AI Development
End-to-end AI product development: from use case scoping and model selection to production deployment, monitoring, and continuous improvement loops.
What's included
Use case scoping
We help you identify where AI creates real value vs. where it adds complexity without measurable ROI. Output: a prioritised build list, not a vague roadmap.
MCP & tool integration
Connect LLMs to your databases, APIs, and internal systems using the Model Context Protocol. We build the integration layer that turns a model into a useful product.
RAG & knowledge systems
Retrieval-Augmented Generation pipelines with vector databases, chunking strategies, and hybrid search.
AI agents & automation
Autonomous agent systems with tool use, memory, and multi-agent orchestration. We scope the boundary between what should be agentic and what should stay deterministic.
Fine-tuning & model adaptation
Task-specific fine-tuning (classification, extraction, formatting, domain vocabulary) and RLHF-aligned instruction tuning when base models fall short.
The engagement process
Use case audit
We map your workflow to identify the highest-ROI AI opportunities. Not every workflow should be AI-enabled; we help you prioritise the right ones.
Prototype & validate
Rapid prototype to validate that the AI approach works before full investment. We define evaluation metrics upfront so the prototype has a clear pass/fail gate.
Production build & evaluation
Inference infrastructure, prompt engineering, cost management, and fallback handling, built alongside an evaluation pipeline with golden datasets and LLM-as-judge scoring.
Monitoring & iteration
Production observability across latency, cost, and hallucination rate. User feedback collection and a continuous improvement cadence from day one.
Shipped to real workflows, not demos
We have shipped AI products to production, including MedSynth, a clinical discharge summary generator using fine-tuned GPT-4, used in real healthcare workflows.
Honest about what AI cannot do
We will tell you when a rules-based system or deterministic algorithm is the right tool instead of an LLM. We optimise for your outcome, not for building more AI.
Web3 and AI under one roof
Our lab is active in on-chain AI agents: ElizaOS multichain extensions, GAIA autonomous DeFi agents. If your use case lives at the intersection, we already have the foundation built.
Our stacks & tools
90-day post-launch warranty
Every engagement includes a 90-day warranty after launch. If something breaks in production, we fix it. No new contract, no billing conversation.
Questions we hear often
Specific questions? Book a 30-minute discovery call. No commitment, just honest answers.
Get in touchYes, this is one of our most common engagements. We start with a codebase review to assess what should be kept, rewritten, or de-risked. We document the architecture gaps and propose a practical path to a production-grade system.
RAG connects an LLM to external knowledge at inference time (good for large, changing knowledge bases). Fine-tuning adjusts the model weights for specific tasks or tone (good for consistent formatting, domain vocabulary, or classification tasks). In most cases RAG is the right starting point; we will tell you honestly if fine-tuning is warranted.
Yes. Our innovation lab has prototypes of multichain AI agents (ElizaOS-based) that read on-chain state and execute transactions autonomously. We can scope agent systems that bridge AI decision-making with on-chain execution.
We build evaluation pipelines with golden datasets and LLM-as-judge scoring. We define acceptable quality thresholds before launch and instrument production with feedback collection so quality can be tracked over time.
Free 30-minute call
Ready to scope your project?
Tell us what you're building. We'll ask the right questions, validate the approach, and tell you honestly what it would take.