MODEL INTEGRATION
Accessing AI through a separate tool is a workflow addition, not a workflow improvement. The teams that see the most value from AI are the ones where AI capabilities are embedded in the systems they are already working in, not the ones that have to remember to open another tab.
We integrate AI models, large language models, vision models, classification models, and purpose-built ML models, directly into your existing applications, workflows and data systems. The AI capability becomes part of the tool, not an addition to it.
Model integration is an engineering problem as much as an AI problem. The model is one component; the API design, the data pipeline, the prompt architecture, the caching strategy, and the fallback behaviour determine whether the integration is reliable and valuable in production rather than impressive in a prototype.
INTEGRATION DISCOVERY & SCOPING
We assess your existing applications and workflows against AI integration opportunities, where AI capability in the tool your team already uses would produce measurable improvement in speed, quality or capacity. Scoping produces a prioritised integration roadmap with expected impact and implementation complexity estimated for each item before any build is committed to.
PROMPT ENGINEERING & OPTIMISATION
Prompt engineering is the discipline of defining what the model receives so that what it returns is consistently useful. We design prompt architectures for your specific use case, including system prompt structure, context injection, output formatting, and validation logic, and optimise them against real-world inputs rather than against synthetic test cases that do not reflect production variation.
TESTING & RELIABILITY ENGINEERING
AI integrations fail in ways that are qualitatively different from conventional software failures, outputs that are subtly wrong rather than obviously broken, performance that degrades with prompt changes, and edge cases that do not appear in standard testing. We build evaluation suites that test the integration against the range of inputs it will encounter in production and establish quality thresholds before deployment.
API DESIGN & ARCHITECTURE
A reliable AI integration requires careful API design, request routing, response handling, caching for repeated queries, rate limiting to manage cost, and fallback behaviour for when the model is unavailable or returns an unacceptable response. We design this layer before any front-end or workflow work begins, because retrofitting it is substantially more expensive than designing it correctly from the start.
DATA PIPELINE INTEGRATION
A model without access to your specific data produces generic outputs. We design the data pipelines that retrieve, chunk, embed and retrieve relevant context from your knowledge bases, databases and document stores, making the model’s outputs specific to your organisation rather than to the training corpus it was built on.
MONITORING & CONTINUOUS IMPROVEMENT
Model outputs drift as models are updated and as the distribution of real-world inputs diverges from what was anticipated in design. We implement output monitoring, cost tracking, latency measurement and user feedback collection, so the integration is observable in production and can be improved based on evidence rather than anecdote.
A model that responds correctly in a test environment is not a delivered integration. The deliverable is a model capability that is reliable in your production environment, connected to your data, and accessible from the tools your team uses, without requiring a behaviour change from the people who benefit from it.
AI API costs and response latency are engineering constraints, not post-launch surprises. We design caching strategies, request batching, model selection and prompt efficiency into the integration architecture before build, so the production cost and latency match what was designed, not what emerged from an unconstrained implementation.
We build evaluation suites for every integration, testing the range of real-world inputs the system will encounter, establishing quality thresholds, and validating that the integration meets those thresholds before production users depend on it. An integration that ships without evaluation ships with unknown quality.
