AI Services / LLM Development

LLM development — from prompt to production-grade

We design, fine-tune, and operate LLM applications — RAG, agents, copilots, custom-tuned models. Built for real users, real latency, and real ROI — not a single-shot demo.

600+
LLM features shipped
<200ms
P95 token latency
92%
Output acceptance rate
Multi
Model stack

Why Empiryx

Why teams choose Empiryx for LLM development

Model-agnostic: GPT, Claude, Llama, Mistral — picked per task fit
Eval-first delivery: success is measured in metrics, not vibes
Cost- and latency-aware: cheapest stack that hits the bar
RAG, agents, and fine-tuning — chosen for the workload, not habit
Observability: traces, per-call logs, and eval baselines from day one
Handoff-ready: documented code your team can own operationally

Expertise

LLM skills we specialize in

01

RAG pipelines

Retrieval, reranking, and citations for grounded LLM answers.

02

Fine-tuning

LoRA, QLoRA, full SFT on your domain with eval baselines.

03

Agents & tool use

Multi-step planning, tool calling, memory, and reliability patterns.

04

Copilots

In-app copilots wired to your real data and workflows.

Expertise

Roles & capabilities we specialise in

A deeper look at the specialists we place and the work they ship.

Model selection

OpenAI, Anthropic, Llama, Mistral — picked per task fit.

Pipelines

Ingest, chunk, embed, retrieve, generate, evaluate — modular and observable.

Fine-tuning

LoRA, QLoRA, full SFT — chosen on quality bar, budget, and latency.

Cost control

Model routing, caching, compression, batching — predictable spend.

Guardrails

PII redaction, jailbreak defense, prompt-injection filters, content policy.

Observability

Per-call logs, traces, metrics, and evals wired to dashboards and alerts.

Industries we serve

Built for high-stakes sectors

Domain-aware engineers who understand your sector's constraints — not just the syntax.

SaaS & Product

Embedded LLM features and copilots

FinTech

Regulated LLM services

Healthcare

Clinical copilots within approved scope

E-commerce

AI copy, search, and assist

FAQ

Answers to common questions

Process

How it works

01

Discovery

We scope the feature, the eval criteria, and the model options.

02

Vertical slice

We ship a thin end-to-end LLM feature — model, RAG, evals — in 2–4 weeks.

03

Harden

We add observability, guardrails, evals, and rollback before scale.

04

Operate

We monitor drift, cost, latency, and quality in production.

Get started

LLM features that ship to real users

Tell us the feature. We'll bring the model, the RAG, and the evals.

Let's talk

Tell us what you're building

Share your engineering goals and the Empiryx team will respond within 24 hours with a tailored path forward.