Last updated: 24 May 2026
LLM providers and implementation partners support chatbots, internal copilots, document search, workflow agents, and model integration. Buyers should evaluate security, evaluation, retrieval quality, and operating costs.
OpenAI operates as an AI research and deployment company, specializing in large language models (LLMs). The organization develops various advanced AI models, including ChatGPT, GPT-4, and DALL-E, in addition to the Sora video generation model. A core aspect...
Mistral AI is a French artificial intelligence company that develops open-weight and commercial large language models. The company provides frontier generative AI models and an API platform for developers and enterprises to build AI applications. Mistral AI...
Squirro offers an enterprise generative AI platform focused on delivering secure and accurate intelligence for regulated industries. The platform provides capabilities such as document intelligence, knowledge graphs, and dynamic taxonomy and ontology manage...
Only with controls. Confirm exactly what data is sent to which model provider, whether it is used for training, where it is processed, and how long prompts and logs are retained. For personal or regulated data, prefer providers offering no-training guarantees, regional processing and data-processing agreements, and minimise or redact sensitive inputs.
It is a trade-off. Hosted APIs are fastest and most capable but send data to the provider; self-hosting open models keeps data in your environment at the cost of infrastructure and MLOps effort. Many Singapore firms use hosted APIs for low-sensitivity tasks and private or self-hosted models for regulated data. Decide per use case, by data sensitivity.
Costs scale with tokens and model choice, so right-size the model per task, cache, and limit context. For accuracy, use retrieval-augmented generation grounded in your own data, and evaluate outputs against a test set rather than trusting demos. Ask vendors how they measure quality, prevent hallucination, and report token spend.
Retrieval-augmented generation grounds an LLM in your own documents so answers draw on your data instead of the model general training. It improves accuracy, reduces hallucination, and keeps proprietary knowledge under your control. It is the common pattern for internal assistants and customer support where correctness and data governance matter.
Ask for an evaluation methodology, not a demo — a test set, scoring approach, and how they handle wrong answers and edge cases. Confirm how they ground responses, log and review failures, and keep your data private. A serious vendor can show measured accuracy on representative tasks and a plan for monitoring it in production.