We've built production LLM systems for clients in the US, UK, and Ireland, RAG pipelines processing thousands of documents daily, agentic workflows handling multi-step automation, and evaluation frameworks that measure output quality before any prompt ships.
Start within days, no lengthy hiring cycles or notice periods.
LLM engineers who have shipped RAG systems, evaluation frameworks, and agentic workflows in production.
Transparent weekly reporting with evaluation metrics so you see improvement in numbers, not descriptions.
Recognition
Struggling to find prompt engineers who have actually shipped LLM systems into production, not just playground demos?
Burned by AI pilots that looked good in testing but failed when real users and real data were involved?
LLM outputs inconsistent or unreliable in production, even when the demo looked good?
In short
RaftLabs provides prompt engineers who have shipped production LLM systems, RAG architectures, agentic workflows, and evaluation pipelines, for clients in the US, UK, and Australia. Engagements run part-time, full-time, or as a dedicated team. Engineers start within 1-2 weeks at USD 2,400/month.
Hiring a prompt engineer sounds simple. Getting one who understands the full picture, retrieval architecture, evaluation pipelines, cost management, and failure handling at production load, is a different problem.
We've built LLM-powered systems for clients in the US, UK, Ireland, and Australia. That includes RAG systems processing thousands of documents daily for enterprise knowledge bases, agentic workflows that automate multi-step operational tasks, and evaluation frameworks that catch regression before it reaches users. These are live systems with real traffic, not prototypes that only work on the demo data.
If you need an engineer who has debugged hallucinations at scale, tuned retrieval pipelines under latency constraints, and designed evaluation frameworks that actually measure what matters, that is the work we do.
We work with product teams building AI features and organisations deploying LLMs in production workflows.
Adding an AI assistant, document summariser, or content generator to an existing product. We design the prompts, build the integration, and set up evaluation so you know the feature is actually working, not just passing a demo.
Using LLMs to automate document processing, customer support, report generation, or internal knowledge retrieval. We design the prompt architecture and RAG system that makes LLMs reliable for operational workflows.
Building products where LLMs are the core capability, AI writing tools, research assistants, code generation products, or domain-specific expert systems. We help you build prompts and evaluation frameworks that hold up at scale.
AI pilot projects that didn't deliver. Prompts that worked in testing but failed in production. We diagnose what went wrong and rebuild the prompt architecture, retrieval system, or evaluation framework that was missing.
Systematic prompt design for production use cases, system prompts, few-shot examples, chain-of-thought structures, and output formatting specifications. We design prompts that produce consistent, structured output across varied inputs, not just the inputs you tested in the playground.
Retrieval-augmented generation systems for document Q&A, knowledge base assistants, and enterprise search. Chunking strategy, embedding model selection, vector database setup (Pinecone, Weaviate, pgvector), and retrieval pipeline design. The difference between a RAG system that answers accurately and one that confidently hallucinates is the architecture.
Automated evaluation pipelines that measure LLM output quality, accuracy, consistency, format adherence, and task completion. Regression testing when prompts or models change. You can't improve what you don't measure.
Multi-step AI agents that use tools, retrieve information, and take actions, designed to handle edge cases reliably. We architect agentic systems with clear task decomposition, tool definitions, and failure handling so agents do what they're supposed to, not what they interpret.
Integration of OpenAI, Anthropic Claude, Google Gemini, or open-source models (Llama, Mistral) into your product backend. Streaming responses, rate limiting, cost management, fallback strategies, and caching for repeated queries.
Choosing the right model for each task, not every feature needs GPT-4. We design tiered model strategies that use smaller, cheaper models for routine tasks and larger models only where the quality difference matters. LLM costs are controllable; you just need the right architecture.
We've built LLM systems that handle real production traffic. We understand token limits, latency constraints, cost management, and the failure modes that don't appear in demos.
Part-time for a specific AI feature or RAG build. Full-time for a complete LLM-powered product. Dedicated AI team for multi-system deployments. Choose the model that fits your stage and budget.
Fixed monthly rates with no hidden costs. You know exactly what you're paying before we start. From USD 2,400/month for part-time engagement.
Evaluation metrics, prompt iteration logs, and performance benchmarks delivered weekly. You see improvement in numbers, not descriptions. Daily standups or weekly syncs on Slack and Asana.
We set up measurement before we optimise. Without an evaluation framework, prompt engineering is guesswork. We build the metrics first so every improvement is verifiable.
Prompt engineering is one part of building AI products. Our engineers also handle the backend integration, API design, database, and deployment, so you don't need a separate engineering team to ship the product.
| RaftLabs | In-House | Freelance | |
|---|---|---|---|
| Time to hire top Prompt Engineering developers | 1 day to 2 weeks | 4 to 6 weeks | 1 to 12 weeks |
| Project initiation time | 1 day to 2 weeks | 2 to 10 weeks | 1 to 10 weeks |
| Risk of project failure | Exceptionally low with a 98% success rate | Low | Very High |
| Developers supported by project management | Yes, dedicated PM and Agile processes | Varies | No |
| Exclusive development team | Yes, dedicated team guaranteed | Yes | No |
| Assurance of work quality | Yes, with quality assurance processes | Yes | Varies |
| Advanced development tools and workspace | Yes, enterprise-grade tools | Yes | Varies |
Tell us the use case, what you want the AI to do, the data it needs to work with, and the system it needs to integrate with. Fill out the contact form and we'll reach back within one business day.
A 30-minute call to understand the task, the data, and what "working well" means for your use case. We'll tell you what's feasible and what the right technical approach is. No sales pitch, just an honest conversation about fit.
A clear proposal with scope, timeline, and fixed or retainer cost. No vague estimates, concrete numbers you can plan around.
Engineers onboard in days. Evaluation baseline set in week one. First working prompts in production within two weeks.
Pricing
Dedicated senior engineers from $6,000/month (minimum pod $12,000/month).
AI engineers available in days. Fixed cost or monthly retainer. Full source code ownership.
Prompt engineering is part of AI development, not separate from it. A prompt engineer who only writes prompts without understanding the surrounding system, the retrieval architecture, the API integration, the evaluation framework, the cost model, produces prompts that work in isolation but fail in production. Our AI engineers do both: they design prompts as part of building the complete LLM-powered system, not as a standalone activity.
Reliability comes from three things: prompt structure (clear instructions, output format specifications, few-shot examples), retrieval quality (the right context provided in the right format), and evaluation (automated tests that measure whether outputs meet requirements). We set up all three. Without evaluation, you're flying blind, you don't know when a prompt change makes things better or worse.
Hallucinations are reduced by providing accurate context (RAG), constraining the model's response format, adding verification steps for critical outputs, and setting confidence thresholds that route uncertain outputs to human review. For high-stakes use cases (medical, legal, financial), we design systems with human review loops rather than relying on the model alone. The right architecture depends on the acceptable error rate for your specific use case.
We work with OpenAI GPT-4o and GPT-4o-mini, Anthropic Claude 3.5 Sonnet and Haiku, Google Gemini 1.5 Pro, and open-source models including Meta Llama 3 and Mistral, deployed via API or self-hosted on AWS, GCP, or Azure. Model selection depends on your performance requirements, data privacy constraints, and cost budget. We don't recommend a specific model without understanding your actual use case.
Part-time from USD 2,400/month (80 hours). Full-time from USD 4,800/month (160 hours). Dedicated AI teams from USD 15,000/month. Project work from USD 10,000 for an AI feature build scope.
Contact us for a tailored quote based on your specific requirements.
Most engagements start within 1-2 weeks of signing. We have engineers available on short notice for urgent projects. The discovery call and proposal typically happen within 2-3 business days of your initial contact.
Yes. We sign NDAs as standard practice for all client engagements. Contact us to get the process started.
Slack for daily communication, Asana for project tracking. You will have direct access to your engineer and a dedicated project coordinator throughout the engagement. Daily standups or weekly syncs are available depending on your preference.
We build RAG systems for document Q&A and enterprise knowledge bases, agentic workflows for multi-step automation, LLM-powered content pipelines, chatbots with structured output requirements, and evaluation frameworks for ongoing quality monitoring. We work across OpenAI, Anthropic Claude, Google Gemini, and open-source models (Llama, Mistral) depending on your performance, cost, and data residency requirements.
Yes. Our engineers integrate LLM features into existing products regularly, whether that means connecting to your existing database, working within your current API architecture, or building retrieval pipelines on top of your document storage. We adapt to your stack rather than asking you to change it.