Moving reporting off spreadsheets
How to move reporting from spreadsheets into a database without losing the trust of finance and operations: target options, data modelling, quality checks and a staged migration.
ISO 42001 or ISO 27001 for your AI product?
ISO/IEC 27001 manages information security; ISO/IEC 42001 manages the risks of AI systems. What each covers, which to do first and how to run them as one programme.
EU AI Act for software teams: provider or deployer?
Your EU AI Act obligations depend on whether you are the provider or deployer of each AI system, and on its risk tier. Roles, duties and the dates in force as of October 2026.
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Shadow and canary releases for machine learning models
Shadow releases test a new model on live traffic with no user impact; canary releases expose a small share of users and widen only if metrics hold. Here is how to run both, with a release checklist.
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RAG or fine-tuning: which one does your product need?
RAG changes what a model can see; fine-tuning changes how it behaves. This guide explains when each fits, what data each needs and how to decide with an evaluation set rather than a demo.
RAG or fine-tuning: which one does your product need? Read More »
How to test an LLM feature before it ships
How to test an LLM feature before launch: build a golden dataset from real inputs, combine automated checks with human review, and block any prompt or model change that fails the regression set.
AI CTO as a service: reducing risk in your AI roadmap
AI CTO as a service gives startups fractional tech leadership at 60-80% less cost. Dignep Group provides ISO-certified AI strategy, architecture decisions, and vendor evaluations for 2026.
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Data labeling for AI: quality, cost and model readiness
Data labeling is the work of attaching the correct answer to raw examples (a box around a pedestrian, an intent on a support ticket, a speaker on an audio clip) so a model can learn from them or be tested against them. Whether your model works in production depends more on how consistent those labels
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Building a GenAI MVP in 4–8 weeks: scope, team and cost
Yes, you can build a working GenAI MVP in 4 to 8 weeks, as long as it does one job for one type of user and you decide up front how you’ll judge its output. At Dignep it’s a small-squad build, scoped and quoted before work starts. Most overruns come from scope creep and a
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AI agent development: from PoC to production
Getting an AI agent from proof of concept to production usually takes three stages: a PoC that proves the model can do the task on your real data, an MVP that runs it for a limited set of users with logging and guardrails, and production hardening for reliability, permissions and cost. The model is rarely
