LLM Development Services Built on Your Data and Systems
FactoryJet builds large language model applications for US businesses: we test models on your own cases, ground them in your documents with RAG, fine-tune only when the scores call for it, and connect the result to your ERP, CRM and helpdesk. You see working software on your data before you sign, and you own the code.
Need AI that takes actions on its own? See AI agent development.
What LLM Development Services Include, and When You Need Them
LLM development services turn a large language model into software a business can rely on: a model chosen on your test cases, grounded in your data, connected to your systems and measured before launch. Almost no business needs to train its own model. The work is choosing one, feeding it the right information and proving it gets your cases right.
US searches for "llm development services" rose about 700% in a year, to some 260 a month at $30 a click. That is our own DataForSEO measurement for the US on 17 October 2026. On 18 October every business site on Google's first page for it was a dedicated LLM page, which is why this one exists apart from our broader AI development page.
Your data does not have to train anyone's model. OpenAI says data sent to its API has not been used for training since 1 March 2023 unless you opt in, and Anthropic says it does not use commercial and API inputs or outputs for training by default. OpenAI ↗ Anthropic ↗
Fine-tuning is cheaper than it used to be, but it is still the exception. The LoRA method tunes a small set of added weights, and its authors report it cut trainable parameters by 10,000 times against full fine-tuning of GPT-3 175B. Most business jobs still reach their pass mark with retrieval and careful prompting. Hu et al., 2021 ↗
LLM Development vs AI Development vs AI Agents vs AI Integration
| Service | What you get | Best for | Where it lives |
|---|---|---|---|
| LLM development This page | The model work: model choice, RAG, fine-tuning, evaluation, LLM apps and APIs | A reading or writing job where accuracy on your data decides success | Your repository and cloud account |
| AI development Own page ↗ | Custom AI software built into existing systems, of any kind | A known business job that needs AI plus ordinary software around it | Inside your systems |
| AI agent development Own page ↗ | AI that takes multi-step actions within rules and approvals | Jobs where a draft is not enough and the AI must act | Across several systems |
| AI integration services Own page ↗ | An AI tool you already chose, connected properly to one app | Adding AI features to a CRM, store or helpdesk you keep | Inside one existing app |
Most agent projects sit on top of LLM development work: the same model choice, retrieval and evaluation, with actions added once accuracy is proven.
Large Language Model (LLM) Application
An LLM application is software with a language model at its core and four layers around it: retrieval that brings in your documents (the RAG pattern Patrick Lewis and colleagues published in 2020), optional fine-tuning on your examples, application code that adds tools and permissions, and a test gate that checks answers before people rely on them.
The model is the part you rent. The layers are the part that makes it right for your business, and they are what LLM development services build.
Custom LLM Development Services We Deliver
Model Selection & Benchmarking
Two or three candidate models from OpenAI, Anthropic, Google and open-weight families, scored on your own test cases for accuracy, speed and data terms before anything is built. You see the scores, not a vendor preference.
RAG Over Your Documents
Retrieval-augmented generation over manuals, policies, contracts, catalogs and tickets, with permissions applied per user and a source linked on every answer. Answers stay current without retraining the model.
LLM Fine-Tuning
Fine-tuning, often with parameter-efficient methods such as LoRA, when prompting and retrieval cannot hold the format, tone or classification you need. Always compared against the base model on cases it never saw.
LLM Application Development
Internal tools and customer-facing apps with a language model at the core: a policy assistant, a contract reviewer, a quote drafter. Built in your repository, on your cloud account, with an interface your team will use.
LLM Integration & APIs
The model wired into NetSuite, Salesforce, HubSpot, Zendesk, Shopify or your ERP through official APIs, or through a custom MCP server so assistants such as Claude and ChatGPT can reach your data under limits.
Open the page ↗Evaluation & Private Deployment
A test set of real cases with a pass mark you approve before build, re-scored after every model update. Deployment on business API terms or inside your own cloud account when your data rules require it.
Open the page ↗API Model, Open-Weight Model, RAG or Fine-Tuning: What Your Job Needs
| Approach | Use it when | Trade-off | Market range to build |
|---|---|---|---|
| API model + careful prompts start here | The facts arrive in the request itself: sorting, summarizing, drafting | Fastest to ship; breaks down when the model needs facts it was never given | Part of a $5,000 to $25,000 MVP workflow (Layer3 Labs) |
| RAG over your data most business projects | Answers must come from documents or records that change often | Retrieval quality decides accuracy; the index and permissions need upkeep | Usually inside the same MVP range; more with many sources |
| Fine-tuned model only with evidence | You need a fixed format, tone or label and have 1,000+ good examples | Does not reliably teach new facts; retrain when examples change | $10,000 to $50,000 (ProductCrafters) |
| Open-weight model in your cloud data rules | Prompts and documents must stay inside infrastructure you control | More hosting and patching work; often a step behind the top API models | Depends on hosting scope; quoted after scoping |
| Training your own model rarely | You hold unique data at scale and a capability no rented model offers | Large data and compute needs; usually trails models you can rent | $50,000 to $200,000+ (ProductCrafters) |
Ranges are other firms' published build figures, read 18 October 2026: Layer3 Labs (updated 9 September 2026) and ProductCrafters (24 February 2026). They are not FactoryJet prices; we quote a fixed price after a short scoping call.
What Every LLM Build Includes, Item by Item
Four groups, 24 items. An LLM development company that cannot hand you a list this specific is selling a demo.
Model and data
The model is chosen on evidence, and the data is made fit to read.
6 items- Test set first: 50 to 200 real cases with the answer a skilled employee would give
- Two or three models scored on that set before one is chosen
- Data map of every source the job needs, its owner and who may see it
- Document cleanup: duplicates, outdated versions and scanned PDFs handled
- Chunking per document type, so a contract clause stays with its heading
- Fine-tuning only with evidence that prompting and retrieval fall short
Application and integration
The model has to live where your team already works.
6 items- Official APIs for your ERP, CRM, helpdesk and store, never screen scraping
- Drafts by default: model output lands as a draft a person approves
- Structured outputs validated in code before anything is written to a system
- Model layer kept swappable, so a better model is a configuration change
- Error handling that alerts a person instead of failing quietly
- Handover docs written for the developer who comes after us
Accuracy and testing
If nobody measured it before launch, nobody knows if it works.
6 items- Pass mark agreed with you before the production build starts
- Refusal tests: the system must say it does not know when sources are silent
- Source shown on every answer so a person can check it in seconds
- Adversarial cases, such as instructions hidden inside an emailed PDF
- Re-scoring after every model or data change, on the same test set
- Plain report of where it gets things wrong, not only where it is right
Security and ownership
Mapped to the OWASP Top 10 for LLM applications, then checked by a person.
6 items- Least-privilege access for every system the model can read or write
- Business API terms that keep your inputs out of model training
- Per-user permissions carried through retrieval
- Spend limits per day so a loop cannot run up a surprise bill
- Audit log of prompts, sources and outputs, kept in your account
- You own everything: code, prompts, evaluation sets and any tuned weights
Next step
Have a job a language model should do on your data?
Tell us the job and the systems involved. On a short call with the founder we will say whether a ready-made tool will do, then show working software on your own data before you sign anything, with a fixed price in writing.
LLM Applications That Earn Their Keep
Good first jobs share three traits: people repeat them daily, the data already exists, and a person can check the answer in seconds.
Order and RFQ intake
The model reads emailed purchase orders and RFQs, matches lines to your catalog, and code creates draft orders in NetSuite, Odoo or SAP Business One for a person to release.
USE-02Answers from your policies
RAG over return terms, warranties and product specs inside Zendesk or HubSpot, with a suggested reply and the source linked.
Contract and document review
The model pulls fields and flags clauses that differ from your standard, and a person reviews the flagged ones instead of reading every page.
Invoice and form extraction
Fields extracted into Xero, QuickBooks or your ERP as draft entries, with the matching rules your team already uses and nothing posted without approval.
USE-05When it must act
OWN PAGEOnce the model has to act and not only draft, the build becomes an agent, with approvals, limits and rollback added on top of the same LLM work.
The LLM Development Process, From First Call to Live Use
Scope
One job, one measurable goal, and an honest answer on whether a ready-made tool would do.
Test set
We collect real past cases with known answers and agree the pass mark with you.
Prototype
Working software on your data, with two or three models scored side by side. Usually 3 to 5 weeks in.
Build & pilot
Integrations, permissions, logging and an interface, then a pilot with real users. One job on one or two systems takes 5 to 8 weeks.
Launch & re-test
Rollout with training, and the same test set re-run after every model or data change.
How We Keep an LLM Application Safe to Run
Each commitment is tied to a public source your security team can check.
Built against the known attacks
Prompt injection is first on OWASP's 2025 list, followed by sensitive information disclosure, and excessive agency is sixth. So we limit what a fooled model can do: least-privilege access, actions in code with checks, and a person on anything consequential. OWASP ↗
Every answer can be traced
Prompts, retrieved sources and outputs are logged in your own account, so a wrong answer can be traced to the document or instruction behind it and fixed at the source.
People approve consequential steps
Model output lands as a draft. Anything that moves money, changes a customer record or carries legal weight waits for a named person, and that rule is enforced in code, not in the prompt.
We are engineers, not your lawyers
We map personal data, minimise it and document where it flows so your counsel can sign off. Legal duties stay with your business; we build to your advisers' requirements.
When You Do Not Need an LLM Development Company
A custom LLM build is the right answer less often than vendors admit.
The job is general writing
Drafting, summarizing and meeting notes are covered by business AI tools that are live in days. Spend on training your team.
Your software already has it
Salesforce, HubSpot, Zendesk and Shopify all ship AI features. If one fits your process, set it up well and build only for the gap.
Nobody can judge the output
If no one can say what a correct answer looks like, no one can write the test set either, and the model cannot be measured.
You do not know the first job yet
A short readiness and use-case review ranks your options by value and effort. Build once you know which job pays back first.
What Else Ranks for "LLM Development Services", and How This Page Differs
These are business sites on Google's first page in the US for "llm development services" and "llm development company" when we checked on 18 October 2026 (DataForSEO), with the title Google showed for each. We list them so you can compare; we have not reviewed their services, and the list leaves out forums, video sites and ranking roundups.
| Site | Page title shown in Google |
|---|---|
| signitysolutions.com | Large Language Model Development Services |
| solulab.com | Large Language Model (LLM) Development Company |
| leewayhertz.com | Custom domain-specific LLM development company |
| octalsoftware.com | LLM Development Company |
| tech.us | LLM Development Services | Turn AI Into Business Value |
| indatalabs.com | Large Language Model (LLM) Development Services |
Most of these pages describe what an LLM development company offers. This page adds the market build ranges with their sources, the test-set-first process, and when a ready-made tool is the better buy.
LLM Development Questions, Answered Directly
24 questions US buyers ask Google and AI assistants about LLM development, most taken from Google's People Also Ask boxes on 18 October 2026.
Q01What are LLM development services?
LLM development services are the work of turning a large language model, the kind of AI behind ChatGPT and Claude, into software a business can rely on. The usual list: choosing and testing a model, grounding it in company data with retrieval (RAG), fine-tuning where needed, building the app or API around it, connecting it to existing systems, and testing accuracy before launch.
Q02What is LLM development?
LLM development means building software around a large language model so it does one job well: answering from your documents, drafting replies, extracting fields from invoices or classifying tickets. For most businesses it does not mean training a new model from scratch. It means picking an existing model, giving it the right data and limits, and measuring its answers against real cases.
Q03What does an LLM development company do?
An LLM development company scopes one measurable job, finds and cleans the data the model needs, compares models on your own examples, builds the retrieval, prompts and code around the chosen model, connects it to your systems through their APIs, and sets a pass mark before launch. After launch it re-tests the system whenever the model, the data or the job changes.
Q04What are LLM developers?
LLM developers are software engineers who build applications on large language models. Their daily work is retrieval pipelines, prompt and tool design, evaluation sets, model comparisons, API integrations, guardrails and logging. Most write Python or TypeScript, and the job is ordinary LLM software development with a test set attached. Training a frontier model is a separate research job done by a handful of labs; LLM application development is the part businesses hire for.
Q05What is LLM application development?
LLM application development, or LLM app development, is building a product or internal tool whose core is a language model: a support assistant over your policies, a contract reviewer, a quote drafter, an internal search over manuals. The model is one component. The rest is the interface, the data connections, permissions, evaluation and the handoff to a person when the model is unsure.
Q06What are the uses of an LLM in a business?
The uses that pay back first are the repeated reading and writing jobs: answering staff or customer questions from your own documents, drafting replies and product content, extracting fields from emails, PDFs and invoices, summarizing long tickets or calls, and classifying incoming requests. Each one works best when a person can check the result in seconds and the source is linked.
Q07Is ChatGPT an LLM or generative AI?
Both. ChatGPT is a generative AI product, and the engine inside it is a large language model from OpenAI. "Generative AI" is the broad category of AI that creates text, images or code. An LLM is the type of generative model that works with language. LLM development services build business software on models like these, usually through the provider's API rather than the chat app.
Q08Should we fine-tune an LLM or use RAG?
Start with RAG when the answers must come from your documents or records, because they change and RAG keeps answers current without retraining. Fine-tune when you need a fixed format, tone or classification that prompting cannot hold and you have hundreds to thousands of good examples. Fine-tuning does not reliably teach new facts, so many production systems use both together.
Q09What are LLM fine-tuning services?
LLM fine-tuning services train an existing model further on your own examples so it follows your format, vocabulary or decisions more consistently. The work is mostly data: collecting and cleaning examples, splitting off a test set, training, then comparing the tuned model against the base model on cases it never saw. Techniques such as LoRA tune a small set of added weights instead of the whole model. Hu et al., the LoRA paper ↗
Q10Can I build my own large language model?
You can, but almost no business should. Training a model from scratch takes huge amounts of data and computing time, and the result usually trails the models you can already rent. ProductCrafters puts training custom models at $50,000 to $200,000 or more. The practical route is an existing model plus your data through RAG, and fine-tuning only where tests show it is needed. ProductCrafters cost breakdown ↗
Q11What is the best LLM for business use?
There is no single best LLM. The right one depends on the job, the accuracy you need, how fast it must answer, the data terms and the price per call. We score two or three candidates from OpenAI, Anthropic, Google and open-weight families on your own test cases, then choose. Keeping the model layer swappable means a better option later is a configuration change.
Q12Should we use an open-weight model or an API model?
An API model from OpenAI, Anthropic or Google is faster to start and usually more capable. An open-weight model, such as a Llama, Mistral or Qwen family model, runs on infrastructure you control, which helps when data must stay inside your cloud or network. Open weights mean more hosting and upkeep work. Decide on data rules and test scores, not on habit.
Q13How do you stop an LLM from making things up?
You reduce it by design and measure what remains. Ground answers in retrieved sources and show the source, tell the model to say it does not know when the sources are silent, check outputs against rules in code, and keep a person on anything with money or legal impact. Then score the system on a test set of real questions, including ones it should refuse.
Q14What are the four types of LLM?
Lists vary, but a common split is by how the model is offered and trained: general-purpose base models, instruction-tuned chat models, domain or task-tuned models, and smaller models built to run on modest hardware. For a business decision the useful split is simpler: hosted API models versus open-weight models you run yourself, and general models versus ones tuned on your examples.
Q15Do LLM apps use LangChain?
Some do. LangChain and LlamaIndex are popular open-source frameworks for LLM application development, and the model providers now ship their own SDKs for tools and agents. We pick per project: a framework helps with common retrieval patterns, while plain code is easier to test and debug for a narrow job. What matters more is the evaluation set and the logging around it.
Q16How much do LLM development services cost?
Published market ranges: Layer3 Labs puts a production MVP workflow at $5,000 to $25,000 and an enterprise AI system at $50,000 to $200,000 or more (updated 9 September 2026). ProductCrafters puts fine-tuning an OpenAI or Anthropic model at $10,000 to $50,000, needing 1,000 or more examples. These are other firms' figures, not FactoryJet prices. We quote a fixed price after scoping. Layer3 Labs pricing guide ↗
Q17How long does an LLM development project take?
A working prototype on your real data, with two or three models scored on your test set, usually takes three to five weeks. A production build with integrations, permissions, logging and a pilot with real users adds five to eight weeks for one job on one or two systems, and eight to fourteen weeks across several systems. Data cleanup is the usual cause of delay.
Q18Who owns the model, prompts and data after the build?
At FactoryJet you own the code from day one, together with the prompts, evaluation sets, any fine-tuned weights and the documentation, in your repository. The AI and hosting accounts are in your name and billed by the providers at cost. Check this in any contract: some vendors keep the code or the tuned model and license it back, which makes switching expensive later.
Q19Will our data be used to train the model provider's models?
Not on business API terms. OpenAI says data sent to its API has not been used to train its models since 1 March 2023 unless you opt in, and Anthropic says it does not use inputs or outputs from its commercial products, including the Anthropic API, for training by default. We build on those business terms and never on personal chat accounts. OpenAI: your data ↗
Q20What are the main security risks in an LLM application?
The OWASP Top 10 for LLM applications (2025) lists them, starting with prompt injection, where text hidden in an email or document tries to redirect the model, followed by sensitive information disclosure. Others include data and model poisoning, improper output handling and excessive agency. The defenses are least-privilege access, actions run in code with checks, and a person approving consequential steps. OWASP Top 10 for LLM applications ↗
Q21Can an LLM run inside our own cloud?
Yes. Open-weight models can be deployed in your own AWS, Azure or Google Cloud account, and the major API models are also sold through those clouds under your account. That keeps prompts, documents and logs inside infrastructure you control. It adds hosting and patching work, so we recommend it when your data rules require it rather than by default.
Q22What is LLM integration?
LLM integration connects a language model to the software you already run so it can read the right information and put its output where people work. Examples: an LLM reading emailed purchase orders and creating draft orders in NetSuite, or drafting a Zendesk reply from your policy pages. LLM integration services do this through official APIs; LLM API integration means calling the model through the provider's API from your own code with your keys. AI integration services ↗
Q23How do I choose an LLM development company?
Ask four things. Will they show working software on your own data before you sign? Will they agree a test set and pass mark before the build? Who owns the code, prompts and any tuned model? Whose accounts do the AI and hosting run in? Then ask for the systems they have connected an LLM to. Vague answers on any of these are a warning.
Q24Is LLM development the same as AI agent development?
No. LLM development builds software where the model reads and writes: answers, drafts, extractions and classifications that a person or code then uses. AI agent development goes further, letting the model take several steps across systems within rules and approvals. Most agents are built on the same LLM work, so a project often starts as LLM development and adds an agent later. AI agent development ↗
Tell Us the Job You Want a Language Model to Do
Send the task, the systems involved and what a good answer looks like. We will show working software on your own data before you sign, then put a fixed price per phase in writing.