AI features working in demos but not in production for real users?
LLM costs growing faster than revenue because the integration wasn't designed for scale?
Generative AI engineers who build production LLM systems -- from GPT-4 integration and RAG pipelines to multi-agent workflows -- for teams that need AI features that work in the real world, not just demos.
AI engineers with GPT-4, Claude, Gemini, and open-source LLM experience
RAG systems, agentic workflows, fine-tuning, and AI evaluation frameworks
20+ AI systems built. Start in days. Fixed cost or retainer.
We work with founders and engineering teams building generative AI products and features.
Building a product where generative AI is the core capability. We design the full AI architecture -- model selection, prompt engineering, RAG system, evaluation framework, and production infrastructure -- so you ship with confidence.
Adding AI writing, search, document processing, or automation to existing enterprise software. We integrate LLMs into your existing systems without disrupting what's already working.
We provide generative AI engineering resource for agencies delivering AI products to clients -- embedded in your delivery process, following your standards.
AI pilot that didn't deliver. Prompts that work in isolation but fail at scale. We diagnose what went wrong and rebuild the system correctly -- with evaluation metrics so you know it's working.
Production-grade integration of OpenAI GPT-4o, Anthropic Claude, Google Gemini, or open-source models (Llama, Mistral) into your product backend. Streaming, rate limiting, cost management, fallback strategies, and evaluation built in from the start.
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), retrieval pipeline, and re-ranking. RAG systems that answer accurately, not confidently wrong.
Multi-step AI agents that use tools, retrieve information, and take actions. Designed with clear task decomposition, tool definitions, retry logic, and failure handling. Agentic workflows that complete tasks reliably, not just sometimes.
Full AI-first products from discovery to production -- AI writing assistants, document intelligence platforms, code generation tools, research assistants, and domain-specific expert systems. We've shipped 20+ AI systems across industries.
Automated evaluation pipelines that measure LLM output quality -- accuracy, consistency, format adherence, and task completion rate. Regression testing when prompts or models change. Production monitoring for quality drift. You can't operate AI in production without measuring it.
Fine-tuning for tasks where general models don't produce consistent enough output -- domain-specific extraction, style-constrained generation, or classification tasks. Training data preparation, fine-tune job execution, evaluation against base model, and production deployment.
20+ AI systems built. LLM integration, RAG, agents, and evaluation. Fixed cost or retainer.
We've built AI systems that handle real production traffic -- 20+ AI products shipped across healthcare, fintech, operations, and content. We know what breaks at scale and how to prevent it.
OpenAI GPT-4o, Anthropic Claude 3.5, Google Gemini, Meta Llama, Mistral -- we work across the major providers and recommend based on your requirements, not familiarity with one API.
We measure before we optimise. Every AI system we build includes an evaluation framework so you can prove it works and catch regressions when things change.
Evaluation scores, cost per request, accuracy metrics, and error rates -- at the cadence you choose. AI performance is visible, not assumed.
LLM costs are a product cost. We design cost-efficient integrations -- model tiering, caching, token budgets -- so costs are predictable and don't surprise you at the end of the month.
AI integration is one layer of an AI product. Our engineers handle the backend API, database, authentication, and deployment -- the complete product, not just the AI component.
For a specific AI feature, LLM integration, or RAG system build.
For sustained AI product development or a full AI-powered system build.
A full AI engineering team for complex AI-first products or enterprise AI deployments.
A single generative AI feature integrated into your product with evaluation framework and production monitoring.
A complete AI-first product with multiple features, RAG system, agents, evaluation pipeline, and production deployment.
Enterprise AI deployments with fine-tuning, on-premise or private cloud hosting, compliance requirements, or multi-system integration.
Tell us the use case -- what you want the AI to do, what data it works with, and what system it needs to integrate with.
A 30-minute call to understand the task, the data, and what reliable production performance means for your use case. We'll tell you what's feasible and what the right architecture is.
A clear proposal with scope, timeline, model recommendations, and fixed or retainer cost.
Engineers onboard in days. Evaluation baseline set in week one. First AI features in production within two weeks.
20+ AI systems built. Engineers available in days. Fixed cost or monthly retainer. Full source code ownership.
Frequently Asked Questions
Model selection depends on your task requirements, performance expectations, data privacy constraints, and budget. GPT-4o is the strongest general-purpose model but the most expensive. Claude 3.5 Sonnet is competitive for reasoning and document tasks. GPT-4o-mini and Claude Haiku handle routine classification and extraction at a fraction of the cost. Open-source models (Llama 3, Mistral) are the right choice when data cannot leave your infrastructure. We recommend the right model after understanding your specific use case -- not by defaulting to the most familiar one.
A focused AI feature -- an LLM integration for a specific task, with evaluation and production deployment -- typically takes 8--12 weeks. The time is spent on: prompt design and iteration (2--3 weeks), retrieval system if RAG is required (2--3 weeks), backend integration (2--3 weeks), evaluation framework setup (1--2 weeks), and production deployment (1 week). Projects that try to skip evaluation typically take longer overall because they debug production failures without measurement.
Data privacy options range from API-level controls (OpenAI Enterprise and Anthropic don't train on API data by default) to private deployment of open-source models on your own infrastructure. For regulated industries, we deploy open-source models (Llama 3, Mistral) on your AWS, GCP, or Azure environment -- no data leaves your infrastructure. We design the data handling architecture based on your specific privacy requirements and regulatory obligations, not a generic approach.
Yes. Most generative AI projects involve integrating with existing databases, APIs, CRMs, ERPs, or document management systems. We design the integration architecture that connects the LLM to your data sources -- reading from your databases, calling your APIs, and writing structured results back to your systems. The AI capability sits within your existing data architecture, not as a separate system.