Agents & orchestration
Agentic AI
Coordinate approved tools across tasks, with explicit permissions and human review.
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Zynlogic Specialists in AI engineering
Agentic AI.Enterprise intelligence.
We build AI agents, enterprise knowledge systems, and LLM applications that connect your data and tools, with evaluation and human oversight.
Knowledge. Reasoning. Action.
Models & integrations
an adaptable stack
Our Specialties
Deep expertise.
Applied intelligence.
Four connected disciplines. From agents that act to knowledge systems and applications built around your operations.
Knowledge & retrieval
Enterprise RAG
Retrieve organizational knowledge with visible sources and permission-aware access.
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Models & applications
LLM Applications
Build focused AI interfaces with validated outputs and task-specific evaluations.
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Integrations & workflows
Workflow Automation
Connect business systems through event-driven workflows, validation, and approval steps.
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How it fits together
A clear path from
context to action.
Useful AI needs the right information, a considered intelligence layer and clear boundaries for what happens next.
- 01Knowledge & data
Your business context
Connect the documents, records and systems that matter to the task.
Scope the inputs
Define which information the system can access, with the relevant source permissions in place.
- 02Intelligence layer
Context becomes intelligence
Models interpret the request. Retrieval supplies relevant knowledge. Agents coordinate the next steps.
Models, RAG & orchestration
Retrieval augmented generation (RAG) connects a model response to the source material it needs.
- 03Controlled action
Put intelligence to work
Connect the response to approved tools and workflows, within clearly defined boundaries.
Keep a human review point
Route steps that need judgment or authorization to a person before the workflow continues.
Evaluation and oversight
Across the whole system
Test output quality, trace what happened and use feedback to refine the system as business needs change.
Engineering Lifecycle
Defined requirements.
Accountable operation.
Architecture, implementation, evaluation, and ongoing operation form one connected process, with defined requirements and review points at every stage.
- SCOPE
Define the useful problem
user needsdata readinessaccess rulessuccess criteriaWe map the task, the people using it, and the systems it needs to work with.
Together we agree a bounded scope, the required data access, and what a useful result looks like.
- PILOT
Build a focused first version
model choiceintegrationsprototypereview pointsWe connect the necessary data and tools, then build the smallest version that can test the idea.
You can try the workflow and review where a person should approve, correct, or take over.
- EVALUATE
Test before extending
task examplesfailure pathsqualityusageWe evaluate representative tasks, edge cases, and failures against the criteria agreed during scoping.
The findings guide changes to the model, retrieval, tools, and user experience before wider use.
- OPERATE
Observe and improve
monitoringfeedbackhandoveriterationWe agree how the system will be deployed, monitored, documented, and supported.
Usage and team feedback shape the next steps, with a clear owner for reviews and changes.
FAQ
Frequently asked questions.
Enterprise AI
AI engineering, from architecture to operations.
Models, enterprise knowledge, and operational workflows brought together around defined requirements, evaluation, and oversight.
Contact us→Architecture · Integration · Evaluation · Operations
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Work with curious minds building software, AI and technology that moves ambitious businesses forward.
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