Generative features users actually trust.
For product teams shipping image, audio, video, or text generation. We build the generation pipeline, the content moderation layer, the provenance stack, and the user experience — as one engineered system, not bolted-together demos.
We build products where AI generates something a customer uses — text, images, summaries, drafts. The engineering that matters is the guardrails, the cost per use, and what happens when it produces something wrong.
Updated August 2026
The demo is the easy part.
Three things decide whether a generative product survives real users.
Generative AI is easy to demo and hard to ship responsibly.
Every generative feature looks great in the demo. The hard part is what happens when a user generates something harmful, when a generated image gets scraped and misattributed, or when your model produces output indistinguishable from real content at scale. We build the boring infrastructure that makes generative features trustworthy.
Content safety isn't a checkbox — it's a system. We build classifiers tuned to your content policy, human review queues for borderline outputs, rate limiting and abuse detection, and a clear appeals process. We design these before the model API call, not as an afterthought.
We also build provenance: C2PA watermarking, cryptographic signing, and audit trails that prove what your system generated, when, and from what prompt. As synthetic content regulations tighten globally, this infrastructure is what separates responsible products from liabilities.
What we build.
Image generation pipelines
Stable Diffusion, DALL-E, Midjourney API, and Flux — fine-tuned to your brand aesthetic, with style controls, prompt enhancement, and negative prompt systems.
Video & audio generation
Short-form video generation, text-to-speech, voice cloning, and music generation — with latency optimization for real-time and near-real-time user experiences.
Content moderation layers
Multi-layer classifiers (NSFW, violence, copyright, PII, brand safety) running pre- and post-generation. Human review queues for borderline outputs with configurable thresholds.
C2PA & watermarking
Coalition for Content Provenance and Authenticity (C2PA) signing, invisible watermarking (tree-rings, StegaStamp), and cryptographic audit trails for every generated asset.
User-facing generation UX
Prompt input interfaces, style selectors, generation history, regeneration flows, and export pipelines — designed for the actual user, not the demo audience.
Rights & licensing management
Training data provenance checks, output licensing management, and DMCA workflow automation — for products shipping in regulated creative industries.
The three things that sink generative products.
The demo is the easy part. These are what decide whether it survives contact with real users.
| Problem | What it looks like | How we handle it |
|---|---|---|
| Cost per use | Fine in testing, brutal at volume | Cheaper models where quality allows, caching, hard caps |
| Wrong output | Confident, plausible, incorrect | Grounding, validation, and a way to report it |
| Latency | Users leave before it finishes | Streaming, so they see it forming |
| Abuse | Someone uses it for something else | Rate limits and input filtering from day one |
| Model changes | Provider updates, output shifts | Pinned versions and an evaluation set |
What we build in.
A hard cap, because usage-based pricing on someone else's API can surprise you badly.
Watching text appear feels fast. Waiting for a finished block does not.
So when the provider updates the model, you find out from a test rather than a customer.
Where we've built this.
Marketing & advertising
Ad creative generation, copy variations, personalized visual content at scale, and brand-consistent asset libraries.
Healthcare communications
Patient-facing educational material generation with medical accuracy validation, illustration generation, and plain-language summaries.
How we build it.
Content policy design
We write the content policy before touching APIs: what can be generated, what is prohibited, and how edge cases are handled. This becomes the spec for every downstream system.
Generation pipeline
We select and integrate the right generation models for your use case — balancing quality, latency, cost, and fine-tuning flexibility. We build the prompt pipeline, enhancement layer, and parameter controls.
Safety infrastructure
We build the moderation stack: pre-generation prompt classifiers, post-generation output classifiers, rate limiting, abuse detection, and human review queues.
Provenance & watermarking
We implement C2PA signing and watermarking so every generated asset carries cryptographic evidence of its origin. We build the audit trail and the verification tooling.
UX & integration
We build the user-facing product surface, integrate with your existing asset management and publishing workflows, and monitor quality and abuse patterns post-launch.
Tools we use.
Engagement models.
Feature Build
One generation capability (e.g., image generation for product pages) with full safety and provenance stack.
- Single modality (image, audio, or text)
- Content moderation layer
- Basic C2PA signing
- 60 days post-launch support
Product Suite
Multi-modal generative product with full safety infrastructure, provenance, and user-facing UX.
- Multi-modal generation
- Full safety + moderation system
- C2PA + watermarking
- Rights management workflow
Platform
Generative AI platform for teams shipping at scale with compliance, rights, and abuse requirements.
- Unlimited modalities
- Custom safety classifiers
- Regulatory compliance (EU AI Act)
- Ongoing retainer
Frequently asked.
5 questions answered. Still have one? Reach out.
Multi-layer approach: prompt-level classifiers that block policy-violating inputs before generation, post-generation output classifiers that check every output, rate limiting to detect abuse patterns, and human review queues for borderline cases. We tune these to your content policy — not a generic one.