LLM engineering services
Production LLM systems that answer from your data, stay within guardrails and hold up under real traffic: fine-tuning, RAG, evaluation and the APIs around them.
What is LLM engineering?
LLM engineering is the work of turning a large language model into a dependable product feature. It covers choosing the model, connecting it to your data through RAG or fine-tuning, testing prompts, adding guardrails, and serving it through APIs that stay fast, accurate and affordable under real traffic.
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What we build
Fine-tuning and adaptation
Train foundation models on your data with full fine-tuning, LoRA or QLoRA when a general model cannot match your domain.
RAG architecture
Ground answers in your documents: chunking, hybrid search, re-ranking and citations your team can check.
Prompt engineering and evaluation
Prompt libraries, structured outputs and evaluation sets that turn unpredictable outputs into reliable ones.
APIs and integration
Production APIs with caching, rate limits, provider failover and cost controls, wired into your existing systems.
Safety and guardrails
Content filtering, output validation, PII detection and hallucination checks that protect your users and brand.
Benchmarking
Custom benchmarks for accuracy, latency and cost, so model choices are made on data rather than hype.
Related client work
Delivered for clients, not demos.
An AI grow-guide web app for a seed brand
Product design, the web app and a retrieval-based AI guide that answers in Hindi, Hinglish or English and cites its source.
Next.js, Supabase, retrieval-grounded AILegal Discovery AIParties, claims and key facts pulled from discovery PDFs
Private software for a legal team that reads form-based and free-form discovery documents and extracts the facts lawyers need.
Python, NLP, PDF parsing. Private deployment.Custom fine-tuned GPTA GPT model fine-tuned on a domain dataset
A GPT model fine-tuned on a client’s domain-specific dataset for a specialised use case.
Python, LangChain, NLPHow a project runs
Use case and data
We map the use case, the data and the quality bar, and choose RAG, fine-tuning or both.
Knowledge and data prep
Clean training sets or a knowledge base with a chunking and retrieval design.
Build and evaluate
Prompts, pipelines and parsers, measured against an evaluation set at every step.
Deploy with guardrails
Production infrastructure, safety layers and monitoring for cost, latency and quality.
Tools we work with
Chosen per project; we are not tied to one provider.
Questions about LLM engineering
Should we fine-tune a model or use RAG?
Use RAG when answers must come from documents that change, such as policies, product data or case files. Fine-tune when the model must follow a style, format or domain reasoning consistently. Many production systems combine both, and we test which approach performs better on your own data.
How do you reduce hallucinations?
We ground answers in retrieved sources with citations, validate outputs against trusted data, set confidence thresholds that send uncertain answers to a person, and run evaluation sets before every release. Together these catch wrong answers in testing rather than in front of your users.
Can our data stay private?
Yes. We can use private model endpoints that do not train on your data, keep documents and vector stores inside your own cloud account, and redact personal information before any text reaches a model. Access controls and logging record who can query what.
How do you keep LLM costs under control?
We cache repeated answers, trim prompts, route simple queries to smaller and cheaper models, and call the largest models only when a task needs them. Cost per request is monitored from day one, so spending stays predictable as usage grows.
Related services
Tell us what you want to build
A free 30-minute scoping call with the engineers who would build it. You leave with a clear next step, whether or not we work together.