LLM outputs inconsistent or unreliable in production, even when demo looked good?
AI features not performing as expected because prompts aren't structured for production load?
AI engineers who design, test, and optimise LLM prompts for production systems -- chatbots, document processing, code generation, content pipelines, and agentic workflows that work reliably in the real world.
AI engineers with OpenAI, Anthropic, Gemini, and open-source LLM experience
Prompt design, RAG systems, evaluation frameworks, and LLM integration
Start in days. Fixed project cost or monthly retainer.
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.
Prompt design, RAG systems, evaluation frameworks, and LLM integration for products that ship.
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.
We set up measurement before we optimise. Without an evaluation framework, prompt engineering is guesswork. We build the metrics first so improvements are measurable.
OpenAI GPT-4/4o, Anthropic Claude, Google Gemini, Meta Llama, Mistral -- we work across the major platforms and know when each is the right choice for your use case and budget.
Evaluation metrics, prompt iteration logs, and performance benchmarks -- you see the improvement in numbers, not descriptions.
LLM costs can escalate fast at scale. We design systems with cost budgets in mind -- model tiering, caching, prompt compression, and batching to keep per-request costs predictable.
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.
For a specific AI feature, prompt optimisation, or RAG system build alongside your existing team.
For sustained AI feature development or a full LLM-powered product build.
A full AI engineering team for complex AI-first products or multi-system LLM deployments.
A single LLM-powered feature -- chatbot, document Q&A, content generator, or extraction pipeline -- integrated into your product.
An AI-first product with multiple LLM features, RAG system, evaluation pipeline, and production deployment.
Enterprise-scale LLM deployments with custom fine-tuning, compliance requirements, or multi-system integration.
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.
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.
A clear proposal with scope, timeline, and fixed or retainer cost.
Engineers onboard in days. Evaluation baseline set in week one. First working prompts in production within two weeks.
AI engineers available in days. Fixed cost or monthly retainer. Full source code ownership.
Frequently Asked Questions
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.