AI-native products and platforms
Syngen builds products and platforms from the data foundation up: the data layer, the AI layer, and the application on top. People and AI work as one team. Use the platforms and frameworks we've already built, or build your own product on the same foundation.
Build the foundation once
Most teams rebuild the same foundations for every project: event instrumentation, content pipelines, AI guardrails. We build each one once, with a standard data model and a written spec, and the next product starts on top of it.
What the platform covers
Product direction and engineering sit in one team, from first prototype to production. Each area below carries numbers from systems we've run.
Prototype in the first sprint
A working prototype ships in the same sprint the direction is set. The same product approach led Adobe's data and cloud transformation through a 3.5× revenue inflection, and founded four products before that, two of them acquired.
People and AI engineering
Location-agnostic engineering that pairs senior judgment with AI execution. The same model ran a lean, cross-geo team serving a 30K-person product ecosystem.
Data platforms
Pipelines, lakes, and the analytics and recommendation layers on top, built for freshness and scale. A standard data model and taxonomy let 1,500+ apps and services build on one foundation, so a new use case doesn't need a new architecture.
Deterministic AI, disclosed by design
AI that gives the same output twice, with provenance and disclosure built into the generation step. This is the governance model behind AI-content disclosure at consumer scale.
Self-serve instrumentation
A common instrumentation standard and self-serve dashboards mean engineers don't instrument the same event twice or dig through logs to find what broke.
Cloud cost as an architecture input
Right-sizing, consolidation, and vendor renegotiation are part of how each platform runs, year-round.
The AI framework behind the platforms
Teams usually tune AI with adjectives and trial and error. AI-IDS treats instructions as a software contract with defined roles, hard guardrails, and a documented order for resolving conflicts. It is published, model-agnostic, and used in every AI product we ship.
| What we measured | Before | After AI-IDS |
|---|---|---|
| Inconsistent AI output | baseline | −97% |
| Regeneration rate | baseline | −50% |
| Manual intervention required | baseline | −75% |
| Operational error rate, 25 rounds | 2.2% | 0% |
Built and proven inside a production content platform generating 400K+ multimedia experiences across 80+ publishers. It is being formalized as a research submission for a repeatable deterministic AI framework in enterprise production.
Platforms we've shipped
A sample of what's live in production.
Platforms we've built
AI-native content platform
Multi-model, multimodal pipelines that turn stories into 400K+ multimedia experiences, with voice synthesis, contextual media, and programmatic scoring. It runs across 80+ publishers and 15 channels, reaches 1B+ viewers, and is governed by our deterministic AI framework.
Enterprise data foundation
Consolidated 24+ systems and 330+ pipelines into one 90PB+ data lake processing 140B+ daily events, powering personalization, recommendations, and experimentation across 1.5K+ applications at 99.9999% availability.
Product analytics & growth platform
A standard data model adopted by 700+ applications, turning raw usage into product-led growth, recommendations, and operational analytics, with a measured, multiple-times return on the business impact it drove.
How we build and govern
Engineers and agents, one delivery bar
Geo-distributed teams across the US, Europe, and Asia work alongside AI agents under one written engineering standard, whether the hands are human or AI.
Human + AI engineering standard
An engineering operating system that encodes years of architecture experience into 30+ AI skills. We use it to build our own products.
Compliant by design, at scale
GDPR, CCPA, and COPPA translated into system design, including data minimization, policy-driven access, and end-to-end lineage, across 50+ markets without slowing teams down.
Teams that build on it
Marketing, product, and governance teams work from the same data model and the same AI spec.
One brief, every channel
Distribution-ready output across channels from one brief and one brand profile, governed to prevent fabricated content and cross-channel repetition.
Usage data feeds the roadmap
A standard data model that let product-led growth, recommendations, and experimentation ship without every team rebuilding the same instrumentation.
Compliance in the architecture
Privacy, provenance, and audit trails designed in from the start, ahead of legal requests. GDPR, CCPA, and COPPA are handled in the architecture across 50+ markets.
The product categories we build in:
How a release gets made
The same four steps for a new platform and for a single feature.
Frame
Start from a user problem and a measurable target. Output is a working prototype.
Design
Architecture and AI specs written as reviewable artifacts before a line of code exists.
Build
One accountable team, location-agnostic, working alongside AI, releasing weekly.
Measure
Every AI feature ships with a measured baseline. Every platform ships with visibility into what changed.
Industries and company stages
Company stages supported