Deployment registry
Each model below is actively serving requests through our managed inference layer. We handle container orchestration, auto-scaling, and version rollback so your team can focus on the product, not the plumbing.
| Model | Type | Status | Uptime |
|---|---|---|---|
| Receipt parser v3 | NLP extraction | Live | 99.97% |
| Demand forecast Q2 | Time-series | Live | 99.91% |
| Product recommender | Collaborative filter | Warm-up | 98.4% |
| Defect classifier | Computer vision | Live | 99.99% |
| Churn predictor | Gradient boost | Staging | — |
Recent outcomes
Results from the past 90 days across three client engagements. These are measured figures, not projections.
- Receipt parser cut manual data entry by 72% for a logistics firm handling 11,000 invoices per month.
- Demand forecast reduced overstock costs by £41k in a single quarter for a mid-size retailer.
- Defect classifier catches surface flaws at 0.3% false-positive rate on a production line running 16 hours a day.
Capability map
We organise our work into four operational layers. Every engagement touches at least two. The layers below are not marketing categories; they map directly to how we staff projects, price work, and measure delivery.
Data engineering
We build and maintain the pipelines that feed your models. That includes ingestion from APIs, databases, flat files, and streaming sources. We use Airflow, dbt, and custom connectors depending on what your stack already looks like. Typical pipeline build takes two to four weeks from schema mapping to production monitoring.
Model development
Training, evaluation, and iteration. We work with tabular data, text, images, and time-series. Most projects start with a baseline model inside the first week, then we iterate based on domain feedback. We track experiments in MLflow and version datasets alongside code.
Inference and serving
Deploying a model is where most teams stall. We containerise models, set up auto-scaling behind a load balancer, wire up monitoring for drift and latency, and handle rollback when a new version underperforms. Our median time from trained model to live endpoint is five working days.
Monitoring and retraining
Models degrade. Input distributions shift. We run weekly drift checks, trigger retraining when accuracy drops below agreed thresholds, and keep a human in the loop for edge cases. You get a Slack or email alert when something needs attention, along with a plain-language explanation of what changed.
Operations log
- 14:32 Retraining job completed for demand forecast model. Accuracy improved from 0.87 to 0.89 on holdout set.
- 13:15 Pipeline #7 ingested 42,000 new records from client warehouse. No schema violations detected.
- 11:48 Product recommender moved from staging to warm-up. Expected live by Thursday.
- 09:22 Drift alert cleared for receipt parser. Input distribution returned to baseline after upstream data fix.
- 08:05 Scheduled maintenance window closed. All endpoints back to full capacity.
How we work with you
We do not sell AI as a product you install and forget. Every engagement starts with a scoping call where we map your data landscape, identify the highest-value use case, and agree on a measurable success metric before writing any code.
From there, work happens in two-week sprints. You see working outputs at the end of each sprint, not a slide deck. If a use case turns out to be unviable after the first sprint, we say so and redirect effort. We have walked away from three projects this year because the data could not support the goal, and we refunded the unused portion of the retainer in each case.
Our team sits in Scotland. We are available during UK business hours and respond to urgent production issues within 30 minutes outside those hours.
"They told us our initial idea for a chatbot was not worth building given our data volume. Instead they proposed a simpler classification model that solved the actual problem. Saved us months."— Operations director, Edinburgh-based fintech
"The drift monitoring alone justified the retainer. We had a model silently degrading for weeks before they came on board. Now we catch issues the same day."— CTO, UK e-commerce company
"Fast, honest, and they document everything. Our internal team picked up the codebase without a single handover meeting because the docs were that clear."— Head of data, Glasgow logistics firm
Readiness diagnostic
Before we quote a project, we run a lightweight diagnostic. It takes about a week and costs a flat £1,200. You get a written report covering these five areas, plus a recommendation on whether to proceed.
Data availability
Do you have enough labelled examples? Is the data accessible through an API or export? We check volume, freshness, and format compatibility. If labelling is needed, we estimate the effort and can manage it.
Infrastructure fit
Where will the model run? We assess whether your current cloud setup, on-prem servers, or edge devices can handle the compute load. We flag gaps and propose the cheapest viable option.
Integration surface
A model is useless if it cannot plug into your existing workflow. We map the integration points, whether that is an API call from your app, a batch job in your data warehouse, or a webhook to a third-party tool.
Regulatory exposure
If you handle personal data, health records, or financial information, we flag the compliance requirements early. We are not lawyers, but we know which questions to raise and when to bring legal counsel in.
ROI baseline
We estimate the expected return against the project cost. If the numbers do not work, we tell you before you spend anything on development. Roughly one in four diagnostics ends with a recommendation not to proceed.
What we do not do
We do not build general-purpose chatbots, generate marketing copy, or sell pre-built SaaS tools. Our work is custom AI engineering: we build, deploy, and maintain models that solve a specific problem inside your business.
We also do not take on projects where the expected return does not justify the investment. If a spreadsheet formula or a simple rule-based script would do the job, we will tell you that and point you in the right direction. Roughly 30% of initial inquiries end with us recommending a non-AI solution, and we consider that a good outcome for everyone.
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The operational metrics, model statuses, and log entries shown on this page are illustrative. They represent the type of work we do and the way we present it to clients, but they are not live production data.
Past results described in client quotes and outcome summaries do not guarantee future performance. Every AI project depends on the quality, volume, and structure of the data provided, as well as the specific business context. We scope each engagement individually and set expectations before work begins.
Genuine AI Pros is not liable for decisions made based on information presented on this website. For advice specific to your situation, please contact us directly.