Rules & automation
When rules, exceptions and outcomes are clear, deterministic logic is more predictable, cheaper and easier to test.
AI INTEGRATION
Add AI where interpretation adds more value than fixed rules.
Some process steps can't be reduced to explicit rules, because meaning, language or context matters. Link2Leap integrates AI into existing applications and workflows where that capability makes a specific process step work better, without turning the whole solution into an AI product.
When AI Integration becomes relevant
The process already exists. One step needs an understanding of language or context.
AI Integration starts from a concrete step in an existing process, not from a model or provider.
- 01
Documents, messages or unstructured text have to be interpreted before anything can happen.
- 02
Information has to be classified, extracted or summarised before someone can act on it.
- 03
Users need context-aware assistance inside the software they already work in.
- 04
Repetitive judgement work takes time but still benefits from human control.
- 05
Existing systems hold the workflow, but one step requires an understanding of language or context.
Rules, AI Integration or Agentic AI
Which route fits this process step?
Not every process step needs AI. We decide per step what fits.
AI Integration
When one or more steps benefit from interpreting language, documents or context, within an otherwise existing workflow.
Agentic AI
When multiple steps, systems and decisions need to be coordinated dynamically within explicit boundaries.
AI Integration fits when interpretation adds value but full agentic orchestration is unnecessary.
What we integrate
Bounded AI capabilities inside existing software.
Each capability gets a clear place, input and responsibility in the workflow.
Classification & extraction
Recognising and categorising documents, messages and text, and pulling out the relevant data.
Search & context
Finding the right information in existing sources and making it available for a task or question.
Generation & drafting
Preparing drafts, summaries or replies that a person reviews and completes.
Decision support
Suggestions or recommendations that inform a decision without taking it over.
How strictly outputs are validated depends on the consequences of an error in that process step.
Where AI sits in the architecture
AI forms one bounded layer within the solution.
A reliable integration defines where AI starts and where it stops.
Business-critical rules that work better deterministically stay deterministic. AI does not own them.
- 01
Source systems & data
The applications and data where the process already lives.
- 02
Context assembly
Gathering only the information this step needs and is allowed to use.
- 03
AI capability
The bounded interpretation, extraction or generation.
- 04
Business rules & validation
Deterministic checks on outputs, thresholds and exceptions.
- 05
User or downstream action
A person reviews, or a system performs a permitted action.
- 06
Logging & monitoring
Recording inputs, outputs and deviations to track quality.
How we work
From process step to controlled operation.
We work in a fixed sequence, from use case to monitoring.
- 01
Sharpen the process and use case
Which step, which problem, and what does a good outcome look like?
- 02
Understand data, context and constraints
What information is available, usable and permitted?
- 03
Decide deterministic or AI
Choose per part what rules can handle and where interpretation is needed.
- 04
Integrate
Connect to existing software, APIs and permissions.
- 05
Validate quality
Test outputs and edge cases with realistic examples.
- 06
Monitor & fall back
Track quality, improve deliberately and define a human fallback route.
Technical depth
What a reliable AI integration requires technically.
The model is rarely the hardest part. The connection to systems, data and controls is.
- Context & retrieval
- Deciding what information the AI receives and keeping it current and relevant.
- Instructions
- Clear, testable instructions and output formats that fit the next step.
- Integrations & APIs
- Connecting the capability safely to existing applications.
- Permissions & data access
- AI gets no more access than the user or process allows.
- Validation & fallback
- Checking outputs and having a route for when they fall short.
- Observability
- Making visible what happened, so quality can be tracked and improved.
- Human-in-the-loop
- Placing human review where the consequences require it.
- Cost & speed
- A deliberate trade-off between quality, response time and usage cost per step.
AI Integration and Agentic AI
One capability, or a coordinated workflow.
AI Integration
A bounded AI capability inside an existing workflow.
Agentic AI
Coordinates multiple steps, systems and decisions dynamically within defined boundaries.
Within Link2Leap
AI connected to the rest of the system.
AI Integration is part of AI & Automation. When the use case depends on existing application architecture, modernisation or system integration, Link2Leap connects those disciplines around the same workflow.
When we are less suited
Not every AI wish calls for AI.
Link2Leap is generally not the best fit when:
- The aim is mainly to add an AI badge or chatbot without a real process problem.
- Deterministic rules already handle the step reliably.
- The data or context needed cannot be made available.
- AI output is expected to be accepted without validation in a high-risk process.
- The main need is marketing content generation not improving operational software or processes.
A process step where interpretation is still manual work?
Tell us about the workflow, the information available and the decision involved. We'll help determine whether AI Integration is the right fit and where it belongs in your existing system.
Discuss AI in your existing process