AI Product Teams
Building products where LLM performance directly drives user value. We design the LLM architecture -- model selection, prompt engineering, RAG system, evaluation pipeline -- that makes the product work reliably for real users.
LLM proof of concept working in demos but breaking with real user data?
No systematic way to measure whether your LLM system is performing correctly?
Hire LLM Engineers
Large language model engineers who build production AI systems -- RAG pipelines, fine-tuned models, evaluation frameworks, and multi-model architectures -- that work at scale beyond the proof of concept.
LLM engineers with RAG, fine-tuning, evaluation, and multi-model pipeline experience
Works across OpenAI, Anthropic, Google, Meta Llama, and Mistral
Start in days. Fixed cost or monthly retainer.
Who We Work With
We work with engineering teams that need specialised LLM expertise beyond basic API integration.
Enterprise Knowledge Management
Using LLMs to search, summarise, and extract insights from large document corpora. We build the RAG architecture and fine-tuning strategy that makes LLMs accurate on your specific knowledge base.
Research and Evaluation Teams
Teams that need LLM evaluation frameworks, benchmarking infrastructure, or systematic comparison of model performance across tasks. We build the evaluation tooling that makes LLM development measurable.
Teams Scaling from Prototype
LLM prototypes that worked but need systematic engineering to handle production load, edge cases, and reliability requirements. We rebuild the architecture with the evaluation and monitoring infrastructure that production systems require.
Our LLM Engineering Services
RAG System Architecture
Retrieval-augmented generation systems designed for accuracy at scale. Document processing pipelines, chunking strategy optimisation, embedding model selection (OpenAI, Cohere, open-source), vector database implementation (Pinecone, Weaviate, pgvector, Qdrant), retrieval pipeline design, and re-ranking for precision. The difference between a RAG system that answers correctly and one that hallucinates confidently is the architecture.
LLM Fine-Tuning
Domain-specific fine-tuning for tasks where general LLMs don't produce consistent enough output. Training data curation and formatting, fine-tune job execution on OpenAI, Anthropic, or open-source models, evaluation against the base model, and production deployment. LoRA and QLoRA fine-tuning for efficient adaptation of open-source models.
LLM Evaluation Framework
Systematic evaluation infrastructure for LLM output quality -- task-specific metrics, golden dataset construction, automated evaluation pipelines (using GPT-4 as judge or deterministic metrics), regression testing when prompts change, and production monitoring for quality drift. You cannot improve LLM performance without measuring it.
Multi-Model Architecture
LLM system design with multiple models at different price/performance tiers -- routing complex tasks to large models and routine tasks to small, cheap models. Multi-provider fallback for reliability. Model-agnostic abstraction layers that make provider switching a configuration change rather than a rewrite.
Agentic System Design
Multi-step AI agents with tool use, planning, and memory. Task decomposition design, tool definition, retry and error handling logic, conversation state management, and human-in-the-loop escalation paths. Agents designed to complete tasks reliably, not to pass demos.
Open-Source LLM Deployment
Self-hosted Llama 3, Mistral, or other open-source models on AWS, GCP, or Azure -- for data privacy requirements, regulatory compliance, or cost reduction at high inference volumes. vLLM or Ollama for efficient inference serving, with the same API surface as hosted providers.
Hire LLM engineers who build AI systems that work in production
RAG systems, fine-tuning, evaluation frameworks, and multi-model architecture. 20+ AI systems built.
What Sets Our LLM Engineers Apart
Evaluation-First Engineering
We build evaluation before we optimise. Every LLM system we deliver includes automated evaluation so you know what's working and can measure the impact of changes.
Multi-Provider Experience
OpenAI, Anthropic Claude, Google Gemini, Meta Llama, Mistral, Cohere -- we work across providers and recommend based on your specific task, privacy requirements, and cost budget.
Production System Track Record
We've shipped 20+ AI systems to production. We know the failure modes that don't appear in prototypes and how to design around them.
Regular Reporting
Evaluation metrics, accuracy scores, cost per query, and error rates -- at the cadence you choose. LLM performance is visible in numbers, not described in status updates.
Cost Architecture
LLM inference costs are controllable. We design cost-efficient architectures -- model tiering, caching, batching, and open-source alternatives -- so costs scale predictably with usage.
Full-Stack Delivery
LLM engineering is one layer. Our engineers also handle the backend API, database, authentication, and deployment -- the complete AI product, not just the model integration.
Comparative Analysis of RaftLabs, In-House & Freelancers
LLM Engineer Hiring Costs -- Monthly
Hire Resource (Part-Time)
For a specific RAG system, fine-tuning project, or evaluation framework build.
- 10 work days per month (80 hours)
- Dedicated project coordinator
- Senior team member support when required
Hire Resource (Full-Time)
For sustained AI product development or a full LLM-powered system build.
- 20 work days per month (160 hours)
- Dedicated project coordinator
- Full senior team support included
Dedicated AI Team
A full AI engineering team for complex LLM products or enterprise AI deployments.
- 20 work days per month (160 hours) per resource
- Dedicated project manager
- AI, backend, and frontend resources available
LLM Project Costs -- Project Basis
RAG System Build
A production RAG system for document Q&A or knowledge base search -- with evaluation framework and accuracy baseline.
- Document pipeline, vector store, and retrieval
- Evaluation framework and accuracy metrics
- 8--12 week delivery
Full LLM Product
A complete LLM-powered product with multiple AI features, RAG, agents, fine-tuning, and evaluation pipeline.
- Multiple LLM features with shared knowledge base
- Fine-tuning, evaluation, and production monitoring
- 16--24 week delivery
Enterprise AI System
Enterprise LLM deployments with private model hosting, custom fine-tuning, compliance requirements, or complex multi-system integration.
- Private model deployment or custom fine-tuning
- Compliance and data residency requirements
- Custom scoping required
Our AI and Backend Stack
Get Started Today
Contact Us
Tell us the LLM task -- what you want the AI to do, what data it works with, and what reliability means for your use case.
Discovery Call
A 30-minute call to understand the task, the data, the performance requirements, and what existing implementation we're working with or building from scratch.
Get a Proposal
A clear proposal with model recommendations, architecture approach, timeline, and fixed or retainer cost.
Project Kickoff
Engineers onboard in days. Evaluation baseline set in week one. First production-ready LLM features within two to three weeks.
Hire LLM engineers who build AI systems that work at scale
20+ AI systems built. Engineers available in days. Fixed cost or monthly retainer. Full source code ownership.
Frequently Asked Questions
Prompt engineering is designing the instructions and context that go into an LLM. LLM engineering is the broader discipline that includes prompt engineering plus the surrounding systems -- retrieval architecture, fine-tuning, evaluation frameworks, cost management, production monitoring, multi-model routing, and the backend integration that makes LLMs work in real products. Most production LLM failures come from the system design, not from poorly worded prompts.
Prompt engineering (with or without RAG) is the right starting point for most tasks -- it's faster, cheaper, and sufficient for most use cases. Fine-tuning makes sense when: you need consistent output format or style that prompt engineering doesn't reliably achieve, you're running high query volumes where smaller fine-tuned models would reduce cost significantly, or you have a domain with specific terminology and reasoning patterns that general models handle poorly. We evaluate whether fine-tuning is justified based on your specific task and volume.
Evaluation depends on the task. For extraction tasks: precision, recall, and F1 score against a gold-standard dataset. For generation tasks: ROUGE/BLEU scores, human evaluation rubrics, or GPT-4-as-judge scoring. For RAG systems: retrieval accuracy, answer faithfulness (does the answer match the retrieved context?), and answer relevance. We build the evaluation framework before we optimise, so every change in prompts, models, or retrieval is measured against a baseline.
Yes. For use cases where data cannot be sent to third-party APIs, we deploy open-source models (Meta Llama 3, Mistral, Mixtral) on your AWS, GCP, or Azure infrastructure using vLLM or Ollama for efficient inference. These models run entirely within your infrastructure -- no data leaves your environment. Performance and cost depends on the model size and your hardware budget. We evaluate the tradeoff between model performance and privacy requirements for your specific use case.