Generative AI Company in San Francisco

It comes down to which of these you need: a focused generative AI proof of concept at SF rates, a production single-modality system for a mid-market SF buyer (creative pipeline with multiple LoRAs, a video generation workflow combining Runway and traditional compositing, or a music generation surface with rights handling), or multi-modal programs with text plus image plus video plus audio, multi-model routing, AB 2013 disclosure files, Copyright Office posture analysis, and SOC 2 control mapping. Scope drivers include adapter training, eval harnesses, brand-safety classifiers, C2PA provenance, and integration with one or two source systems. SF rates run above other US markets because of the OpenAI, Anthropic, Midjourney, and Cursor talent premium, but we deliver from Edmonton and Chandigarh on PST hours so headline rates compress meaningfully. Fixed-fee proposals in USD, with one-time build cost separated from ongoing inference, rights-clearance, and storage fees.

California AB 2013, signed in September 2024 with the operative compliance date in January 2026, requires developers of generative AI systems made available to Californians to publish a high-level summary of the datasets used to train or substantially modify the model. The summary covers sources of the data, whether it includes personal information or copyrighted material, the time period covered, and any synthetic data used. For SF generative deployments we run an AB 2013 review on any fine-tuning corpus, LoRA or DreamBooth training set, or RAG-adjacent embedding pipeline that could be read as substantial modification. We draft the public-facing summary your counsel will sign off, structure dataset documentation so the disclosure is defensible, and record a written rights and consent posture on every training asset. We also flag adjacent obligations, including the California AI Transparency Act watermarking requirements and the Colorado AI Act for cross-state deployments.

Voice cloning and avatar generation in SF can sit inside SAG-AFTRA's 2023 Television and Theatrical and Interactive Media Agreements, which require informed consent, separate negotiation, and compensation for the use of a digital replica of a covered performer. Our workflows for ElevenLabs, HeyGen, Synthesia, and self-hosted XTTS pipelines treat consent as a first-class artifact. Each voice or avatar talent signs a written replica agreement scoped to specific uses, durations, geographies, and prohibited contexts. The signed consent is bound to the model fingerprint, stored in an audit registry with the training audio or video manifest, and queried by the inference pipeline so generation refuses if consent has expired or been revoked. For non-union talent we still apply the SAG-AFTRA-aligned template because enterprise buyers and platform trust and safety teams now expect that posture. Deepfakes of identifiable people without explicit signed consent are refused by policy regardless of contract value.

Yes. We have shipped brand-safe image generation pipelines for Goodby Silverstein, AKQA, and Duncan Channon-tier SF agency engagements plus in-house creative teams at SaaS platforms in the Figma and Canva orbit, using Stable Diffusion 3.5, Flux.1, and SDXL through ComfyUI graphs with LoRA fine-tunes on the client's licensed campaign library. Brand-specific negative prompts, NSFW classifiers, trademark-violation detection, output moderation queues, and rights-clearance metadata are captured at generation time. Midjourney and DALL-E 3 are used as creative director sketchpads, not production endpoints, because the rights posture is weaker. Every approved output carries C2PA Content Credentials so downstream platforms (Meta, Google, TikTok, broadcast partners) can verify origin, and every campaign retains a generation log the client can produce if a training-data infringement claim arrives. Outputs depicting identifiable people require signed model releases, and style emulation of named living artists is refused by policy.

The California AI Transparency Act, signed September 2024, requires covered providers of generative AI systems with one million monthly users to offer free AI detection tools and to apply both visible and latent disclosures to AI-generated content. For SF buyers approaching that threshold we wire C2PA Content Credentials into the generation pipeline so every customer-facing output carries cryptographically-signed provenance metadata indicating the model, prompt template hash, and generation timestamp. For latent disclosure we use SynthID where the underlying model supports it (Google Imagen, Veo, Lyria) and an internal watermarking scheme on self-hosted Stable Diffusion and Flux deployments. We also build the detection-tool surface required by the Act when the client crosses the user threshold. Provenance metadata is stored in S3 us-west-2 with object lock, so the audit trail is tamper-evident and queryable by trust and safety and counsel.

Yes. For image generation we train LoRA and DreamBooth adapters on the client's licensed asset library on self-hosted GPUs in AWS us-west-2 or Modal, with a documented training data manifest, an infringement test set against known protected works, and a versioned adapter registry tied to the rights posture. For video we fine-tune motion LoRAs and ControlNet adapters where the use case warrants, and combine them with Runway Gen-3 Alpha customization. For voice we train per-talent XTTS or ElevenLabs Professional Voice Clones bound to a signed SAG-AFTRA-aligned consent agreement. For code generation we use private repository indexing on Cursor Enterprise or Copilot Enterprise rather than weight-level fine-tuning, which keeps the exposure surface smaller. Every fine-tune ships with a model card describing intended use, residual risks, training data provenance, an AB 2013 disclosure summary, and the conditions under which the adapter must be retired or retrained.

The US Copyright Office issued guidance in March 2023 and detailed reports through 2024 and 2025 confirming that copyright protection requires meaningful human authorship and that purely machine-generated content is not eligible. Works combining AI-generated material with human authorship can be registered, but the human contribution must be meaningful and the AI contribution must be disclaimed in the application. For SF buyers building generative pipelines that will produce works the client wants to register or assert rights over, we structure the pipeline to capture the human contribution at each stage: prompt authoring, selection, in-painting edits, compositing, color grading, and final approval. Each generation log records human inputs alongside model outputs so counsel can demonstrate the threshold of human authorship at registration. For work that does not require registration the posture is still useful when a downstream rights dispute arrives.

A SF production generative build typically takes ten to twenty weeks from kickoff to launch, depending on modality and regulatory tier. Weeks one to three are discovery, modality and model selection, AB 2013 review, Copyright Office human-authorship posture, and SAG-AFTRA review where voice or avatar are in scope. Weeks four to eight are pipeline design, LoRA or DreamBooth adapter training, eval set construction, and the first runnable end-to-end pipeline including brand-safety classifiers and C2PA provenance. Weeks nine to fourteen are integration with the client's DAM, brand portal, or product surface, plus red-team testing for harmful content, prompt injection, and protected-work infringement. Weeks fifteen to eighteen are shadow mode, trust and safety review, and observability wiring through Langfuse or Helicone. Week nineteen onward is gradual rollout behind feature flags. Multi-modal programs combining image plus video plus audio at platform scale can extend to twenty-six weeks.