LLM Development Services
We build production applications powered by large language models.
Not model training. Not research. The engineering that turns a powerful language model into a reliable application: retrieval, grounding, integration, evaluation, and the production rigor that makes an LLM feature actually work at scale.
Let's be clear about what we do
"LLM development" means two very different things, and the difference matters before you choose a partner.
One meaning is building and training language models
Pre-training models from scratch, full fine-tuning, assembling massive training datasets, running model evaluation pipelines. This is the work of AI research labs and specialist ML teams. It requires data scientists, significant compute, and a fundamentally different kind of organization.
The other meaning is building applications powered by language models
Connecting them to your data, engineering reliable outputs, integrating them into products, and deploying production systems that use LLMs to do real work. This is product engineering.
We do the second thing. We build LLM-powered applications, and we’re good at it because we’re product engineers who’ve shipped production software for over a decade.
If your project genuinely requires training a custom model from scratch, we’ll tell you that honestly and point you toward the right kind of team. For the vast majority of business use cases, that’s not what you need. What you need is a well-engineered application built on an existing, capable model. That’s exactly what we build.
The fine-tuning question, answered honestly
Most companies that think they need fine-tuning don't.
Many companies come to us assuming they need a fine-tuned or custom-trained LLM. Most of them don’t. Here’s the honest breakdown.
For most use cases, you don’t need fine-tuning. The frontier models (GPT, Claude, Gemini) are capable enough that, combined with good prompt engineering and retrieval grounding in your data, they handle the large majority of business use cases reliably. Reaching for fine-tuning first is a common and expensive mistake.
RAG usually solves what people think fine-tuning will solve. When companies want the model to “know their data,” they often assume that means training. It doesn’t. Retrieval-Augmented Generation connects the model to your data at query time, so it answers from your actual content. This is faster, cheaper, easier to update, and usually more effective than fine-tuning for knowledge-based use cases.
Fine-tuning makes sense in specific situations. When you need a very consistent output format across thousands of edge cases, when you have a large set of high-quality examples of the exact behavior you want, or when prompt engineering has genuinely hit a ceiling that careful engineering can’t overcome. In these cases, parameter-efficient fine-tuning (LoRA, QLoRA) of an open-source model can be the right call.
What we do: We start with the approach that fits your actual need. For most projects, that’s strong prompt engineering plus RAG on a frontier model. Where fine-tuning of an open-source model genuinely outperforms that, we’ll do it or coordinate it. What we won’t do is sell you an expensive custom model project when a well-engineered application on an existing model would serve you better. We’ll tell you which situation you’re in before you spend anything.
What we build
Six kinds of production LLM applications.
RAG Systems (Retrieval-Augmented Generation)
The foundation of most production LLM applications. We build the retrieval pipeline that connects a language model to your data: document processing, chunking, embedding, vector storage, and retrieval logic that surfaces the right content for each query. This is what makes an LLM answer from your actual knowledge rather than from general training. We build RAG systems that are accurate, fast, and maintainable, with the retrieval quality that determines whether the whole application works.
LLM-Powered Application Features
Language model capabilities engineered into your product: intelligent search, summarization, content generation, classification, extraction, question answering, and analysis. Built with the production rigor that distinguishes a reliable feature from a demo: output validation, error handling, cost management, and consistent performance at scale.
Intelligent Assistants and Copilots
LLM-powered assistants embedded in your product or operations. Context-aware, connected to your data, and designed to handle the full range of real user inputs. This overlaps with our chatbot and agent work; the focus here is on the LLM engineering underneath: the prompting, the retrieval, the context management, and the output reliability.
Document Intelligence Systems
LLM applications that process documents at scale: extraction of structured data from unstructured documents, summarization, classification, comparison, and analysis. Useful in document-heavy industries where manual processing is a bottleneck. We build these with the accuracy validation and human review workflows that the stakes require.
LLM Integration and Orchestration
The engineering layer that connects language models to your systems. API integration, prompt management, context handling, multi-step LLM workflows, and the orchestration logic for applications that chain multiple LLM calls or coordinate between models. Built cleanly so your team can maintain and extend it.
Open-Source LLM Deployment
For use cases where data privacy, cost at scale, or control requirements call for it, we deploy open-source models (Llama, Mistral, and others) in your own environment. This includes the deployment architecture, the inference setup, and the application layer on top. Where a fine-tuned open-source model genuinely fits the use case better than a frontier API, we build that.
How we build LLM applications
Seven steps, anchored by the right approach and rigorous evaluation.
Step 1: Define the use case and the right approach
What should the application do? What does reliable output look like? And critically: what’s the right technical approach? We determine whether your use case needs prompt engineering, RAG, fine-tuning, or a combination. Getting this decision right at the start saves significant cost and complexity. Most projects need less than people assume.
Step 2: Design the architecture
The model choice, the retrieval architecture if RAG is needed, the prompting strategy, the context management approach, the integration points, and the evaluation method. We design how the whole application fits together before building any of it.
Step 3: Build the core LLM pipeline
We engineer the prompting, the retrieval, the output handling, and the orchestration. This is where a capable model becomes a reliable application. The quality of this engineering is the difference between an LLM feature that works consistently and one that produces good results in testing and inconsistent results in production.
Step 4: Integrate into your product or workflow
We connect the LLM application to your systems, your data, and your user experience. Built to live where the work actually happens, not as a separate tool requiring people to switch context.
Step 5: Evaluate rigorously
LLM applications need systematic evaluation, not spot-checking. We build evaluation methods appropriate to your use case: test sets of representative inputs, output quality measurement, edge case testing, and adversarial input testing. We calibrate until the output is reliable enough for production.
Step 6: Optimize for cost and performance
LLM applications have real cost and latency considerations. We optimize: caching where appropriate, model selection per task (not every step needs the most expensive model), context window management, and token usage controls. We design the cost profile to scale sensibly with usage.
Step 7: Deploy and monitor
We deploy with output quality monitoring, cost tracking, latency monitoring, and usage analytics. LLM application quality can drift as inputs and content change. Monitoring catches it. We stay involved post-launch to tune based on real production data.
RAG vs. fine-tuning: how we decide
The most consequential decision, made honestly.
This is the most consequential technical decision in many LLM projects, and getting it wrong is expensive. Our decision framework:
Use RAG when…
The application needs to answer from your data, your knowledge changes or grows over time, you need to be able to update what the model knows without retraining, or you need the model to cite or ground its answers in specific sources. This covers most business use cases.
Consider fine-tuning when…
You need a highly consistent output format or style that prompting can’t reliably achieve, you have hundreds or thousands of high-quality examples of the exact behavior you want, the use case is narrow and well-defined, or inference cost at scale makes a smaller fine-tuned model more economical than a frontier model.
Often the answer is both or neither. Sometimes strong prompt engineering alone solves the problem. Sometimes RAG plus light prompting is enough. Sometimes a fine-tuned model handles the format while RAG handles the knowledge. We assess your specific situation rather than defaulting to the most complex or most impressive-sounding approach.
The honest truth: most companies that think they need fine-tuning need good RAG and good prompt engineering instead. We’ll tell you which camp you’re in before you commit budget.
Where LLM applications deliver value
The use cases with the clearest payoff.
Knowledge access and search.
LLM applications that let people find and synthesize information from large bodies of content. Instead of keyword search that returns documents, users ask questions and get answers grounded in the source material.
Document processing at scale.
Extracting, summarizing, classifying, and analyzing documents that previously required manual review. The clearest ROI is in workflows with high document volume and repetitive structure.
Content and communication generation.
LLM applications that draft, summarize, and generate written content grounded in your context, with human review where approval matters.
Customer and user support.
LLM-powered support that resolves queries accurately by answering from your actual product knowledge, with escalation to humans designed in.
Data interaction.
LLM applications that let non-technical people query and understand complex data through natural language, expanding who can get value from your data.
Industries where we build LLM applications
One concrete LLM application per domain.
Real estate and proptech.
Document intelligence for transaction and due diligence workflows, natural language property search, investor report generation, and knowledge assistants for property data. Document-heavy use cases where LLM processing saves significant manual effort.
Healthcare.
Clinical document summarization, structured extraction from records, knowledge assistants for protocols and policies, and patient communication support. Built with data privacy as a foundational requirement and human oversight for clinically relevant output.
Education platforms.
Learning assistants grounded in course content, automated content generation and feedback, and knowledge tools for students and educators. For early-stage edtech and institutional platforms.
Enterprise SaaS.
In-product LLM features, internal knowledge assistants, document automation, and natural language data interfaces. LLM capabilities that make products more useful and operations more efficient.
Marketplace platforms.
Content generation and improvement, intelligent search and matching, automated categorization, and support automation. LLM applications that improve the platform experience at scale.
Technology we build with
The application stack, not a research lab.
Language models
OpenAI (GPT-4 and newer), Anthropic Claude, Google Gemini for frontier capability. Open-source models (Llama, Mistral, and others) for use cases requiring data privacy, cost control, or deployment in your own environment. We choose based on the use case, not on defaults.
Retrieval and vector infrastructure
Vector databases (Pinecone, pgvector, Weaviate, Chroma), embedding models, document processing pipelines, and hybrid search approaches combining semantic and keyword retrieval. The retrieval layer is where RAG applications succeed or fail, and we engineer it carefully.
Orchestration frameworks
LangChain and LlamaIndex for retrieval and LLM workflows. LangGraph for applications with complex, stateful, multi-step LLM logic. We size the framework to the application’s complexity.
Evaluation and observability
LLM evaluation frameworks for systematic quality measurement, output validation logic, and observability tooling (LangSmith, Langfuse) for monitoring quality, cost, and performance in production.
Fine-tuning (where it fits)
Parameter-efficient fine-tuning (LoRA, QLoRA) of open-source models for the specific use cases where it genuinely outperforms RAG and prompting. We approach this as one tool among several, used when the use case calls for it, not as a default.
Deployment and infrastructure
Azure OpenAI, AWS Bedrock, Google Vertex AI for managed access. Self-hosted open-source model deployment for use cases requiring it. Deployment aligned with your cloud environment and security requirements.
What we don't do
Being clear about this beats faking it.
We don’t pre-train language models from scratch. That requires resources and expertise specific to AI research labs, and almost no business use case requires it.
We don’t do large-scale model training or operate as an ML research team. We build applications on existing models. When fine-tuning is the right call, we do parameter-efficient fine-tuning of open-source models for specific, well-defined use cases, not large-scale custom model development.
We don’t run ongoing MLOps for custom model lifecycle management. We build and deploy LLM applications and monitor them in production. If your use case requires a dedicated ML operations function for custom model retraining, that’s a different kind of engagement and we’ll tell you.
Being clear about this is more useful to you than claiming capabilities we’d have to fake. What we do, building reliable production applications on top of capable language models, is exactly what most companies actually need.
"We needed AI search built into our platform without rebuilding the whole product. GTC designed the integration cleanly, it shipped on time, and it actually improved how users found things. That was the measure that mattered."
"They were honest from the start about what AI would and wouldn't solve for our specific product. That scoped the project correctly from day one. The integration worked in production on the first try."
FAQ
Questions teams ask before engaging.
No. We build applications powered by existing large language models. Pre-training a model from scratch is the work of AI research labs and requires resources and expertise that almost no business use case justifies. What we do is the product engineering that turns a capable existing model (GPT, Claude, Gemini, or an open-source model) into a reliable production application: retrieval, grounding, integration, evaluation, and deployment. For the vast majority of companies, this is exactly what “LLM development” should mean for them.
Probably not, though we’ll assess your specific situation. Most companies that assume they need fine-tuning actually need good RAG (connecting the model to their data at query time) and careful prompt engineering. These approaches are faster, cheaper, easier to update, and usually more effective for knowledge-based use cases. Fine-tuning makes sense in specific situations: highly consistent output formats, large sets of high-quality training examples, or narrow use cases where a smaller fine-tuned model beats a frontier model on cost. We’ll tell you honestly which approach fits before you spend anything.
RAG (Retrieval-Augmented Generation) is the technique of retrieving relevant information from your data and providing it to the language model before it generates a response. It matters because it’s how an LLM application answers from your specific knowledge rather than from general training data. We use it heavily because it solves the problem most companies actually have (“we need the AI to know our stuff”) more effectively than fine-tuning, while being faster to build, cheaper to run, and easy to update when your content changes.
We work with the frontier models, OpenAI’s GPT-4 and newer, Anthropic Claude, and Google Gemini, and with open-source models like Llama and Mistral for use cases that require data privacy, cost control, or deployment in your own environment. We don’t have a preferred model. Different use cases genuinely perform differently across models, and the right cost and privacy trade-offs vary. We choose based on your specific requirements and often test more than one before committing.
Yes, with open-source models. Models like Llama and Mistral can be deployed in your own cloud or on-premise, giving you full control over where your data flows. The frontier API models (GPT, Claude, Gemini) run through their providers, though enterprise agreements offer data handling terms that prevent your data being used for training. If running in your own environment is a hard requirement, we architect around open-source models deployed in your infrastructure.
Through engineering, not hope. Retrieval grounding ensures the model answers from your actual content. Prompt engineering encodes your requirements and constraints. Output validation catches problems before they reach users. Systematic evaluation against representative test inputs measures quality objectively. And for use cases where accuracy is critical, human review workflows where appropriate. No LLM application is perfect, but these techniques make output reliable enough for production. The retrieval and evaluation engineering is where most of the accuracy comes from, and it’s exactly the work that distinguishes a production application from a demo.
Cost management is part of the architecture. We design with caching to avoid redundant model calls, model selection per task (cheaper models for simpler steps), context window management to control token usage, and usage monitoring to catch cost issues before they scale. We model the expected cost profile at your anticipated usage before you commit, so the economics are clear upfront rather than a surprise after launch.
A focused LLM application with a clear use case and clean data typically takes four to eight weeks from scoping to production. Applications with complex retrieval requirements, multiple LLM workflows, or extensive integrations take longer. The most common timeline factor is data readiness: if the content the application needs to retrieve from requires cleanup or restructuring, that adds time. We assess this during scoping.
We build systematic evaluation rather than relying on spot-checks. This means assembling a test set of representative real-world inputs, defining what good output looks like for your use case, measuring output quality objectively against that standard, and testing edge cases and adversarial inputs. We don’t consider an application ready until it performs reliably across this evaluation, not just on the easy cases. For many use cases, we also build ongoing evaluation so quality can be monitored in production.
You do. Full source code, all prompt configurations, the retrieval pipeline, and all integration logic belong to you from day one. The application runs on your infrastructure and your chosen model providers. There’s no proprietary platform lock-in. The prompting and RAG engineering often represent significant value, and that value is entirely yours to keep, maintain, and build on.
An active one. We need access to the people who understand the use case, the data the application will work with, and the systems it will integrate into. For document or knowledge use cases, we need someone who can tell us what good output looks like and what’s unacceptable, because defining the quality bar is a domain question, not just a technical one. We handle the LLM and product engineering. Your team provides the domain context and the decisions about what the application should do. You don’t need internal AI expertise to work with us, but you do need someone who knows the problem well.
An active one. We need access to the people who understand the use case, the data the application will work with, and the systems it will integrate into. For document or knowledge use cases, we need someone who can tell us what good output looks like and what’s unacceptable, because defining the quality bar is a domain question, not just a technical one. We handle the LLM and product engineering. Your team provides the domain context and the decisions about what the application should do. You don’t need internal AI expertise to work with us, but you do need someone who knows the problem well.
Yes, and in some ways it’s more realistic for you than for large enterprises. You typically have less bureaucracy, clearer decision-making, and more focused use cases. The approach we take, building applications on existing models rather than training custom ones, is specifically what makes LLM development accessible without a large enterprise budget or an in-house AI team. We scope projects to fit a clear use case and a defined budget, starting with the application that delivers the most value, rather than an open-ended enterprise AI program. This staged, focused approach is well suited to mid-market companies.
Tell us what you want the LLM application to do.
Tell us what you want the LLM application to do.
If you have an LLM application in mind, or you’re trying to figure out whether you need RAG, fine-tuning, or something simpler, tell us about it. We’ll give you an honest assessment of the right approach and what it would take to build.
Thirty minutes. A product engineer. A straight answer, including if the approach you’re considering is more than you need.
No pitch. If you need less than you think, we’ll tell you.