AIKUS · Boutique engineering
Connect AI models to the systems your business runs on.
AIKUS provides forward-deployed AI integration engineering. We connect models and agents to your APIs, data and business workflows using Python services, tool integrations and clear operational boundaries.
From a working prototype to a usable customer workflow, we define a practical integration scope, build and verify the connections, handle failures and hand over working software and evidence.
Where we add value
Forward-deployed AI integration
Connect an existing AI assistant or agent to a real business workflow. We work with model APIs, Python/FastAPI services, tool calls, retrieval, customer-system integrations and operational handover.
A concrete starting point: one AI-enabled workflow connected to one defined business system, with agreed acceptance criteria.
API and enterprise integration
Build and investigate the connections between applications. Python services, REST APIs, authentication, data transformation and failure handling.
A concrete starting point: one interface to implement, repair or prepare for acceptance.
Reliable agent workflows
Make model-driven actions dependable: tool permissions, API failures, repeated requests, human approvals and exception handling. Verify the outcome, not just whether the agent called a tool.
A concrete starting point: one consequential agent action with explicit limits and a measurable result.
Automation and quality engineering
Build maintainable checks for APIs, user journeys and business processes. Integrate them into CI/CD so changes produce useful feedback for engineers.
A concrete starting point: automate the critical path for one application or release.
Performance and resilience
Investigate bottlenecks, load behaviour, dependency failures and recovery. More than ten years of JMeter experience supports practical analysis and repeatable evaluation.
A concrete starting point: one performance concern or failure scenario with measurable acceptance criteria.
Security and technical assurance
Evaluate web and API controls, including authorisation, access revocation and cross-user isolation. Provide reproducible findings and verification of agreed remediation.
A concrete starting point: one defined access boundary or consequential workflow.
From an AI demo to a connected business workflow.
Your model can answer a question. Can it safely retrieve customer data, update a CRM, create a record or complete a transaction through an API? What happens when a tool times out, a request repeats or a human must approve the action?
AIKUS helps turn a promising prototype into an integrated workflow: define the permitted actions, implement the system connections, handle exceptions and verify the outcome against agreed acceptance criteria. Your team receives the software, documentation and a practical handover.
A clear first assignment
Connect one AI workflow
Bring an existing model or agent and one business system it needs to work with. We agree the actions, interfaces, acceptance criteria and handover before implementation.
Make a model-to-API connection reliable
Address authentication, tool permissions, retries, duplicate actions and failed handoffs in one defined integration. Agree deliverables and verification before work begins.
Evaluate one API delivery
Delivery Check covers one API product, up to ten endpoints, one test environment, one agreed test-data set and one retest.
We agree scope, responsibilities, timing and commercial terms before work starts. Evaluation and testing use customer-authorised test or isolated environments. Your team retains the release decision; delivery partners retain their client relationship.
Company-to-company engagements. German and English. Remote collaboration, with travel by agreement.
Lab evidence
One reservation request. Three calls.
In our lab, the version deliberately built without idempotency reserved stock three times for one repeated request. The fixed tool returned the same reservation on all three calls and rejected a changed quantity under the same key.
AIKUS-built tool and mock inventory; seeded flaw disclosed.
Payment committed. Confirmation lost.
In our lab, a charge committed but its confirmation was lost; when the flawed tool was retried, it charged twice, after reporting "failed". The fixed tool charged once, reported "outcome unknown" after the loss, and returned the committed charge through a status lookup.
AIKUS-built tool and mock payment provider; seeded flaws disclosed.
Tell us what needs to work.
What are you trying to deliver, where is it stuck and when do you need it? Send a short description to connect@aikus.ai. We will discuss a concrete first scope and the expertise it needs.
AIKUS is operated by Dynacov.