RAG and semantic search
Connect language models to trusted business or product knowledge using retrieval pipelines, embeddings, vector search, grounding and context design.
Services AI & Intelligent Systems
AI systems engineered for production, not just prototypes.
We design and build intelligent systems where model quality, retrieval, latency, privacy, reliability and operating cost are treated as one engineering problem.
Where we help
Turn AI capability into dependable product functionality.
The goal is not to add AI for its own sake. We start with the user or business outcome, then design the model, retrieval, workflow and platform architecture around what the product actually needs to do.
Agents and AI workflows
Design controlled multi-step AI workflows for tasks that require reasoning, tools, business rules, integrations, approvals or human intervention.
Speech, voice and vision
Build experiences that can listen, speak, understand images or documents, and combine multiple modalities where the use case benefits from them.
AI inside existing platforms
Introduce AI into an existing web, mobile, commerce or enterprise product without treating the model as a separate island from the rest of the system.
Capabilities
AI engineering across the complete request path.
From model selection and context design to production APIs, evaluation, observability and cost controls.
Generative AI & LLM integration
Integrate language models into real product workflows with clear boundaries, fallback behaviour and application logic.
RAG & semantic search
Retrieval pipelines, embeddings, vector databases, ranking and grounded context for domain-specific answers.
AI agents
Tool-enabled workflows with controlled actions, orchestration, state and explicit business constraints.
Speech, voice & vision AI
STT, TTS, voice, image and document understanding for multimodal user experiences and workflows.
Model selection & routing
Choose models around task complexity, quality, latency, context requirements and operating economics.
Prompt & context design
System instructions, structured context, tool contracts and response constraints designed around the application.
Production approach
Design for the constraints that appear after the demo.
Define what a useful answer or action means and evaluate the system against realistic product scenarios.
Plan timeouts, fallbacks, caching and asynchronous work around the experience the user actually sees.
Minimise unnecessary data exposure and keep model access aligned with application permissions and product rules.
Use model choice, routing, retrieval, caching and workload design to keep recurring AI cost visible and controllable.
Related capabilities
AI works best as part of the wider platform.
Explore the engineering capabilities commonly paired with AI implementation.
Start a conversation
Have an AI use case that needs to work in production?
Tell us the problem, available data and expected user outcome. We can help frame the architecture and implementation path.