How to Choose an LLM Development Company: A Buyer's Evaluation Guide

An LLM development company is a firm that designs, builds and operates applications powered by large language models, from retrieval-augmented assistants to agents that take actions in business systems. Choosing one is mostly a risk decision: the model is rarely the hard part, and vendors differ far more on evaluation, data handling, security and what happens after launch.

This guide is a buyer's checklist. It explains what an LLM development company actually does, what to look for, how to verify security claims, how to use the NIST AI Risk Management Framework as a shared vocabulary, and how to run a proof of concept before you sign. If you already know your scope and want to see what Sphere delivers, our LLM development services page covers the offer. The checklist below applies to any vendor, including us.

What does an LLM development company actually do?

The work usually spans seven areas. Use case selection decides which problems are worth solving with an LLM at all. Data and retrieval work connects the model to your documents and systems, most often through retrieval-augmented generation (RAG). Model selection and prompting choose and configure a hosted or self-hosted model. Fine-tuning is added only when testing shows that prompting and retrieval are not enough. Evaluation builds the test sets and scoring that show whether answers are correct. Guardrails and security limit what the system can see, say and do. Integration and operations connect the application to business systems and keep it running, monitored and controlled.

Two of these areas separate strong vendors from weak ones. Retrieval quality decides most of what users experience; our custom RAG development services and our article on private LLM and RAG show how that layer is built. Deployment decides where your data goes; if you need a model inside your own environment, read about why enterprise deployments use vLLM.

What should you look for in an LLM development company?

Start with the criteria below. Each one pairs what good looks like with a sign that you should keep looking.

How do you verify an LLM vendor's security claims?

Ask the vendor to walk through the OWASP Top 10 for LLM Applications (opens in new tab). The 2025 edition lists prompt injection, sensitive information disclosure, supply chain, data and model poisoning, improper output handling, excessive agency, system prompt leakage, vector and embedding weaknesses, misinformation and unbounded consumption. A credible vendor can tell you which of these apply to your use case, what control addresses each one and how the control is tested.

Three of the ten deserve questions in almost every project. On prompt injection: how does the system treat instructions hidden in documents, emails or web pages it reads? On sensitive information disclosure: how are permissions enforced at retrieval time, so a user never receives content they could not open directly? On excessive agency: if the system can take actions, which ones need human approval, and how is each action logged? Our articles on prompt injection defense, LLM guardrails for the enterprise and the six attack classes every enterprise LLM faces go deeper on each.

How does the NIST AI RMF help you evaluate a vendor?

The NIST AI Risk Management Framework (opens in new tab) is a voluntary framework for managing the risks AI poses to individuals, organizations and society. Its elements, Govern, Map, Measure and Manage, give you a neutral vocabulary for vendor questions that does not depend on any vendor's terminology. NIST released AI RMF 1.0 on January 26, 2023, and published a Generative AI Profile (NIST-AI-600-1) on July 26, 2024 to help organizations identify risks specific to generative AI.

In practice, turn the four words into questions. Govern: who at the vendor and at your company owns the risk of this system? Map: how will the vendor document the use case, the data it touches and the people it affects? Measure: which metrics, such as accuracy, sourcing and refusal behavior, will be tracked, and how often? Manage: what happens when a metric slips, and who decides to pause or roll back the system?

What questions should you ask before you sign?

Beyond the framework, eight direct questions surface the differences between vendors quickly.

1. Can you show an evaluation on documents like ours before we commit? A vendor that cannot test on your content is asking you to buy on trust.

2. Where do our data and prompts go, and who can see them? Ask for the data flow in writing, including logging and retention.

3. Which models can you run, and how hard is it to switch? Model quality and availability change often, so portability protects you.

4. How are source permissions enforced? The right answer applies each user's access rights at retrieval time, not after the model has already read the content.

5. What does a wrong answer look like, and how do you catch it? Look for citations, refusal behavior when sources are missing and a review loop.

6. Who is on the team, and who stays after launch? Ask for names and roles, not a staffing model.

7. How do you control usage in production? Unbounded consumption is a listed OWASP risk, and a good vendor tracks usage by team and use case.

8. What do we own at the end? Confirm ownership of prompts, evaluation sets, integrations and the application code.

How should you run a proof of concept with an LLM development company?

A proof of concept is the cheapest way to replace opinion with evidence. Keep it short and structured, and follow five rules.

Define one use case and one success measure before work starts, for example the share of real employee questions answered correctly with a source.

Build the test set from your own questions and documents, and keep it hidden from the vendor until the scoring run, so the result reflects real performance.

Compare against a baseline, such as your current search or a plain prompt to a general model, so you can see what the build adds.

Review security with the same rigor as a production system: permissions, logging and the failure cases from the OWASP list.

Decide in advance what result means go, adjust or stop. A proof of concept with no decision rule becomes a project by default.

If the proof of concept will touch business systems, our guide to generative AI integration explains how to connect models to them safely.

Should you build in-house, hire an LLM development company or do both?

Most organizations do both over time. An outside team is the faster way to a first production system and to a reliable evaluation method. An in-house team is the right owner once the system is stable. The best engagements are designed for handover from the start: documented architecture, an evaluation set your team can run and training for the people who will operate it. If you are also weighing general AI software firms, our guide to choosing an AI software development company covers the wider decision.

What are the red flags when choosing an LLM development company?

Walk away, or at least slow down, when you see any of these: a demo with no evaluation behind it, reluctance to say where data goes, one model presented as the answer to every problem, no named senior engineers, security described only as a certificate, and a plan that ends at launch.

On commercial terms, the scope and investment for an LLM project depend on the use case, the data and the deployment model, so they are defined with each client during an assessment. Sphere's LLM development services page is the place to start that conversation.