Consulting · AI Systems
Building Internal Systems
A model is a component. The system is the product.
A language model on its own is a text box. What makes it useful in a business is everything around it: the retrieval that gives it the right context, the tools that let it act, the evaluation that catches its errors, the monitoring that shows what it did, and the infrastructure that runs it securely at the volume the firm needs. Wesvel helps firms build that system, and decide which parts to buy.
Architecture
An orchestration layer that breaks tasks into steps, calls models and tools, and enforces limits. Retrieval over the firm’s data with permissions intact. Tool interfaces that let the system read systems of record and, where allowed, act in them, with every action logged. An evaluation harness that runs on every change. Observability that records prompts, outputs, costs and latency for review.
Agents and autonomy
Systems that plan and act across many steps can do real work and real damage. Autonomy is granted by task and by risk: reading and drafting freely, proposing actions for human approval, and executing only within narrow, reversible limits. The same separation of authority that governs Wesvel’s trading systems applies.
Infrastructure
Running open-weight models in production means serving infrastructure, whether owned hardware, reserved capacity in a private cloud, or a managed inference provider operating within the firm’s tenancy. The choice turns on volume, latency, data terms and the engineering the firm can support. Hybrid designs keep sensitive inference inside and burst elsewhere for the rest.
Buy or build
Most firms should buy the commodity layers, vector stores, serving runtimes, observability, and build the parts that encode their own judgment: the retrieval over their data, the evaluation sets, the prompts and tuned models, the tool integrations into their own systems. Building everything is slow; buying everything hands the advantage to the vendor.
Practice
How Wesvel approaches it
- 01Systems are designed around the firm’s tasks, data and risk limits, with the model as a swappable component.
- 02Autonomy is granted by task and risk, with human approval and logging for anything that acts on a system of record.
- 03Evaluation and observability are built before launch, not added after the first incident.
- 04Commodity layers are bought; retrieval, evaluation, prompts, tuned models and integrations are built and owned.
More in AI Systems
- Legacy 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.
- Own 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.
- Training 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.
- Governance and ComplianceModel risk management, human oversight, record-keeping, vendor diligence and disclosure for AI in regulated financial businesses.
- AI 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.