Consulting · AI Systems
AI you own, not AI you rent.
Models chosen by task, data kept in-house, systems built to be examined.
Most firms are adopting AI by default: a hosted model behind a chat box, company data flowing to a vendor, no record of what it learned. Wesvel advises institutions on doing it deliberately, from choosing between legacy hosted models and open-weight alternatives, to keeping proprietary data inside the perimeter, training models on the firm’s own knowledge, and building and governing the systems around them.
Principles
Four decisions, made on purpose.
- 01
Own the data
Proprietary data stays inside the firm’s perimeter. Models read it through retrieval the firm controls; nothing is given to a vendor to learn from.
- 02
Choose models by task
Hosted frontier models where the hardest reasoning justifies the cost and the data allows it. Open-weight models, tuned to the firm, for everything sensitive or high-volume.
- 03
Build the system, buy the commodity
Retrieval, evaluation, prompts, tuned models and integrations are the firm’s advantage and are built. Vector stores, serving runtimes and observability are bought.
- 04
Govern like any other model
Inventory, validation, monitoring, change control, human approval and records, extended from the model risk discipline financial firms already run.
Go deeper
Inside AI Systems
- 01Legacy LLMs vs Open-Weight ModelsWhat hosted frontier models and open-weight models each do well, what they cost in money and control, and how to use both without being captured by either.
- 02Own Your DataKeeping proprietary data inside the firm’s perimeter, understanding what vendors may learn from it, and building retrieval so models use your knowledge without absorbing it.
- 03Training Your Own ModelsWhen to fine-tune or distill an open-weight model on internal data, how to do it without leaking it, and how to prove the result is better.
- 04Building Internal SystemsThe architecture around the model: orchestration, tools, retrieval, evaluation, monitoring, security and the infrastructure to run it.
- 05Governance and ComplianceModel risk management, human oversight, record-keeping, vendor diligence and disclosure for AI in regulated financial businesses.
- 06AI in Financial OperationsThe use cases that hold up in investment, lending and advisory operations, and the ones that do not, drawn from how Wesvel uses these systems itself.
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Institutional inquiries.
For corporate and institutional entities only. Describe what the system would do, what data it would touch, and who has to sign off on it.
Institutional inquiries