N-IT / AI engineering
Useful intelligence. Real accountability.
Practical AI features and automation with explicit evaluation, privacy boundaries and human oversight.
01 / What needs to change
A convincing demonstration is not a reliable production feature. Inconsistent answers, sensitive inputs and unpredictable usage costs need engineering controls.
What we deliver
- 01
Document search and retrieval-assisted assistants
- 02
Human-reviewed extraction, classification and summarization
- 03
Workflow automation with bounded tool permissions
- 04
Evaluation datasets, monitoring and usage controls
02 / The engineering approach
We compare an AI feature against a simpler baseline, define unacceptable failures and test on representative examples. Sensitive data handling and provider retention settings are agreed before integration.
03 / From discovery to operation
Discover
A convincing demonstration is not a reliable production feature. Inconsistent answers, sensitive inputs and unpredictable usage costs need engineering controls.
Architect
We compare an AI feature against a simpler baseline, define unacceptable failures and test on representative examples. Sensitive data handling and provider retention settings are agreed before integration.
Build
Document search and retrieval-assisted assistants. Human-reviewed extraction, classification and summarization
Test
Review behavior, security and failure scenarios.
Deploy
Release with monitoring, documentation and rollback.
Improve
Use feedback to decide what earns a place next.
SOLUTION CONCEPT · NOT A CLIENT CASE STUDY
An internal knowledge assistant that cites approved documents and escalates uncertain answers to a person.
Where this fits
Pricing
Every proposal defines deliverables, exclusions and assumptions. Final pricing follows an agreed scope. FinTech, security testing and enterprise architecture are individually quoted.
PricingGood questions. Clear answers.
Will AI replace human review?
Not for critical decisions. We define where automation is acceptable, where approval is required and how uncertain results are escalated. AI output is tested, not assumed correct.
What do you need to estimate the work?
A description of your users, current workflow, constraints and desired outcome. We validate uncertain integrations during discovery, then document deliverables, exclusions and acceptance criteria.
Who owns the code and delivery decisions?
Ownership, third-party licenses and handover terms are written into the agreement. Human engineers review implementation and own technical decisions; AI assistance does not remove that responsibility.
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