We integrate LLMs, computer vision, and intelligent automation into your existing products and workflows, moving you from AI experimentation to production-grade capability.
Components
Library
Capability
LLM & RAG
Standard
Responsible AI
Auditing your product and workflows to identify where AI creates genuine business value, and where it adds complexity without meaningful return.
Designing the AI service layer, RAG pipelines, and prompt engineering framework, validated with a working prototype before full production development begins.
Building production-grade AI features with robust error handling, semantic caching, fallback strategies, and clean API abstractions that survive model upgrades.
Deploying LLM evaluation frameworks, output quality scoring, and production dashboards so you always know exactly how your AI features are performing.
Most AI integrations fail not because the models underperform, but because the integration is brittle, the UX is confusing, or the cost model is unsustainable. We engineer for production from day one.
GPT-4 is not always the answer. We evaluate model capability, latency, cost, and data privacy requirements before selecting tooling for each feature.
Prompt design, versioning, evaluation harnesses, and regression testing, we treat prompts with the same rigour as application code.
AI API costs scale with usage. We design token budgets, caching strategies, and fallback paths so AI features are economically viable at scale.
Every AI call is logged, traced, and monitorable. We build evaluation pipelines that surface quality degradation before users notice it.
Production AI is a different discipline from AI prototyping. We bridge that gap.
Your team has built an AI demo that works in a notebook but can't get it to production quality, reliability, latency, and cost are all unresolved.
Production engineering for AI features, robust API design, error handling, fallback strategies, caching, and a monitoring layer that makes AI reliable at scale.
AI API usage is scaling faster than revenue, token costs are spiralling and there's no visibility into what's driving consumption or how to control it.
Token budget design, semantic caching, model routing, and usage dashboards, giving you full visibility and control over AI infrastructure costs.
OpenAI, Anthropic, Google Gemini, Mistral, and open-source models via Ollama or Hugging Face. We select based on your specific capability, cost, and data privacy requirements.
Yes. RAG pipelines over internal knowledge bases, documentation, CRM data, and product catalogues are a core part of our AI integration work.
We design data handling to minimise what's sent to external APIs, implement PII scrubbing where required, and advise on on-premise model options for sensitive use cases.
