Service directory
Click any service name to expand its detail card below the table.
| Service | Category | Typical timeline | Status |
|---|---|---|---|
| Natural language processing | Text analysisAutomation | 4–8 weeks | Available |
| Computer vision systems | Image recognitionQuality control | 6–12 weeks | Available |
| Predictive modelling | ForecastingRisk | 3–6 weeks | Available |
| Conversational AI | ChatbotsSupport | 2–5 weeks | Available |
| Data pipeline engineering | InfrastructureETL | 4–10 weeks | Available |
| Document automation | OCRExtraction | 3–7 weeks | Available |
| Anomaly detection | SecurityMonitoring | 5–9 weeks | Waitlist |
| Recommendation engines | E-commercePersonalisation | 4–8 weeks | Available |
| Sentiment analysis | Social listeningBrand | 2–4 weeks | Available |
| Generative AI integration | ContentLLM | 3–8 weeks | Available |
| MLOps and model governance | OpsCompliance | 6–14 weeks | Waitlist |
Natural language processing
We build custom NLP pipelines that classify, summarise, and extract entities from your text data. Typical use cases include automated ticket routing for support teams, contract clause extraction for legal departments, and multilingual content tagging for publishers. Each pipeline ships with an evaluation dashboard so you can track precision and recall as your data changes.
Computer vision systems
From defect detection on production lines to shelf-stock monitoring in retail, our computer vision work centres on practical accuracy rather than flashy demos. We train models on your own imagery, validate them against edge cases you care about, and deploy via containerised inference endpoints that run on-premise or in any major cloud.
Predictive modelling
We help you forecast demand, churn, equipment failure, or financial risk using gradient-boosted trees, time-series models, and survival analysis. The output is a scored dataset or API endpoint your team can plug into dashboards and decision workflows. Past projects have reduced inventory waste by 18% for a Scottish food distributor and flagged 73% of at-risk accounts two months before cancellation for a SaaS firm.
Conversational AI
We design and build chatbots and voice assistants grounded in your knowledge base. Unlike off-the-shelf tools, our bots are retrieval-augmented: they search your documentation before answering, which cuts hallucination rates significantly. Deployments include web widgets, Microsoft Teams, WhatsApp, and custom mobile apps.
Data pipeline engineering
Good AI depends on clean, timely data. We architect ingestion, transformation, and storage pipelines using tools like Apache Airflow, dbt, and Snowflake. The goal is a reliable, tested data flow that your analysts and models can trust without midnight firefighting.
Document automation
Invoices, purchase orders, medical letters, planning applications: we train OCR and layout-aware models to extract structured fields from messy scans and PDFs. Accuracy targets are agreed up front, and we build human-in-the-loop review screens for the cases the model flags as uncertain.
Anomaly detection
Real-time or batch anomaly detection for network traffic, financial transactions, or sensor telemetry. We combine statistical baselines with isolation-forest and autoencoder models, tuned to your acceptable false-positive rate. Currently on a short waitlist due to demand; reach out to reserve a slot.
Recommendation engines
We build collaborative-filtering and content-based recommenders for product catalogues, media libraries, and learning platforms. Each engine is A/B-testable from day one so you can measure incremental revenue or engagement lift before scaling.
Sentiment analysis
Track how customers talk about your brand across reviews, social media, and survey responses. Our models go beyond positive/negative to detect specific emotions and intent signals, delivered as a live dashboard or weekly digest report.
Generative AI integration
We integrate large language models into your existing products and workflows. This includes prompt engineering, fine-tuning on your domain data, guardrail design to prevent off-topic or harmful outputs, and cost optimisation so your token spend stays predictable as usage grows.
MLOps and model governance
If you already have models in production but struggle with versioning, drift monitoring, or audit trails, this service is for you. We set up CI/CD for model retraining, automated performance alerts, and governance documentation that satisfies internal risk teams and external regulators. Currently waitlisted; contact us for estimated availability.
Is AI the right move for you?
Good signals
- You have at least six months of structured data you already collect
- A manual process is consuming more than 20 hours per week
- Your team can articulate what a correct answer looks like
- Leadership has allocated budget for a pilot, not just exploration
Caution signs
- Data lives in disconnected spreadsheets with no shared identifiers
- The problem definition changes every meeting
- There is no internal owner who will use the model's output
What we suggest first
Book a 30-minute diagnostic call. We will ask about your data, your team, and the decision you want the model to support. If AI is premature, we will tell you plainly and suggest what to fix first. No charge for that call.
How we work differently
Most AI consultancies start with a capabilities presentation. We start with your data. Before writing a single line of code, we run a two-day data audit: schema review, quality profiling, gap analysis. That audit produces a written report you keep regardless of whether you proceed with us.
If the data supports a viable model, we scope a fixed-price pilot. Pilots are capped at eight weeks. You get a working prototype, a performance report, and a clear recommendation on whether to scale, iterate, or stop. Roughly one in five pilots ends with a "stop" recommendation. We would rather save you from a bad investment than chase a longer contract.
When a pilot succeeds, we move to production deployment with weekly check-ins. Handover documentation is written for your team, not for ours. After go-live, we offer a three-month support window included in the project fee.
From first call to production
Diagnostic call
30 minutes, no charge. We learn about your problem, data landscape, and internal capacity. You leave with an honest assessment of feasibility.
Data audit
Two-day on-site or remote review. We profile your data quality, identify gaps, and produce a written findings report.
Pilot build
Fixed scope, fixed price, maximum eight weeks. You see a working model and its measured performance on your own data.
Go / no-go decision
We present results honestly. If the model does not meet the agreed threshold, we recommend stopping. No hard feelings, no upsell.
Production and handover
Deployment, monitoring setup, and documentation written for your engineers. Three months of included support after launch.
What clients have said
A note on pricing
We do not publish fixed prices because every project depends on data volume, model complexity, and integration depth. What we can share: diagnostic calls are free, data audits start at £1,800, and pilot projects typically range from £8,000 to £25,000 depending on scope.
We quote fixed prices, not day rates. You will know the total cost before signing. If the scope changes mid-project because of something we missed, we absorb the difference.
Request a diagnostic call
Tell us a little about your situation. We will reply within one working day to arrange a 30-minute call.
Office
5 Rempel Corner, Schuppe Park, Scotland, YE8 4KF, United Kingdom
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The service descriptions, timelines, and example outcomes on this website are illustrative. Actual results depend on your data quality, organisational readiness, and project scope. Past client outcomes described on this page are factual to the best of our knowledge but should not be taken as a guarantee of future performance.
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