Insights · Artificial Intelligence
Artificial Intelligence at Vestval.
The strategy, architecture and engineering of serious AI systems.
Category overview
Artificial intelligence is moving from research demos to operational infrastructure. This category covers how serious teams design, deploy and govern AI systems — model strategy, data architecture, evaluation, safety and the trade-offs that determine whether a deployment compounds or quietly stalls. We write for engineering leaders, founders and operators making real decisions, not for the demo circuit.
Pillar themes
What this category covers.
Model strategy
Choosing between proprietary, open-weight and fine-tuned models. Routing across providers. Cost and latency budgets. When to invest in your own weights.
Data and retrieval
Vector stores, retrieval architectures, evaluation harnesses, feedback loops and the data hygiene that decides whether RAG actually works.
Evaluation and safety
Real evaluation pipelines, hallucination control, red-teaming and the governance layer required for AI you can answer for in front of a regulator.
Deployment patterns
Inference infrastructure, private deployment, model gateways, observability and the operational discipline that separates a pilot from production.
Articles in this category
The complete guide to AI agents
What agents actually are, when they help, when they hurt, and the architecture for agents you can ship to production.
2026-03-25 · 13 min read
Model strategy for enterprise AI: proprietary, open-weight and fine-tunes
A practical framework for deciding when to use proprietary APIs, when to deploy open-weight models, and when to invest in your own fine-tunes.
2026-01-12 · 12 min read
FAQ
Artificial Intelligence — frequently asked
- Both, increasingly. The serious pattern is a model gateway with proprietary APIs for frontier capabilities, open-weight models for cost-sensitive and privacy-sensitive workloads, and fine-tunes only where you have a defensible data advantage.
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