17AI solutions & automation
Sydon Tech builds AI into the software a business already runs — assistants answering from your own records, documents read into structured data, and repetitive workflows automated with a person still in control of what matters.
Sydon Tech builds AI into business software — assistants that answer questions from a company’s own records, document and data extraction, and automation of repetitive workflows — integrated with the systems a business already runs.
The useful question is not whether to “adopt AI” but which specific task is repetitive enough, high-volume enough and light enough on judgement to hand over — and whether the data that task depends on is actually being recorded. Those two checks decide whether an AI project returns anything.
Six applications we build. Each solves a named business problem rather than demonstrating a capability.
An assistant that answers questions against your documents and business data — stock positions, order history, policy documents — instead of from the public internet.
Supplier invoices, delivery notes and purchase orders read automatically into structured fields, with a human confirming what the model was unsure about.
The steps a person currently does by copying between screens: classifying, routing, summarising and drafting the routine response.
Asking a question in plain language and getting the figure, for managers who will never open a report builder.
Drafting replies from your own product and policy documents so an agent edits rather than writes, and repeat questions stop consuming the day.
The useful place for AI is usually inside the screen someone already works in, not a separate chat window nobody opens.
Deliberately front-loaded with checks, because the expensive failure in AI work is building something accurate enough to demo and not accurate enough to trust.
We look for work that is repetitive, high-volume and judgement-light. If a task is none of those, AI will cost more than it saves and we will say so.
AI answers from data. Before anything is quoted we confirm the records are actually there, in a form a system can read — this is where most AI projects quietly fail.
A small working version against a real sample, measured for accuracy. You see the failure cases before committing to a build, not after.
The model proposes and a person confirms, wherever a wrong answer would cost money. Confidence thresholds decide what needs review.
Delivered inside the system people already use, so the automation is in the path of the work rather than beside it.
Accuracy and time saved are tracked after go-live. Where a model underperforms, prompts, retrieval or the human-review threshold are adjusted.
The fastest way to waste money on AI is to apply it to a process that is not defined or not recorded.
We would rather lose an AI project than deliver one that quietly produces wrong numbers. The requirement study is free, and a fair share of them end with a recommendation that has nothing to do with AI.
What businesses ask before starting.
Work that is repetitive, high in volume and light on judgement: reading documents into structured data, classifying and routing incoming requests, summarising long records, drafting routine replies and answering factual questions from company data. Work requiring negotiation, accountability or genuine judgement is not a good candidate, and we will say so rather than sell it.
It can, which is why we build with a human in the loop wherever a wrong answer costs money. Answers are grounded in your own records rather than general knowledge, the system cites the record it used so a person can check, and low-confidence outputs are routed for review instead of being sent automatically.
Often the second. If the underlying process is undefined or the data is not being recorded, AI adds a layer on top of a problem rather than solving it. We check this during the requirement study, and a fair number of conversations end with us recommending a workflow or reporting change instead of an AI build.
That is a decision made explicitly at design time and written into the scope: which data the system may see, whether processing happens through a third-party model provider or on infrastructure you control, and what is retained. We do not send business records to an external service without that being agreed and documented.
Yes — that is usually the point. The value comes from AI reading and writing the records your business already keeps, so the work happens inside the system people use rather than in a separate tool. Where the existing system exposes an API or database we integrate with it directly.
It depends on the volume of data, how much integration is required and how accurate the result has to be before it can run unsupervised. We quote a fixed price against a written scope after the requirement study, and we quote the proof-of-concept separately so you can stop after it if the accuracy is not there.
Describe the work that repeats every day and we will tell you honestly whether AI is the right answer, what data it would need, and what it would cost to prove before you commit to a build.