Basic AI Implementations
Not every AI project needs to be a platform. The fastest return usually comes from a few narrow tools that take one repetitive task out of someone's week.
Start with the task, not the technology
Most stalled AI projects start from the wrong end. Someone decides the company should be using AI, and then goes looking for somewhere to put it. The implementations that survive run the other way round: they begin with a specific piece of repetitive work that someone can name, and they finish when that work takes less time.
That work is usually unglamorous. Writing the fiftieth description for a phone mount that differs from the previous one by cable length. Answering the same three pre-sales questions in slightly different words. Copying numbers out of a spreadsheet into a summary somebody reads for thirty seconds. None of it is difficult, all of it is time, and none of it needs a platform to fix.
What a first implementation usually looks like
The pattern is narrow scope, one clear owner, and output a person checks before it goes anywhere.
Descriptions at volume. The assistant works from your attributes and spec data, produces a draft in your house style, and leaves you the verification step.
Repeat correspondence. Common questions get a drafted answer in the inbox rather than a sent one, so the person keeps the final word.
Knowledge that lives in people's heads. Procedures, past decisions and internal notes indexed and searchable in natural language.
Recurring analysis. The numbers were never the slow part. Writing up what they mean was.
Content groundwork. Plans, outlines and drafts that remove the blank page without pretending to replace an editor.
Verification is part of the design
A basic implementation does not remove the person. It moves them from producing to checking, which is a much faster job. The mistake is treating that check as a matter of discipline rather than something the tool enforces.
So we build the review step in. Drafts land where a human already works instead of publishing themselves. Output that touches customers goes out on someone's approval. And where the model could invent something costly, we constrain it: a description generator that works from your spec sheet cannot invent a warranty term, because it has your warranty terms in front of it and nothing else to draw on.
Buy where it fits, build where it does not
Part of our job here is telling you when the answer is a subscription and half a day of configuration. For an internal knowledge base, a tool like NotebookLM may cover it. For occasional drafting, a well-written prompt in an existing assistant is often the whole solution, and building anything custom would be waste.
Custom work earns its place when the tool needs your systems, not just your text. Generating four hundred descriptions straight from your ERP, writing them back to the shop, and keeping the whole run consistent is not something you do by hand in a chat window. The dividing line is volume and integration, not sophistication.
Where it goes from here
The real value of a small first implementation is what it teaches you about your own organisation. Some teams pick a tool up immediately and start asking for the next one. Others use it twice and quietly return to the old way, which usually says something about the workflow around the tool rather than the model inside it.
Either answer is worth having, and it costs far less to find out this way than by committing to a year-long programme first. Once one narrow tool is genuinely in daily use, the next decision (a configurable no-code workflow, a full automation, eventually an agent) is grounded in something you have seen rather than something you were promised.
What you get
Product description assistant
Generates descriptions from your spec sheet and catalogue data rather than from what the model remembers about the category. You verify instead of writing from scratch.
Repeat email drafting
Drafts replies for the questions that arrive several times a week, in your tone and with your standard wording. The draft lands in the inbox; a person still sends it.
Internal knowledge search
Your procedures, notes and past decisions indexed so people can ask a question instead of finding the colleague who remembers. Ends the recurring "how did we do this last time?".
Analytical support
You supply the data, the assistant summarises it and points at what changed. Useful for recurring reports where the numbers are easy and the write-up is the slow part.
Content and social media support
Publication plans, first drafts and variations for channels you already run. It removes the blank page, not the editorial judgement.
Prompts, templates and usage rules
The working prompts written down, plus clear rules on what must not go into a model and what always needs review before it leaves the company.
How we work
- 01
Find the repetitive work
We sit with the people doing it and list the tasks that recur weekly. The candidates are the ones they can describe in one sentence and would rather not do.
- 02
Pick one task and define good
Before building anything we agree what an acceptable output looks like, with real examples of both good and unacceptable results to test against.
- 03
Build it around your material
The tool is connected to your catalogue, documents or past replies, so its output starts from your facts rather than a plausible-sounding general answer.
- 04
Hand it to the people who do the work
A short walkthrough, written prompts and a review step built into the flow. Adoption fails far more often than the technology does.
- 05
Check after a few weeks
We look at whether it is actually being used and what people quietly work around. That tells you whether to extend it or leave it alone.
Tools and technology
Where a solid open-source tool exists, we choose it over a closed one. No lock-in to a single vendor, and costs you can actually predict.
- Vercel AI SDK
- Ollama
- Open WebUI
- n8n
- OpenAI
- Anthropic Claude
- Google Gemini
- NotebookLM
- Microsoft Copilot
- Google Workspace
- Notion
- Make
Frequently asked questions
More services in this category
No-Code and Low-Code AI
We design and build the logic; you keep the ability to change it. Workflows you can open, read and adjust yourself, without waiting in a development queue.
Process Automation
If someone does the same thing the same way for the third time, it probably should not be manual. We automate the repeatable part and leave the judgement where it belongs.
AI Agents
An agent decides its own steps instead of following a fixed script. That makes it useful for work that varies, and it makes the permissions around it the most important part of the build.
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