per-document context using the Cerebras-hosted enrichment model. The enriched text is what gets embedded.
5. Embedding — chunks are sent to OpenAI for embedding under the OpenAI Zero Data Retention agreement (no logging, no training).
6. Indexing — vectors and metadata are written to Graphor’s own managed graph store.
7. Retrieval — at query time, Graphor’s own code searches the index and selects relevant chunks.
8. Inference — selected chunks plus the user’s question are sent to either the Fast or Standard tier provider for an answer.
9. Operational improvement — anonymized, mask-applied operational telemetry (latencies, error rates, retrieval quality signals) may be used by Synapse to improve the Graphor product. This explicitly excludes the content of customer questions and answers.

Items 4, 5, and 8 are the only steps in which an external AI provider receives Customer Content. All three providers are governed by the no-training commitments cited verbatim below.

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4. Per-provider evidence (verbatim)

Each provider’s public position on training and retention is reproduced here exactly as published. Sources are linked so any reader can verify.

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4.1 Anthropic, PBC — model owner of Claude

Anthropic models reach the Graphor pipeline through AWS Bedrock; no Customer Content transits Anthropic’s own infrastructure. The model-owner commitment from Anthropic’s own commercial terms reinforces the AWS-side commitment in §4.2:

“Anthropic may not train models on Customer Content from Services.”

Source: Anthropic Commercial Terms of Service, Section B (Customer Content).

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4.2 Amazon Web Services — AWS Bedrock

Bedrock is the production lane for every Standard-tier and fast-tier-fallback inference call:

“No, AWS and the third-party model providers will not use any inputs to or outputs from Amazon Bedrock to train Amazon Nova, Amazon Titan, or any third-party models.”

“Users’ inputs and model outputs are not shared with any model providers.”

“Any customer content processed by Amazon Bedrock is encrypted and stored at rest in the AWS Region where you are using Amazon Bedrock.”

Source: AWS Bedrock FAQs, Security section.

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4.3 OpenAI, L.L.C. — embeddings and optional reranker

OpenAI provides the embedding model used during indexing and (when explicitly enabled) the reranker:

“As of March 1, 2023, data sent to the OpenAI API is not used to train or improve OpenAI models (unless you explicitly opt in to share data with us).”

Source: OpenAI API — Your Data, Data controls section. Additionally, Graphor is enrolled in OpenAI Zero Data Retention (ZDR), which eliminates the default 30-day abuse-monitoring log for API requests. ZDR is enrolled and maintained by Synapse; if ZDR is ever disabled (whether by Synapse choice or by OpenAI policy change), this page is updated and subscribers to subprocessors@graphorlm.com are notified.

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4.4 Cerebras Systems, Inc. — chunk enrichment and fast tier

Cerebras serves the open-source gpt-oss-120b model used for chunk enrichment and the fast tier:

“the foregoing does not grant Cerebras the right to use Service Content for the purpose of training or fine-tuning models.”

Source: Cerebras Terms of Use. Cerebras additionally commits to zero retention of inference data:

“We do not retain inputs and outputs associated with our training, inference and chatbot Services.”

Source: Cerebras Privacy Policy.

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5. Currently running

The tier-based declaration in §1.2 is the stable contract — provider and tier do not change without a published update to this page. The exact model SKUs that serve each tier do change as providers release new versions; the table below records the models in production as of the Last updated date in the front matter. Model upgrades within the same tier (for example, a future Claude Opus version) are deployed as part of normal release management; the table above is revised on each upgrade and the change history records the date.

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6. What Graphor explicitly does NOT do

Stating these explicitly removes ambiguity for security and privacy reviewers:

• No fine-tuning on customer corpora. Graphor does not maintain a pipeline that fine-tunes any model on customer-derived datasets.
• No opt-in to provider training programs. Synapse does not opt in to any provider’s training programs (for example, OpenAI’s training opt-in) for the production Graphor org.
• No cross-customer data aggregation for model improvement. Operational telemetry used to improve the Graphor product is aggregated and mask-applied; the content of customer questions and answers is not part of it.
• No human review of customer prompts for quality grading. Synapse personnel do not review customer Content for quality grading, dataset curation, or evaluation purposes.
• No silent provider substitution. Provider and tier changes are published in advance via the Subprocessors page and the subprocessors@graphorlm.com notification list.

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7. Change history

When the tier-to-provider mapping, the per-provider citation, or the currently-running model annex changes, this table is updated and subscribers to subprocessors@graphorlm.com receive an email.

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Contact

• General privacy and DPA inquiries: privacy@graphorlm.com
• Subprocessor and model-change notifications: subprocessors@graphorlm.com
• Customer support: support@graphorlm.com