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AISOFTAGENCY
AI & Automation

Recommendation Engines

Personalised product, content and next-best-action recommendations that lift engagement and revenue.

  • 6–10 weeks
Overview

We build recommendation systems that combine behavioural signals, product data and semantic similarity to show each user what they are most likely to want next.

Recommendations are tested against control groups so you can see the measurable lift in conversion and order value.

What's included

  • Behavioural and content-based models
  • Semantic similarity with embeddings
  • Real-time personalisation
  • A/B testing against baselines
  • Merchandising controls

Deliverables

  1. 01Recommendation service
  2. 02Integration into your product
  3. 03Lift analysis

Technologies

  • Redis
  • Python
  • pgvector
Process

AI delivery process

A de-risked path from AI idea to a measured, production-grade system.

  1. 011 week

    Discovery

    We map the workflow, the decisions inside it and the business case, so AI is applied where it pays back.

    • Use-case map
    • ROI model
    • Success metrics
  2. 021–3 weeks

    Data

    We audit, clean and structure the knowledge, documents and system data the model will rely on — securely.

    • Data audit
    • Knowledge base
    • Access & privacy plan
  3. 032–3 weeks

    Prototype

    A working prototype on your real data within weeks, tested with the people who will actually use it.

    • Working prototype
    • Prompt & retrieval design
    • User feedback
  4. 042–6 weeks

    Integration

    We connect the AI to your CRM, help desk, ERP or internal tools with the guardrails production needs.

    • API integrations
    • Human hand-off flows
    • Guardrails
  5. 051–2 weeks

    Evaluation

    Automated evaluations and human review measure accuracy, safety, latency and cost before go-live.

    • Evaluation suite
    • Accuracy report
    • Cost projections
  6. 06Ongoing

    Production

    Launch with monitoring, analytics and continuous improvement as your data and needs evolve.

    • Monitoring
    • Usage analytics
    • Improvement roadmap
FAQ

Common questions

Is our data safe when using AI models?

Yes. We use enterprise API agreements that exclude your data from model training, encrypt data in transit and at rest, apply role-based access and can deploy private or regional models when compliance requires it.

How do you make sure the AI gives accurate answers?

We ground responses in your approved knowledge with retrieval and citations, test against evaluation sets built from real questions, set confidence thresholds and route uncertain cases to people. Accuracy is monitored continuously after launch.

Which AI models do you work with?

We're model-agnostic. We regularly build with OpenAI, Claude and Gemini as well as open-source models, and select the best option per task based on quality, speed, cost and data requirements.

Let's build what's next

Have a project in mind?

Tell us where you want to go. We'll map the fastest route there — design, engineering, growth and AI included.