Capabilities · Enterprise AI Systems and Services
Custom LLM Solutions for Proprietary Business Knowledge
Custom LLM solutions from NEURAL-LINK use retrieval-augmented generation, fine-tuning, or self-hosted models depending on the accuracy, latency, and data-handling requirements of the use case. The approach is chosen per project, not per fashion.
Key facts
- Service type
- Custom LLM development
- Intended audience
- Product and platform teams building domain-specific AI features
- Delivery model
- Hosted API integration, self-hosted deployment, or hybrid
- Provider
- NEURAL-LINK Singapore
- Geographic focus
- Singapore and Southeast Asia
- Governance
- Trust and AI Governance
- Last reviewed
- 2026-07-10
What a custom LLM solution means
A custom LLM solution is a model configuration — retrieval, prompting, fine-tuning, and serving — designed for a specific use case rather than a general-purpose chat product.
RAG versus fine-tuning
Retrieval-augmented generation (RAG) is the right tool when the failure mode is missing knowledge. Fine-tuning is the right tool when the failure mode is style, format, or task adherence. The two are complementary, and NEURAL-LINK will recommend the combination based on observed failure modes on real tasks.
Hosted models versus self-hosted models
Hosted models are usually more cost-effective and faster to deploy. Self-hosted models are appropriate when hosted providers' data-handling terms are insufficient, or when latency and cost at scale justify the operational cost of self-hosting. NEURAL-LINK does not train foundation models from scratch.
Data preparation and access control
Retrieval sources are curated and access-controlled. Fine-tuning datasets are versioned and de-duplicated. NEURAL-LINK does not include client data in model provider training runs unless a client explicitly agrees.
- Vector stores with access-controlled retrieval
- Prompt and artefact versioning
- Evaluation-set version control
Evaluation and hallucination testing
Evaluation runs against a representative labelled task set. Hallucination testing includes retrieval-grounded factual checks and refusal behaviour tests.
Prompt-injection and retrieval risks
Prompt-injection defences include structured tool interfaces, output validation, and refusal patterns for out-of-policy requests. Retrieval risks include stale or contaminated sources; NEURAL-LINK includes retrieval-quality checks in the evaluation harness.
When an existing model is sufficient
If a general-purpose hosted model already meets the accuracy, latency, and data-handling requirements, NEURAL-LINK will recommend using it directly.
Known limitations
- Fine-tuning does not fix retrieval problems, and RAG does not fix style problems.
- Self-hosting shifts operational responsibility to the client.
- Evaluation is only as good as the evaluation set.
When this is not the right fit
- Use cases well served by a general-purpose hosted model without customization.
- Requests to train a foundation model from scratch (out of scope).
Frequently asked questions
When should a company use RAG?
When the model needs current or private knowledge that isn't in its training data, and hallucination is the primary risk.
When should a company fine-tune a model?
When style, format, or task adherence is the issue and cannot be solved by better prompts and retrieval.
Private AI versus public AI APIs?
Private or self-hosted is appropriate when data terms of hosted providers are unacceptable; otherwise hosted APIs are usually more cost-effective.
Related capabilities
Contact NEURAL-LINK
To discuss a custom llm development engagement, contact NEURAL-LINK or email generalaffairs@neurallink.sg.
Provider: NEURAL-LINK Singapore · Author: NEURAL-LINK Research and Engineering · Last reviewed: 2026-07-10