Generative Systems
Generation as an engineered product capability.
Text, image and multimodal generation built into products — with the control, consistency and safety production demands.
Architectural layers
Problem
What problem does this solve?
- Generated output is inconsistent, off-brand or unsafe when used without control structures.
- Teams struggle to evaluate generation quality beyond vibes.
- Generation latency and cost can wreck the product experience if unengineered.
Scope
What CAS builds
- Content generation pipelines with structured outputs, style control and verification.
- Image and multimodal generation systems with brand constraints and provenance tracking.
- Diffusion and transformer-based generation adapted to domain requirements.
- Human review loops, moderation layers and usage analytics.
Architectures
Architectures that may be used
Each links into ModLens, the CAS architecture explorer, where the structure and trade-offs are diagrammed.
Decoder-only generation
open in ModLens →
Multimodal architectures
open in ModLens →
Transformer foundations
open in ModLens →
Method
How CAS approaches engineering
- Constrain the generation space: schemas, style references, verification steps.
- Build evaluation before scaling: rubrics, pairwise comparison, automated checks.
- Treat provenance as a feature — what was generated, from what, by which model.
- Design for the failure case: detection, fallback, human escalation.
Outcomes
What can result
- Generation quality that is measured, not assumed.
- Brand-safe output with review and moderation built in.
- Generation costs tuned to the product's real usage pattern.
Delivery
What the process looks like
01 · Generation audit
What to generate, quality criteria, risk profile, volume.
02 · Pipeline design
Model selection, control structures, verification gates.
03 · Evaluation
Quality rubrics and automated scoring before scale-up.
04 · Integration
Product integration with review loops and moderation.
05 · Operate
Cost, latency and quality telemetry; model refresh process.
Preparation
What a client should prepare
- Examples of acceptable and unacceptable outputs.
- The workflow the generated content enters.
- Policy requirements: disclosure, moderation, provenance.
Outcomes depend on data, constraints and integration reality. CAS states assumptions explicitly and reports negative results when evidence demands them.
Discuss generative ai with an engineer.
Bring the problem; we will bring the architecture, the evaluation plan and the honest feasibility read.