Smallest viable model first
We start with the simplest model that could solve the problem — often logistic regression or a decision tree. If that works, we ship it. Complex architectures come later, only if the data justifies them.
Artificial Intelligence glossary
Before starting any AI project, teams need a shared vocabulary. We built this glossary from real conversations with clients who were confused by vendor jargon. Each definition is written for business decision-makers, not computer scientists.
- Machine learning
- A subset of AI where software improves its predictions by analysing historical data rather than following hand-coded rules. Most business applications today — from demand forecasting to spam filtering — rely on machine learning models trained on company-specific datasets.
- Natural language processing (NLP)
- The branch of AI that handles text and speech. Chatbots, document summarisation, sentiment analysis of customer reviews — all NLP. Quality depends heavily on the training corpus and how well it reflects your customers' actual language.
- Computer vision
- Algorithms that interpret images and video. Practical uses include automated quality inspection on production lines, medical image screening, and retail shelf-monitoring. Accuracy improves dramatically when models are fine-tuned on images from your own environment.
- Large language model (LLM)
- A neural network trained on enormous text datasets. GPT-4, Claude and Gemini are examples. They generate fluent text but can hallucinate facts. Deploying an LLM in production requires guardrails, prompt engineering and ongoing monitoring.
- Retrieval-augmented generation (RAG)
- A pattern that feeds an LLM verified documents at query time so it answers from your knowledge base instead of guessing. RAG reduces hallucinations and keeps responses grounded in approved content — ideal for internal help desks and customer support.
- MLOps
- The discipline of deploying, monitoring and retraining machine learning models in production. Without MLOps, a model that worked in a Jupyter notebook quietly degrades after a few months because the real-world data drifts away from the training set.
- Edge AI
- Running inference directly on devices — phones, cameras, factory sensors — rather than sending data to the cloud. Reduces latency and bandwidth costs. Relevant when milliseconds matter, such as autonomous vehicle perception or real-time defect detection.
"We handed this glossary to our board before our first AI steering meeting. It saved an hour of confused back-and-forth." — Director of digital transformation, logistics firm, Belfast
Capability map
Not every AI technique fits every problem. The table below maps business objectives to the AI capabilities we deliver, along with a maturity tag so you can gauge where each sits on the adoption curve.
| Business objective | AI capability | Typical timeline | Maturity |
|---|---|---|---|
| Reduce manual document review | NLP extraction & classification | 6–10 weeks | Core |
| Forecast demand or churn | Supervised ML (tabular) | 4–8 weeks | Core |
| Automate visual quality checks | Computer vision | 8–14 weeks | Core |
| Internal knowledge assistant | RAG + LLM orchestration | 6–12 weeks | Advanced |
| Personalised customer interactions | Recommendation engine | 8–12 weeks | Advanced |
| Real-time anomaly detection | Streaming ML + edge inference | 10–16 weeks | Advanced |
| Generative content at scale | Fine-tuned LLM + guardrails | 8–14 weeks | Research-grade |
| Autonomous decision loops | Reinforcement learning | 16–24 weeks | Research-grade |
Timelines assume a prepared dataset and an available product owner on your side. Discovery phase (2–3 weeks) is additional.
Use-case scenarios
Abstract AI talk helps nobody. Here are three scenarios we encounter regularly, described in enough detail that you can judge whether they resemble your own situation.
Scenario A: drowning in invoices
A mid-sized distributor processes 4,000 supplier invoices per month. Staff spend roughly 12 minutes per invoice matching line items to purchase orders. Errors average 6%.
We deploy an NLP extraction pipeline that reads PDF invoices, maps fields to PO data, and flags mismatches for human review. Processing time drops to under 90 seconds per invoice. Error rate falls below 1.5% within the first quarter.
Scenario B: the knowledge silo problem
A 200-person engineering consultancy has 15 years of project reports scattered across SharePoint, email archives and local drives. New hires take months to find relevant precedents.
We build a RAG-powered assistant that indexes those documents, respects access permissions, and answers natural-language questions with cited sources. The assistant handles roughly 70% of internal queries without escalation to senior staff.
Scenario C: visual defect detection on a production line
A food packaging plant relies on two human inspectors per shift to spot seal defects. They catch roughly 92% of faults, but each missed defect risks a product recall.
We install edge-AI cameras that classify every package at line speed. Detection rate reaches 99.3% in validation. The inspectors shift to oversight roles, reviewing only flagged items.
"The invoice automation alone freed up two full-time equivalents. We redeployed them into supplier relationship work that actually needs human judgement." — Finance manager, wholesale distributor, Derry
Readiness diagnostic
Most AI projects fail not because of bad algorithms but because the organisation wasn't ready. Answer these five questions honestly. If you tick fewer than three, a strategy sprint with us will close the gaps before you spend money on models.
- Do you have at least 6 months of structured, digital data relevant to the problem you want to solve?
- Is there an identified business owner who will use the AI output in a daily decision?
- Can your IT team provision a cloud environment or on-premise GPU within two weeks?
- Have you defined a measurable success metric (e.g. "reduce processing time by 40%") rather than a vague goal?
- Does leadership accept that the first model will need iteration and won't be perfect on day one?
Fewer than three ticks? That's fine. We run a two-week readiness sprint that maps your data landscape, identifies quick wins, and produces a prioritised roadmap. The sprint costs a fixed fee and the roadmap is yours to keep regardless of whether you proceed with us.
Decision framework
When a client asks "should we use AI for this?", we walk through a structured decision tree. Below is a simplified version you can apply internally before engaging any vendor.
Step 1 — Is the task repetitive and rule-describable?
If yes, traditional automation (RPA, scripted workflows) may be cheaper and more reliable than machine learning. AI shines when the rules are too complex to write explicitly or when patterns shift over time.
Step 2 — Do you have labelled examples?
Supervised learning needs examples of correct answers. If you have thousands of invoices already matched to POs, that's gold. If you have no labelled data, budget for an annotation phase or explore unsupervised approaches like clustering.
Step 3 — What happens when the model is wrong?
A wrong product recommendation is mildly annoying. A wrong medical diagnosis is dangerous. The cost of errors determines how much human oversight you need in the loop and how rigorous your testing must be.
Step 4 — Can you measure improvement?
Define a baseline now. Measure the current speed, accuracy, or cost of the process. Without a baseline you'll never know whether the AI actually helped or just looked impressive in a demo.
We've talked three prospective clients out of AI projects because the decision framework showed traditional automation was the better fit. They saved tens of thousands of pounds and still got the outcome they wanted.
— Technical lead, AI Wisepath
Case narrative
Regional insurer cuts claims triage time by 58%
A Northern Ireland–based insurer handled 1,200 motor claims per month. Each claim required a human adjuster to read the description, classify severity, and route it to the correct team. Average triage time: 22 minutes.
We trained an NLP classifier on 18 months of historical claims data. The model reads incoming claim text, assigns a severity score, and routes it automatically. Adjusters now review only edge cases — claims where the model's confidence is below 85%.
Triage time dropped to 9 minutes on average. The insurer redeployed two adjusters to complex fraud investigation, a role that had been understaffed for years. The model has been in production for 14 months and is retrained quarterly on fresh data to prevent drift.
"The AI doesn't replace our adjusters. It removes the boring bit so they can focus on the claims that actually need human experience." — Operations director, regional insurer
Our methods
We follow a handful of principles that keep projects grounded. These aren't marketing slogans; they're the operational rules our engineers and data scientists work by every day.
Weekly demo cadence
Every Friday afternoon we show you what the model can do right now, on your data. No slide decks. You see inputs, outputs and error cases in a live environment. Feedback goes straight into the next sprint.
You own the model
We hand over all code, training data pipelines, and deployment scripts. No vendor lock-in. If you want to bring the project in-house after launch, we'll train your team during a structured handover period.
Monitoring from day one
Every deployed model ships with drift detection, latency tracking and automated alerts. When performance drops below your agreed threshold, the system flags it before your users notice.
Start a conversation
Tell us what you're trying to achieve. We'll respond within one working day with an honest assessment — including whether AI is actually the right approach.
Or contact us directly: 01903 69633 · [email protected]
4 Dietrich Copse, Old Mannford, Northern Ireland, DK74 4WC, United Kingdom
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By using this website you agree to these terms. The content on aiwisepath.best is provided for general informational purposes. While we strive for accuracy, we make no guarantee that glossary definitions, timelines or capability descriptions are complete or error-free. Engagement for paid services is governed by a separate statement of work signed by both parties. Intellectual property created during a project is assigned to the client upon full payment, as specified in the relevant statement of work. AI Wisepath reserves the right to update these terms; continued use after changes constitutes acceptance. These terms are governed by the laws of Northern Ireland.
Disclaimer
The scenarios, case narratives and performance figures on this site reflect specific client engagements under specific conditions. Your results will vary depending on data quality, organisational readiness and project scope. Nothing on this website constitutes a guarantee of outcomes. The glossary is educational and should not be treated as a substitute for professional technical advice tailored to your situation. AI Wisepath accepts no liability for decisions made on the basis of information published here.