← AI Strategy & Implementation
Overview
Implementation is where AI strategy meets reality: latency, hallucination, document freshness, and the fact that your CRM isn’t a tidy knowledge base. We build features - not demos - with retrieval, tool use where appropriate, and evaluation so you know when quality regresses as models or data change.
We also take code or apps produced with AI app builders and codegen tools (e.g. Lovable, Openclaw, and similar) and run them through deployment, hardening, and integration - hosting, secrets, CI/CD, API contracts, and systems - so what ships matches how your organisation actually runs.
Where this helps
- Teams that tried generic chat UIs and need answers grounded in their policies and products
- Support and success functions aiming to draft-first, verify-second workflows
- Product adding AI-assisted flows without handing users unbounded prompts
- Teams with a prototype from an AI builder that needs production deployment, review, and integration rather than another prompt iteration
What we focus on
- RAG & knowledge - Chunking, embeddings, source citation patterns, refresh jobs
- Tooling - Safe reads vs writes; confirmation gates for mutations
- Model & provider choices - Trade-offs on cost, latency, privacy, and capability
- Integration - Systems, SSO, logging, and feature flags
- Deploying AI-generated apps - Environment parity, build pipelines, runtime config, and cutover when the starting point is tooling like Lovable, Openclaw, or comparable platforms
How we work
We ship thin vertical slices: one workflow, measured end-to-end, before expanding surface area. Governance isn’t optional when outputs touch customers or compliance regimes.