LLM Development
Custom language model solutions built for your domain, your data, and your quality bar — not just a demo.
Why off-the-shelf LLMs fall short in production
ChatGPT and similar general-purpose models are impressive in demos. In production business environments, they hallucinate on domain-specific facts, ignore your internal terminology, produce outputs that do not match your quality standards, and have no connection to your proprietary data.
The gap between "AI that works in a demo" and "AI that works reliably in your business" is where most LLM projects fail. Teams spend months on prompting experiments, get inconsistent results, and eventually shelve the project because they cannot trust the outputs.
We build LLM systems that close this gap — grounded in your data, tuned to your domain, evaluated against your quality standards, and deployed with the observability and governance your business requires.
The LLM Development Spectrum
We work across the full range of LLM approaches — choosing the right method for your use case and constraints
Prompt Engineering
Structured prompts and few-shot examples to guide model behavior without training
Best for: Quick wins, general tasks, cost-sensitive deployments
RAG Systems
Retrieval-augmented generation grounds responses in your proprietary documents and data
Best for: Knowledge-intensive tasks, document Q&A, factual accuracy
Fine-Tuning
Adapt foundation models to your domain terminology, style, and task-specific behavior
Best for: Specialized domains, consistent tone, high-volume inference
Agentic Systems
Multi-step AI agents that use tools, APIs, and reasoning to complete complex tasks autonomously
Best for: Complex workflows, multi-system orchestration, autonomous operations
What production LLM systems deliver
Measured outcomes from domain-specific LLM deployments
LLM Development Service Areas
Modular LLM capabilities you can adopt at any stage of your AI journey.
Domain-Specific AI Assistants
Intelligent assistants trained and tuned for your industry, your terminology, and your operational context.
- Customer support & internal helpdesk bots
- Sales & product recommendation assistants
- Operational decision-support agents
RAG & Knowledge-Aware Systems
Ground LLM responses in your proprietary data and knowledge bases for accurate, trustworthy outputs.
- Vector database design & indexing
- Hybrid retrieval & re-ranking pipelines
- Document ingestion & chunking strategies
Prompt & Orchestration Engineering
Design reliable, multi-step LLM workflows that perform consistently at scale in production.
- Prompt design, testing & versioning
- Multi-agent & chain-of-thought orchestration
- Tool use & function-calling integration
Quality, Safety & Evaluation
Systematic frameworks to ensure your LLM systems produce accurate, safe, and reliable outputs.
- Automated evaluation & regression testing
- Hallucination detection & safety guardrails
- Human review workflows & feedback loops
Real-World Use Cases
How organizations use custom LLM systems to solve problems that general-purpose AI cannot
Legal Document Review
A law firm was spending 20+ hours per contract on manual review for standard clause identification and risk flagging. A fine-tuned LLM trained on their contract library now pre-reviews documents, highlights non-standard clauses, and drafts redline suggestions — cutting review time by 75%.
Clinical Knowledge Assistant
A healthcare provider needed clinicians to quickly access treatment protocols, drug interactions, and patient history context during consultations. A RAG system over their clinical knowledge base and EHR data delivers accurate, cited answers in under 3 seconds.
E-commerce Product Content
A retailer with 50,000 SKUs had inconsistent, low-quality product descriptions written by multiple vendors. A fine-tuned LLM trained on their brand voice and category taxonomy now generates consistent, SEO-optimized descriptions at scale — 500 products per hour.
Internal Knowledge Management
A 500-person professional services firm had critical knowledge locked in PDFs, wikis, and email threads. A RAG-powered internal assistant lets employees ask natural language questions and get accurate, sourced answers from the firm's entire knowledge base.
Developer Productivity Assistant
A software company fine-tuned a code assistant on their internal codebase, architecture patterns, and coding standards. Developers get context-aware suggestions that follow internal conventions — not generic completions that require heavy editing.
Financial Report Summarization
An investment firm's analysts spent 3–4 hours per earnings report extracting key metrics and writing summaries. An LLM pipeline processes filings, extracts structured data, and generates analyst-ready summaries in under 5 minutes per report.
Our LLM Development Process
From use case definition to production deployment with quality gates at every stage.
Use Case Definition
Identify the highest-value LLM applications and define measurable success criteria
Data & Knowledge Audit
Assess available data, documents, and knowledge sources for grounding and training
Architecture Design
Select models, design RAG pipelines, plan orchestration flows, and define evaluation criteria
Build & Evaluate
Develop, test, and iterate against quality, safety, and performance benchmarks
Deploy & Monitor
Ship to production with observability, feedback capture, and continuous improvement loops
Have a specific LLM use case in mind?
Tell us what you are trying to build. We will assess the right approach — RAG, fine-tuning, or agentic — and give you a realistic picture of what it takes to get it into production.
Discuss Your LLM Project