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Build an AI system that fits your product, data, and operations.
AIREC designs and develops AI-enabled systems around specific business and technical requirements.
We bring models, application services, data pipelines, and enterprise integrations together into a working solution. The engagement covers not only the AI component, but also the engineering required to make it usable, testable, and maintainable.
An AI feature needs a clear place within the wider application.
We define how information reaches the model, how outputs are validated, which business rules remain deterministic, and how users or downstream systems interact with the result.
Depending on the problem, the solution may combine language processing, retrieval, predictive models, computer vision, or agent-driven workflows.
Capabilities & Practice
We define the responsibilities of application services, models, orchestration components, storage, and external systems.
The architecture establishes API contracts, authentication requirements, processing boundaries, and deployment dependencies. AI-generated outputs are separated from the application rules that govern permissions, transactions, and consequential actions.
We connect approved data sources with the processing and model components required by the application.
This may include ingestion pipelines, preprocessing, retrieval services, model inference, and structured output validation. Data freshness, access permissions, and error handling are incorporated into the integration design.
We develop service APIs and processing workflows that connect AI capabilities with your existing software.
Where needed, we support asynchronous jobs, event-driven processing, request validation, and controlled interaction with third-party APIs. Integrations are designed around the interfaces and access available within your environment.
We combine software testing with task-specific AI evaluation.
The test plan can include unit and integration tests, representative evaluation datasets, output validation, regression checks, and load testing. Models, prompts, configurations, and application code are versioned so changes can be assessed before release.
We prepare the application for its agreed hosting environment, whether that involves managed services, self-hosted infrastructure, or a hybrid design.
Deployment work can include containerization, configuration and secrets management, health checks, logging, monitoring, and rollback procedures. We define how failures are surfaced and what the application should do when an AI component is unavailable.
We begin with requirements and an architecture plan, then build a focused working implementation.
After validating the central workflow, we extend integrations, test operational behavior, and prepare the agreed release. Handover includes the technical documentation and ownership arrangements required for continued operation.
Typical deliverables include application and service code, integration interfaces, processing pipelines, evaluation assets, deployment configuration, and technical documentation.
The project scope defines the deployment environment, acceptance criteria, handover responsibilities, and any ongoing support.
Turn an AI concept into an integrated application with a clear technical architecture and measurable acceptance criteria.
Yes. We assess the existing architecture and identify how to introduce AI capabilities through services, APIs, or workflow integrations without assuming the entire application needs to be rebuilt.
Yes. We review what can be retained and identify gaps in testing, integration, security controls, performance, and operational support before planning further development.
We establish ownership during scoping. An engagement can include handover to your engineering team, a defined support arrangement, or additional development phases.