AI Strategy & Data
We partner with you to lay the groundwork for AI success—getting your data ready, selecting the right models, and planning the integration that actually delivers value. No fluff. Just strategy, clear decisions, and roadmaps that scale.
⊹
Data preparation & pipeline design
⊹
Model evaluation & selection
⊹
AI integration planning
At Devin.no, AI Strategy & Data isn’t about building just another model—it’s about creating a foundation that ensures your AI investments pay off. We help you navigate the complexity of data preparation, judge which models make sense for your use case, and plan how AI fits into your business operations. We believe strategy + data = impact.
What working with us looks like:
Evaluating where you are: audit of your data assets, quality, pipelines, gaps, and biases.
Designing pipelines & architecture: structuring data flows, cleaning & normalizing, setting up storage & processing so that data is reliable, accessible, secure.
Choosing & evaluating models: what works best for your problem? Off-the-shelf vs custom vs fine-tuning. Performance trade-offs. Interpretability. Risk.
Planning for integration: how AI will sit within your product or operations. APIs, front-end/back-end implications, data sync, latency, monitoring, fallback plans.
With Strategy & Data from Devin.no, you’ll walk away with more than ideas—you’ll have actionable clarity: what data to collect, what to build first, and how your AI should live in your business for measurable impact.
Data audit report: current state of your datasets, data quality issues, sources, biases, gaps
01
Pipeline & architecture plan: data ingestion, cleansing, transformation, storage strategy, streaming or batch flows
02
Model evaluation matrix: comparison of potential model architectures, trade-off analyses (accuracy vs latency vs cost vs interpretability)
03
Proof-of-concept / benchmarking: pilot tests or small scale models so you can see which direction performs best
04
Integration roadmap: how AI components will integrate into existing systems, APIs, monitoring, security, and compliance points
05
Risk & compliance plan: data governance, privacy, bias mitigation, regulatory requirements
06
Measurement & KPI framework: what metrics matter (accuracy, performance, user outcomes, cost), how to track & iterate
07
