AI & machine learning developmentFeatures that have to be
right, not impressive.
Retrieval, LLM features, classification and forecasting, each with an evaluation harness so you know when it got better and when it did not.
Eval harness first
A number before a demo.
Retrieval done properly
Chunking, ranking, citations.
Cost and latency
Budgeted, then measured.
Engagement
Data & AI · project or retained
What you get.
An evaluation harness before a demo: a number you can defend to your board
Retrieval done properly: chunking, ranking, citations, and the cases where it should say nothing
Cost and latency budgeted up front, then measured in production
Monitoring, retraining and rollout owned end to end, not handed to you as a notebook
Engagement: Data & AI · project or retained · Ask about your AI feature →
Good for
— An AI feature that has to be right, not impressive
— Search or support that should answer from your data
— Forecasts the finance team will actually use
We will say no when
— The goal is an AI feature rather than a problem the feature solves
— There is no data you are allowed to use, and no plan to get some
Stack · only what we have shipped to production
From the record.
All case notes →01 · Retail · AI
Data & AI
Personalisation and image-search engine
Problem — Recommendations and visual search that had to prove their return in the client’s numbers, not in a demo.
What we did — Recommendation and image-search models with evaluation before release, production monitoring, retraining and version rollout owned end to end.
Measured — Up to 261× client ROI. Shipped an image-search model into production.
Client withheld · details on the callAsk about this →
02 · E-commerce · AI
Data & AI
LLM and RAG assistant in a live customer journey
Problem — A UK deals platform had inbound support volume it could see in its tickets.
What we did — An assistant sourced from support-ticket analysis rather than a feature request, embedded in the live journey, with quality gates before release.
Measured — Reduced inbound support volume — [ADD THE NUMBER].
Client withheld · details on the callAsk about this →
03 · Agritech · computer vision
Data & AI
Detection from user-submitted photographs
Problem — Non-experts photographing in poor conditions; a model that had to act on what they sent.
What we did — Owned the labelled dataset strategy, the evaluation criteria that gated release, production monitoring and the retraining loop as field data arrived; capture guidance and failure handling treated as part of the product. Localised for low-literacy users.
Measured — 98% detection accuracy. Government incubator funding secured on the minimum viable build.
Client withheld · details on the callAsk about this →
04 · AI · delivery
Data & AI
Agentic software-delivery framework on Claude Code
Problem — An engineering practice wanted to deliver faster without losing review, tests and documentation along the way.
What we did — An end-to-end pipeline on Claude Code: project planning, automated epic and story population with progress tracking, autonomous story implementation, a dedicated solutions-architect agent, guardrail enforcement, automated code review and test generation, living documentation, and human-in-the-loop escalation pushed to mobile when a decision needs a person.
Measured — Rolled out to 10 engineers and now the practice’s default path for new work. Two Android apps scoped at five developers were shipped by one, on the original timeline.
Client withheld · details on the callAsk about this →
AI & machine learning · the call
Tell us what it must get right. We will say how to measure it.
Bring the problem, not a brief. If it is outside what we do well, we will say which of our contacts to call instead.
Or email info@hopsig.com — reply within one working day.