How to build GenAI the right way: strategy, architecture & energy‑efficient models
At Trail Openers, we don’t say “don’t use GenAI.” We say: use it where it creates value — and do it responsibly. That means starting from strategy, designing an AI architecture that fits your business and digital environment, and choosing right‑sized models so you get results with less energy, less cost, and more trust.
TL;DR — our stance on Generative AI (GenAI)
Start with strategy and governance, then pick technology.
Design an AI architecture that fits your data, stack, and controls.
Decompose workflows; use GenAI only where it adds value.
Choose the smallest model that meets quality; this cuts energy, cost, and risk.
Measure quality, latency, cost, and environmental impact continuously.
Start with strategy, not with a model
We align AI with business outcomes, risks, and constraints before anyone selects a model or writes a prompt:
Define the business objective (e.g., reduce handling time, improve quality, increase satisfaction).
Map risks and requirements (privacy, security, compliance, accuracy, energy/carbon budget).
Choose the operating model (ownership, maintenance, observability).
This mirrors leading guidance (e.g., NIST AI RMF, ISO/IEC 42001): strategy first, then technology.
Architect to fit your digital environment
We design an AI architecture that integrates with your data, systems, and governance — instead of bending your environment around a single “hero” model.
Ingest and governance: permissions, PII handling, retention, auditability.
Knowledge & retrieval: use retrieval‑augmented generation (RAG) so the model reads from your sources instead of guessing.
Decision points: if a step is deterministic, use code; use GenAI where it truly helps.
Model layer: pick the smallest model that meets the quality bar; scale up only if needed.
Guardrails, testing, and observability: evaluate quality, risk, latency, and cost continuously.
Decompose complex processes into steps
We split complex workflows into clear steps and decide, step by step, whether GenAI belongs — and which pattern to use.
If deterministic logic is enough, prefer rules or conventional code.
For knowledge tasks, use RAG to ground answers in your content and reduce hallucinations.
For style or format consistency, consider lightweight fine‑tuning or prompt libraries.
For multi‑tool tasks, orchestrate with agents — but keep them simple and testable.
Model choice matters — for accuracy, energy, and cost
Not all models are equal. The energy and euro cost per task can vary widely with size, context length, and serving setup. Our rule of thumb: use the smallest competent model.
Right‑sized models often achieve target quality with much lower compute and carbon.
Distillation, quantization, and caching can retain most quality while cutting energy use.
Where and how you run matters: region carbon intensity, batching, and efficient serving affect footprint.
Measure what you run — cost, latency, quality, and environmental impact — and iterate.
The Trail Openers way
We apply the same responsibility mindset we use across green ICT and sustainable digital services:
Understand & measure: baseline energy and resource use; identify high‑leverage AI opportunities.
Optimize architecture: prefer RAG over retraining, trim context, and pick models to fit the job.
Build sustainably: embed governance, testing, and observability; improve continuously.
A practical checklist you can use tomorrow
Strategy & governance: define outcomes, guardrails, and an energy/carbon budget.
Architecture: integrate with your stack; instrument quality, latency, cost, and risk.
Process design: decompose workflows; use GenAI only where it adds real value.
Model selection: start small; scale up only if quality demands it; apply distillation/quantization where appropriate.
Measurement: track per‑request tokens, cost, latency, and periodic environmental impact with open tools.
Frequently asked questions
Where should we start: with a model or with strategy?
Strategy. Define outcomes, risks, and guardrails, then select technology to fit.
How do we decide where GenAI belongs in a process?
Split the process into steps. Use rules where deterministic logic suffices; use RAG or lightweight fine‑tuning when GenAI adds measurable value.
How do we keep costs and energy down?
Right‑size models, keep contexts lean, cache where safe, and run in lower‑carbon regions. Measure and iterate.
Do we need a single large model for everything?
No. Use a model menu and pick the smallest competent model per task; escalate only if quality demands it.
Work with us
Want a concise GenAI strategy, an architecture that fits your environment, and a model menu tuned for quality, energy, and cost?
