Horizon LabsHorizon Labs
Our Process

How We Build Product

From first conversation to production — a structured, transparent process designed around your business. No black boxes, no surprises.

Four stages. Full transparency.

01

AI Discovery

We map your workflows, data assets, and business goals to find where AI creates the highest value. Feasibility testing on your actual data — not theoretical potential.

  • Map business processes to AI automation potential
  • Audit data quality and availability
  • Test feasibility on priority use cases
  • Deliver costed AI roadmap with ROI projections
02

AI Design

We design the AI architecture, UX, and evaluation criteria together. Prototypes use real model outputs so you see exactly what users will experience.

  • Model selection and architecture design
  • AI-native UX with human-in-the-loop flows
  • Define evaluation metrics and success criteria
  • Prototype with real AI outputs, not mockups
03

Build & Evaluate

Two-week sprints. Working AI features every fortnight. Every model ships with evaluation suites, monitoring, and guardrails — production-grade from day one.

  • Iterative model development and fine-tuning
  • Automated evaluation on every change
  • Integration with your existing systems
  • Sprint demos with real AI performance metrics
04

Operate & Improve

AI doesn’t end at launch. We monitor accuracy, detect drift, retrain models, optimise costs, and continuously improve performance.

  • Production monitoring and drift detection
  • Automated retraining pipelines
  • Cost optimisation and model routing
  • Guardrail management and compliance

What you get with Horizon Labs

Every engagement includes the people, processes, and transparency you need to build with confidence.

AI Engineering Depth

Custom models, fine-tuning, RAG, agents, computer vision, and MLOps — not just API wrappers.

Product Strategy Included

AI product management, business analysis, and QA built into every engagement.

Evaluation-Driven

Automated eval suites test accuracy, latency, cost, and safety on every model change.

Transparent Costs

Sprint-level budget tracking. Model inference costs projected upfront. No surprise AI bills.

Model-Agnostic

Claude, GPT-4, Gemini, open-source — we recommend the right model, not just our favourite.

Experienced Team

Engineers and designers who work directly on your project — no layers, no handoffs.

Flexible methodology

We choose the development approach that fits your project — not the other way around.

AI-First Build

Discovery, model development, and product engineering run in parallel. Fastest path to production AI.

Best for: New AI products and features

AI Integration

Embed AI capabilities into your existing products via APIs, agents, and intelligent features.

Best for: Adding AI to existing platforms

AI Rescue

Take over AI projects that stalled, drifted, or never made it to production. Audit, fix, and operationalise.

Best for: Failed or stalled AI initiatives

AI Operations

Our team monitors, retrains, and optimises your AI systems on an ongoing basis.

Best for: Production AI that needs ongoing care

Your team

The team scales with your project. A typical sub-team includes:

2–3
AI Engineers
1
Product Strategist
1
AI/UX Designer
1
ML Ops Engineer
1
QA + Eval

Team composition flexes based on the project. We bring in specialists as needed.

Ready to build AI that works?

Tell us about your AI challenge and we’ll give you an honest assessment of what’s feasible, what it will take, and where to start.

Get in Touch

Ready to talk about your project?

First conversation is free, no obligations. Tell us what you need and we'll tell you honestly what we can help with.

Let's Talk