Booking platformALM MOT Center
Online MOT booking and workshop management for a DVSA-approved garage in Bathgate, Scotland.
- Next.js
- TypeScript
- PostgreSQL
ServicesAI Tools & Workflows
Core practiceAI Tools & Workflows
Custom AI products and automations, from image generation to agents.
If you can describe the outcome, we can usually build the tool. We design and ship custom AI products: image and content generation tools, chat assistants grounded in your data (RAG), autonomous agents that take actions, and workflow automations that connect the apps your team already uses. Built on the latest models, wired to your systems, and shipped as a product people actually use.
What you get
- Image, video, and content generation tools
- Chat assistants grounded in your data (RAG)
- Autonomous agents that take real actions
- Workflow automation across your stack
- LLM integrations, evals, and guardrails
The three shapes this work usually takes
The first is a tool with a screen: something your team or your customers open to generate, summarise, extract, or rewrite. The second is an assistant that answers questions from your own documents, which means retrieval, not fine-tuning. The third is an automation with no interface at all, triggered by an email arriving or a row changing, that quietly does a job nobody enjoys.
The shape matters because it changes the cost. A screen means design and front-end work. A document assistant means getting your content into a searchable state, which is usually the largest single task. An automation is often the cheapest and the highest return.
Grounding answers in your own data
Asking a model about your business without giving it your business is how you get confident nonsense. We build retrieval: your documents are split, indexed, and searched at question time, and the model is given the relevant passages plus an instruction to answer only from them and to say when it cannot.
The unglamorous part is source quality. If your policies live in six conflicting versions across three drives, the assistant will faithfully reproduce the confusion. We tell you when the real fix is tidying the source rather than building anything.
Knowing whether it works
Anything built on a language model needs a way to measure output, or you are shipping on vibes. We build a set of test cases from real examples with the answers you would accept, and run them whenever a prompt or model changes. It turns 'it feels worse today' into something you can check.
We also add guardrails: limits on what the tool will discuss, checks on the shape of the output before it is used, and a log of every request so you can see what was asked and what came back when someone questions a result.
Picking the model, and changing it later
Model choice is a cost and latency decision more than a quality one for most tasks. We benchmark two or three against your own test cases and pick the cheapest that passes, then keep the integration behind an interface so switching later is a configuration change rather than a rebuild.
This matters because the field moves. Anything hardwired to one provider's quirks is expensive to move a year later, and something better or cheaper will exist by then.
Related work
Recent builds.
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- Next.js
- TypeScript
- PostgreSQL
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- Flutter
- Dart
- Node.js
FAQ
AI Tools & Workflows: common questions
Not on the enterprise and API tiers we build against, where providers contractually exclude your data from training. We confirm the specific terms for whichever provider you choose during scoping, and we can run open models on your own infrastructure if your policy requires it.
Have a workflow worth automating?
Book a 30 minute call. We will map where AI voice, automation, or a new app can save your team real hours, and what it takes to ship it.