AI development and LLM integration

What we build

AI runs across our work rather than sitting in a separate practice. We build AI features into products we are already delivering, add AI capability to systems clients already operate, and build standalone AI tools where the workflow warrants it.

• Workflow automation and document processing
• AI features embedded inside existing products
• Retrieval-augmented generation over your own data
• AI agents and tool-using assistants
• Natural-language reporting and query interfaces
• Custom AI tooling and internal copilots
• Integration into ERP, EHR, CRM and operational systems

How we approach AI work

The failure mode in AI projects is building something impressive that nobody uses. We start from the workflow and ask where a model genuinely removes effort. In Shafa EHR that meant summarising a case and flagging undocumented findings before a consultation closes. In GetHookd.ai it meant generating ad creative from patterns in live campaign data. In PullSight.ai it meant catching bugs before a senior engineer spends time on review.

• Start from the workflow, not the model
• Measure against the task, not a benchmark
• Design for cost and latency at production volume
• Keep humans in the loop where the stakes require it
• Integrate into existing systems rather than alongside them

Technology

We work with OpenAI and other LLM APIs, NLP tooling, LangChain, orchestration frameworks, vector databases and AI workflow tooling. We also expose AI capability outward — GetHookd.ai ships a REST API and an MCP agent endpoint so external AI clients can drive the platform directly.