💻 Aire Solve Strategies
🔍 About us
Aire Solve Strategies started in 2019 with a straightforward idea: most companies know they need AI, but they have no idea where to begin. We fill that gap. Our team of six engineers and two data scientists works directly with your staff to identify where machine learning can save time, reduce errors, or open new revenue channels.
We are based in South Douglas, England, but most of our project work happens remotely. Clients range from logistics firms with 50 employees to e-commerce platforms processing 200,000 orders a month. Each engagement starts with a two-week diagnostic, not a sales pitch.
Our models run in production. That matters because plenty of consultancies deliver a proof-of-concept notebook and walk away. We stick around for deployment, monitoring, and retraining cycles.
🔧 Our services
Every project below includes documentation, staff training sessions (typically two half-day workshops), and 90 days of post-launch support.
Predictive analytics
We build regression and classification models on your historical data. Common use cases: demand forecasting for retail, churn prediction for subscription services, and equipment failure alerts for manufacturing lines. Typical model accuracy targets sit between 85% and 93%, depending on data quality.
Computer vision
Object detection, image classification, and visual inspection systems. We have deployed defect-detection pipelines for a packaging company that reduced manual inspection time by 70%. Models run on edge devices or cloud endpoints, your choice.
Natural language processing
Chatbots that answer real questions, document summarisation engines, sentiment analysis on customer reviews. We fine-tune large language models on your domain-specific corpus so responses are accurate, not generic. Typical fine-tuning cycles take three to five weeks.
Data pipeline engineering
Raw data sitting in spreadsheets and siloed databases is useless to an AI model. We design ETL pipelines that clean, normalise, and version your data automatically. Most pipelines run on Apache Airflow or Prefect, scheduled to refresh every hour or on-demand via API trigger.
AI strategy consulting
Not ready to build yet? Our two-week diagnostic audit maps your existing workflows, identifies the three highest-impact automation opportunities, and produces a costed roadmap. You get a 30-page report and a prioritised backlog, not a slide deck full of platitudes.
Model monitoring and retraining
Models decay. Customer behaviour shifts, product catalogues change, seasonal patterns drift. We set up automated monitoring dashboards that flag accuracy drops and trigger retraining jobs. Most clients see a retraining cycle every six to ten weeks.
📋 How we work
Here is the typical lifecycle of a project with us, from first call to production deployment:
- Discovery call — 30 minutes on the phone. You describe the problem, we ask about your data sources, team size, and timeline expectations. Free, no obligation.
- Diagnostic audit — Two weeks. We review your databases, interview stakeholders, and map current workflows. Deliverable: a prioritised opportunity report with cost estimates.
- Prototype sprint — Four to six weeks. We build a working proof-of-concept on a subset of your data. You see real outputs, not mockups.
- Production build — Six to twelve weeks, depending on complexity. The model is containerised, tested under load, integrated with your existing systems via REST API or batch pipeline.
- Training and handover — Two half-day workshops for your team. We cover how the model works, how to interpret its outputs, and when to escalate anomalies to us.
- Ongoing support — 90 days included. After that, optional retainer plans start at 8 hours per month for monitoring and maintenance.
💬 What clients say
"They built a demand forecasting model for our warehouse in under five weeks. We reduced overstock by 22% in the first quarter after deployment."
— J. Hargreaves, operations director, Midlands logistics firm
"The NLP chatbot they fine-tuned handles about 60% of our tier-one support tickets without human intervention now. Response time went from 4 hours to 11 seconds."
— S. Okonkwo, CTO, e-commerce platform
"Honest about what AI can and cannot do. They talked us out of one idea that would have wasted budget and redirected us to a simpler solution that actually worked."
— R. Patel, founder, fintech startup