AI Implementation

What AI Implementation Covers

Most AI projects fail not because the model doesn’t work, but because nothing around it does — the integration is brittle, the evaluation is vibes-based, the costs are unpredictable, and no one knows what to do when the model is wrong. We build AI features that clear all four bars.

Use-Case Identification

We start by ranking candidate use cases by expected business impact, cost-to-serve, and technical feasibility. You get a shortlist of things AI should actually do — and a longer list of things it shouldn’t.

Model Selection & Integration

Frontier APIs (Claude, GPT), open-source models, or fine-tuned custom models — chosen based on accuracy requirements, latency budget, cost ceiling, and data-privacy constraints. Integrated with your data and operational systems so the output actually changes something.

Evaluation & Guardrails

Every AI feature ships with an eval suite, a cost budget, and fallback logic for when the model is unsure or wrong. No “it seemed to work in a demo”. Real metrics, real thresholds.

Key Deliverables

• AI implementation plan with prioritised use cases
• Integrated AI feature(s) live in production
• Evaluation suite and cost monitoring dashboard
• Technical documentation and handover package

Business Benefits

• Measurable efficiency gains on high-volume tasks
• New product features that weren’t economical without AI
• Fewer human errors on structured-but-messy workflows
• Predictable AI spend thanks to budgets and monitoring

How We Work

1. Discovery — Identify candidate use cases, baselines, and success metrics.
2. Prototype — Smallest viable experiment on real data.
3. Evaluate — Accuracy, latency, and cost measured against the baseline.
4. Productionise — Integrate, add guardrails and monitoring.
5. Iterate — Improve prompts/models/fine-tunes based on production signals.

Our Extremely Honest FAQ

Does our problem actually need AI?

Often the answer is “use an LLM for this narrow step, keep traditional code for everything else.” We’ll tell you when AI is the right tool and when it’s an expensive detour.

What about data privacy?

Depends on your constraints. Options range from fully on-prem open-source models to enterprise AI APIs with data-processing agreements. We design the integration to match your compliance bar.

How do we avoid runaway AI bills?

Per-request and per-tenant caps, caching where output is stable, smaller models for the cheap cases and frontier models only where they earn their cost. Cost monitoring is part of every deployment.