LLM Development Services

We build custom LLMs adapted to your domain, from model selection and fine-tuning through evaluation and production deployment.

See Our AI Builds

Empowering teams at leading organizations worldwide

Case Study
Case Study
Case Study
Case Study
Case Study

Recognized Across the Industry

25+

AI systems built and deployed

10+

Industries served

10+ Yrs

Senior AI architects

100%

Milestone-based pricing

LLM Development Services Across the Full Model Lifecycle

We build across the complete large language model development lifecycle. Select a capability to see what we build and how we engineer it.

Model Selection & Strategy

We run foundation model selection against your use case, comparing open-source and proprietary models on accuracy, cost, and licensing so you commit to the right base model before any training spend begins.

Key Benefits & Outcomes

  • Foundation model selection and benchmarking
  • Open-source and proprietary model comparison
  • Customization method chosen per use case
  • Cost and licensing mapped before commitment

Technologies & Process

We benchmark candidate models against your task before selecting one, comparing accuracy, inference cost, context window, and licensing across open-source and proprietary options. Model selection is documented with the tradeoffs made and the customization path chosen, whether fine-tuning, continued pretraining, or a parameter-efficient method. Built on Llama, Mistral, OpenAI, Anthropic, or Google Gemini depending on the result. The recommendation is grounded in measured performance on your data, not on which model is most marketed.

Know the Model, the Data, and the Cost Before You Commit

We scope the customization method, the data you need, and the inference cost upfront, so you approve a defined build before any training spend.

Trusted by Clients Worldwide

Organizations that built AI copilots with AppVerticals and measured the result.

Sales teams lose deals when they lack the right information at the right moment. AppVerticals built a RAG assistant that surfaces it live, qualifies opportunities, and produces SOWs on demand. It does in seconds what took our AEs an hour a day.

Sales teams lose deals when they lack the right information at the right moment. AppVerticals built a RAG assistant that surfaces it live, qualifies opportunities, and produces SOWs on demand. It does in seconds what took our AEs an hour a day.

Kevin Kunz
Kevin Kunz Co-Founder, Hyvara
Kevin Kunz
Kevin Kunz Co-Founder, Hyvara

Powering Progress Across Your Industries

AI copilots built around the compliance, data, and workflow constraints of the industries we serve.

  • Healthcare LLMs for clinical documentation, patient summary generation, and medical knowledge retrieval, built with HIPAA-compliant data handling, human review on clinical outputs, and audit trails on every generated response.

  • Education LLMs for tutoring, content generation, and assessment, adapted to curriculum vocabulary and built with the fairness and data governance controls learning environments require before AI touches learner workflows.

  • Logistics LLMs for document processing, exception summarization, and operational query answering, trained on your domain terminology so responses reflect how your operations actually run.

  • Real estate LLMs for listing generation, contract summarization, and client communication drafting, grounded in your property data and adapted to the vocabulary of your market.

  • Finance LLMs for document analysis, compliance summarization, and research support, built with the audit trails, explainability, and access controls regulated financial environments require.

  • Retail LLMs for product content generation, catalog enrichment, and customer query handling, built to stay accurate across large product catalogs without degrading at scale.

  • SaaS LLMs embedded as product features: intelligent search, drafting, and summarization, built to the security and multi-tenancy standards enterprise customers evaluate before approving an AI feature.

  • Insurance LLMs for policy document analysis, claims summarization, and underwriting support, built with the explainability and audit controls the sector's regulatory obligations require.

  • Legal LLMs for contract analysis, regulatory research, and document drafting, built with access controls, source traceability, and human review checkpoints professional obligations require.

  • Manufacturing LLMs for maintenance documentation, quality reporting, and technical query answering, trained on operational data to surface intelligence from existing records.

See What Your Custom LLM Takes to Build

One scoping session maps your use case, your data, and the right method. You leave knowing what to build, the cost, and the timeline.

Enterprise-Grade AI Compliance

HIPAA

CCPA

ISO

GDPR

Socc

Explainable Ai

EU AI

NIST AI

PCI DSS

SamD

PHIPA

AI model governance lifecycle

Engineering Decisions For The Best LLM Quality

Five decisions made before training begins that separate a custom LLM that performs in production from one that costs too much and drifts too fast.

Customization Method Before Training

We choose between fine-tuning, continued pretraining, and parameter-efficient methods before any training run, because each solves a different problem at a different cost. Committing to full fine-tuning when LoRA would hold accuracy wastes compute, and the wrong choice is expensive to reverse once training has run.

Data Strategy Before Model Selection

We design the training data strategy before locking the base model, because dataset quality determines whether customization works at all. A strong dataset on a modest base model beats a weak dataset on a frontier one, and no model choice recovers a training set that was never prepared correctly.

Evaluation Benchmarks Set Before Build

We define accuracy, factual consistency, and safety benchmarks against your use case before training begins, then measure every checkpoint against them. A model that scores well on a public leaderboard and poorly on your task is a common and costly outcome, and use-case benchmarks set upfront are the only way to catch it early.

Inference Cost Modeled Before Deployment

We model inference cost at architecture stage, because a model that is accurate but too expensive to serve never reaches production. Quantization, caching, and model routing are planned before launch so the running cost you approve is the running cost you get.

Monitoring and Retraining From Day One

We deploy drift detection, accuracy monitoring, and retraining triggers alongside the model at launch. A custom LLM that runs without monitoring degrades silently as inputs change, and the first sign of trouble should be an alert, not a user complaint.

Key Benefits & Outcomes

  • Domain-adapted accuracy on your real workload
  • Lower inference cost through optimized serving
  • Reproducible, versioned training pipelines
  • Evaluation against use-case benchmarks, not leaderboards
  • Private deployment with full data control

Match Your LLM to the Right Customization Path

The right path depends on your data, accuracy target, and budget. Each maps to a different custom LLM problem.

  • Supervised fine-tuning adapts a pretrained model on your labeled examples so it learns your tasks, formats, and vocabulary. Best when you have quality task-specific data and need behavior the base model does not reliably produce.

  • Continued pretraining adapts a base model to your broader domain corpus before task-specific tuning, building foundational knowledge of your field. Best when your domain language differs substantially from general text and you have a large unlabeled corpus.

  • LoRA, QLoRA, and adapter methods adapt a limited set of parameters rather than the full model, cutting compute and storage cost sharply. Best when you iterate quickly across versions or fine-tune a large model on constrained hardware.

  • Distillation compresses a large, accurate model into a smaller, faster one that holds most of the performance at a fraction of the serving cost. Best when inference cost or latency is the binding constraint at your traffic volume.

  • We build the application layer around your model: retrieval, orchestration, and the interface your users work in, turning a customized model into a shipped product. Best when the model is one component of a larger system.

  • We deploy your model inside your own environment, on private cloud or on-premises, with encryption, access control, and audit logging throughout. Best when data residency or regulatory obligations rule out sending inference to a third-party API.

Powered by the World's Most Trusted AI Models

Foundation models, orchestration frameworks, and knowledge infrastructure selected for your use case and your users, not the most marketed option.

Build a Custom LLM That Performs on Your Domain

We adhere to the highest international standards to ensure your data is secure and our processes are flawless.

How We Build Custom LLMs

Each stage produces a defined output your team reviews and signs off before the next one begins, so you always know what was decided, what it costs, and what happens next.

Discovery & Use-Case Scoping

We map the tasks the model will handle, the data available to train it, the accuracy target, and the compliance obligations that apply. You leave with a defined model specification, a customization method, and a data strategy before any training work begins.

Model Selection & Data Strategy

We benchmark candidate base models against your task and design the training data pipeline. The customization method, evaluation benchmarks, and inference cost model are documented and signed off before the first training run is scheduled.

Training & Customization

We run training in documented, reproducible pipelines, validating against held-out data at every checkpoint. Datasets and model versions are tracked so every result can be traced, reproduced, and rolled back if a run underperforms the previous version.

Evaluation & Safety Testing

We evaluate the model against use-case benchmarks, adversarial inputs, and safety criteria before deployment, measuring accuracy, factual consistency, hallucination rate, and instruction-following. We do not ship until the model performs against the benchmarks set at scoping.

Deployment & Monitoring

We deploy through LLMOps pipelines with inference monitoring, drift detection, and cost tracking live from day one. Thirty days of post-launch optimization is included as standard, covering accuracy tuning, inference cost reduction, and retraining on fresh data.

LLM Development Insights

Perspectives on selecting base models, fine-tuning for your domain, and shipping custom LLMs applications.

Explore More
Custom CMS Development: How to Plan, Build, and Avoid Costly Mistakes

Custom CMS Development: How to Plan, Build, and Avoid Costly Mistakes

Custom CMS development means building a content management system around your business’s exact content, workflow, security, and publishing needs. That…

18 min read 2026-06-02
MVP Development Services for Startups: How to Vet, Question, and Choose The Right Partner 

MVP Development Services for Startups: How to Vet, Question, and Choose The Right Partner 

To choose the right MVP development service for your startup, look for a partner who starts with discovery before scoping,…

16 min read 2026-05-25
8 Top CMS Development Companies in US (2026 Updated)

8 Top CMS Development Companies in US (2026 Updated)

In 2026, top CMS development companies for US businesses include AppVerticals, ScienceSoft, Iflexion, Radixweb, BairesDev, Itransition, Fingent, and Zazz. These…

12 min read 2026-05-20

Frequently Asked Questions

A large language model is an AI system trained on large volumes of text to understand and generate language, using a transformer architecture to predict and produce text based on the input it receives. LLMs power tasks like summarization, question answering, and content generation. A custom LLM is a foundation model adapted through fine-tuning or continued pretraining to perform a specific domain or task accurately.

LLM development runs through defined stages: use-case scoping, foundation model selection, training data preparation, customization through fine-tuning or continued pretraining, evaluation against use-case benchmarks, inference optimization, and production deployment with monitoring. Each stage produces a reviewed output before the next begins. Skipping the data and evaluation stages is the most common cause of a model that performs in a demo but fails in production.

Custom LLM cost depends on the customization method, the size of the training dataset, the base model, and the inference volume after launch. Parameter-efficient methods like LoRA cost far less than full fine-tuning or continued pretraining, and inference cost scales with traffic and model size. We scope and price at the architecture stage, so you approve a defined number against a defined deliverable before any training spend begins.

An LLM assistant is an application built on a language model that helps users complete tasks like answering questions, drafting content, or retrieving information from your knowledge base. The benefit comes from adapting it to your domain: a general assistant answers general questions, while a domain-grounded one handles your vocabulary, your data, and your edge cases accurately, which is where the measurable time savings appear.

A focused custom LLM using parameter-efficient fine-tuning on prepared data typically takes six to ten weeks from scoping to production. A larger build involving continued pretraining, a custom dataset, and on-premises deployment typically takes three to five months, depending on data readiness, compliance scope, and evaluation requirements. We scope timelines at the architecture stage against your actual data and environment.

An MVP for an LLM-powered application typically takes four to eight weeks, depending on whether an existing foundation model with light customization meets the accuracy target or a fine-tuned model is required. The MVP covers the core use case, a working retrieval and orchestration layer, and evaluation against your benchmarks, so you validate performance on real queries before committing to a full build.

Custom LLM development covers the full lifecycle: use-case scoping, foundation model selection, training data engineering, customization through fine-tuning, continued pretraining, or parameter-efficient methods, evaluation against use-case benchmarks, inference optimization, and deployment with LLMOps monitoring. Every build includes reproducible training pipelines, versioned datasets, safety and hallucination testing, and drift detection from launch day.

Not always. If a general foundation model with prompt engineering and retrieval meets your accuracy target, that is the cheaper and faster path, and we will tell you so. A custom LLM earns its cost when your domain language, accuracy requirements, data privacy constraints, or inference cost at scale make an off-the-shelf API insufficient. We assess that at scoping before recommending a build, so you do not pay for customization you do not need.

Contact Us

Tell Us What You Are Building

Whether you are ready to scope a custom LLM development project or need to define the use case first, a US-based solution architect responds within 2 to 4 business hours. We sign an NDA before any technical discussion begins.