How We Work
A structured path from business requirement to production.
Complex AI projects can involve several technical disciplines. Our job is to bring those pieces together through one managed delivery process.
From discovery and architecture through development, independent testing, deployment, documentation, and ongoing support, NL Applied AI keeps the project moving toward one defined outcome.
Clear process. Specialist delivery. One accountable partner.
You do not need to arrive with a technical specification.
Start with what is happening in the business.
Maybe a process takes too much time. Information has to be copied between several systems. Employees spend hours working through documents. Customers are waiting too long for routine requests. Or you know AI could help, but you are not sure what the implementation should actually look like.
That is enough to start the conversation.
We begin by understanding the problem, the existing process, the people involved, the systems already in place, and what a successful outcome would look like.
The technology comes after the requirement.
From requirement to production.
Every project is different, but the delivery structure remains consistent.
01
Discover
Understand the business problem, workflow, systems, constraints, data, and desired result.
02
Architect
Define what should be built, how it should work, what it must connect to, and what expertise is required.
03
Build
Assemble the appropriate specialists and implement the approved solution.
04
Test
Review functionality, failure conditions, edge cases, integrations, and agreed acceptance requirements.
05
Deploy
Prepare the system for real-world use, complete documentation, and support the technical handoff.
06
Manage
Continue monitoring, maintaining, troubleshooting, and improving the system where ongoing support is required.
Architected | Built | Tested | Managed
01 — Discover
Understand the business before designing the system.
Good implementation starts with good requirements. Before deciding which tools or models belong in the project, we need to understand what is actually happening today.
Discovery gives the technical team enough context to separate the real business requirement from assumptions about how it should be solved.
Discovery may include:
Existing workflow
People and responsibilities
Current software and systems
Data sources
Manual bottlenecks
Required integrations
Security or access considerations
Business rules
Failure and exception scenarios
Desired outcome
Measures of success
Sometimes the right answer is not AI.
If a simpler automation or conventional software approach makes more sense, that should be identified before unnecessary complexity is introduced.
02 — Architect
Turn the requirement into a practical technical plan.
Once the business requirement is understood, a technical lead determines how the solution should be structured.
The objective is to choose an architecture that is appropriate for the use case, rather than making the project unnecessarily complex or forcing it into a predetermined platform.
Architecture may define:
System components
AI or automation requirements
Required integrations
API connections
Data flow
Human review points
Permissions and access
Technical dependencies
Testing requirements
Project phases
Specialist roles
Estimated implementation effort
The team is assembled around the architecture, not the other way around.
03 — Build
Bring in the specialists the implementation actually requires.
Different projects need different expertise. A simple workflow may require an automation specialist and API developer. A larger AI knowledge system may also require a solutions architect, AI engineer, data specialist, QA evaluator, and security review.
NL Applied AI coordinates the work as one project rather than leaving the client to manage each contractor independently.
A project team may include:
AI Solutions Architect
AI / LLM Engineer
Automation Engineer
Full-Stack / API Developer
Data & RAG Specialist
QA & AI Evaluation Specialist
Security Specialist
Technical Writer
Project Management
One project does not mean one generalist.
It means one managed team with clearly defined responsibilities.
04 — Test
The person who builds the system should not be the only person deciding whether it works.
Where appropriate, implementations are independently reviewed by a QA or evaluation specialist who was not responsible for the original build.
Testing is defined according to the system and the agreed project requirements. The objective is to identify problems before the implementation becomes part of normal business operations.
Testing may include:
Expected workflow behaviour
Integration failures
Edge cases
Incorrect or unexpected outputs
Human escalation paths
Permission or access behaviour
Error handling
Data validation
Regression testing
Agreed acceptance criteria
AI systems cannot responsibly be promised as perfectly accurate in every situation.
Our focus is appropriate testing, controls, escalation, and transparency around what the system is designed to do.
05 — Deploy
Move from working build to usable business system.
Deployment is more than switching something on. The system needs to be prepared for the environment where people will actually use it.
Depending on the engagement, deployment may include configuration, access setup, documentation, final testing, production connections, and support during handoff.
Handoff may include:
Technical documentation
Workflow documentation
Configuration details
Operating instructions
Access and responsibility notes
Known limitations
Support procedures
Training materials where required
Final acceptance review
The finished system should not become an undocumented black box.
06 — Manage
Production systems change after launch.
APIs change. Models change. Business processes evolve. New requirements appear. Integrations fail. Employees find new use cases.
For clients who need continued technical ownership, NL Applied AI can provide ongoing support after implementation rather than disappearing when the initial project closes.
Ongoing support may include:
Monitoring
Troubleshooting
Workflow maintenance
Model or API changes
Minor improvements
Regression testing
Prompt or configuration adjustments
Technical support
New integration requirements
Periodic system review
Support is scoped according to the system and the client's operational requirements.
Clear scope protects everyone.
Technical projects become difficult when requirements change without the scope changing with them.
Before implementation begins, projects should have clearly defined deliverables, responsibilities, assumptions, milestones, and acceptance criteria appropriate to the engagement.
Larger projects may be divided into milestones so progress, approval, and payment follow defined stages of delivery.
If the requirement changes materially, the scope should change explicitly rather than becoming invisible additional work.
Clear scope. Clear expectations. Clear ownership.
Access should match responsibility.
Not every contractor on a project needs access to every client system or piece of production data.
Access requirements depend on the project, but our delivery approach is based on giving specialists the access required for their responsibilities rather than treating unrestricted production access as the default.
Projects involving sensitive information, elevated security requirements, or specialized compliance obligations may require additional technical or security expertise.
Depending on the engagement, that may involve:
Sandbox or test environments
Limited permissions
Role-based access
Temporary credentials
Test or representative data
Separate production access
Security specialist review
Documented access responsibilities
Security requirements should be designed around the actual risk of the system.
Multiple specialists should not mean multiple people for the client to manage.
One accountable delivery partner.
NL Applied AI coordinates the specialists involved in the engagement so the client does not need to independently manage every technical contributor.
Project communication, milestones, responsibilities, and technical decisions should remain organized around the project as a whole.
The team may change according to what the project requires. Accountability does not.
You do not need to know which specialist you need.
You only need to know what you are trying to solve.
Start with a complimentary feasibility review. Tell us what is happening in the business and we will take an initial look at whether the opportunity appears suitable for AI, automation, custom integration, or another technical approach.
Currently accepting a limited number of Founding Client engagements.
Qualifying early clients can access introductory project pricing while receiving the same structured delivery model described on this page.
Founding Client availability is intentionally limited.
Ready to turn a business problem into a working system?
Start with the problem. We will help determine what comes next.
No technical specification required.
Canadian-Led • One Accountable Team • Clear Scope • Specialist Delivery • Independently Tested • Production-Ready • Ongoing Support
Canadian-Led • One Accountable Team • Clear Scope • Specialist Delivery • Independently Tested • Production-Ready • Ongoing Support