Artificial intelligence can improve search, assistance, analysis and content workflows, but a convincing demonstration is not the same as a dependable product. Teams need clear boundaries, representative evaluation and responsible controls before wider use.
Artificial intelligence can summarise documents, answer questions, classify requests and generate content. The value comes when these abilities solve a clear problem inside a real workflow with appropriate safeguards.
We integrate AI into products with careful evaluation, privacy protection, human oversight and transparent communication to users.
Select a decision worth assisting
The best starting points have meaningful value, available context and a safe path for uncertain results. Teams should compare the proposed approach with conventional software and process improvement. Artificial intelligence belongs where its flexibility creates a measurable advantage.
Evaluate with representative cases
Quality must be measured against the language, edge cases and risk found in real work. A useful evaluation set includes expected answers, difficult examples and unacceptable outcomes. Human review provides essential evidence during early adoption.
Design controls around consequences
Privacy, access, source grounding and audit records should reflect the impact of an error. High consequence decisions require stronger review and clear escalation. Users also need honest feedback about uncertainty and the role of automation.
Starting with the problem
The best AI projects begin with a specific task that is repetitive, costly or slow. Teams should define what a good result looks like, how it will be measured and what happens when the model is wrong. This keeps projects focused on value rather than novelty.
Retrieval and grounding
Large language models become far more reliable when they answer using trusted company content. Retrieval augmented generation searches approved documents and supplies relevant passages to the model, which reduces invented answers and allows responses to cite sources.
Evaluation and monitoring
AI features need test sets of real examples, automated scoring and human review. After release, teams should monitor quality, cost, latency and user feedback. Model updates must be evaluated before they reach users, just like code changes.
Safety, privacy and oversight
Sensitive data should be minimised, masked or kept within approved regions. Users should know when they interact with AI, and high impact decisions should keep a human in the loop. Clear policies define acceptable use and escalation paths.
Recommended technology
Tools we use and why
OpenAI and Google Gemini models
General purpose language and multimodal reasoning.
Anthropic Claude
Long context analysis and careful instruction following.
pgvector
Vector search inside PostgreSQL for retrieval.
LangChain and LlamaIndex
Frameworks for retrieval and agent workflows.
Hugging Face
Open models and tooling for custom fine tuning.
Langfuse
Tracing, evaluation and cost monitoring for AI features.
Step by step approach
How we deliver it
- 1Select a specific high value task and define success criteria.
- 2Collect representative examples and build an evaluation set.
- 3Prototype with grounding in trusted content.
- 4Add safeguards for privacy, safety and human review.
- 5Release to a limited group and gather feedback.
- 6Monitor quality and cost continuously and iterate.
Avoid these
Common mistakes
- Adding a chatbot without a clear problem to solve.
- Skipping evaluation and relying on impressive demos.
- Sending sensitive data to models without review.
- Letting AI make high impact decisions without human oversight.
Measure success
Key metrics
- Task accuracy measured against an agreed evaluation set.
- Time saved per completed task.
- User satisfaction and escalation rate.
- Cost per successful interaction.
Practical priorities
What technology leaders should carry forward
- Choose valuable and bounded use cases
- Evaluate against representative real work
- Match human oversight to the consequence of error
Questions answered
Frequently asked questions
Will AI replace our staff?
In our experience AI works best by removing repetitive work so people can focus on judgement, relationships and complex cases.
Is our data used to train public models?
We configure providers and contracts so that business data is not used for training, and we minimise what is sent.
How do you stop AI from inventing answers?
Through grounding in trusted content, careful prompts, evaluation and clear fallbacks when confidence is low.
SJ Dev Technologies perspective
Responsible artificial intelligence is disciplined product engineering. A clear purpose, measurable quality and proportionate controls turn possibility into sustainable value.