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Turn your knowledge and documents into useful AI applications.
AIREC develops applications that use large language models to retrieve information, interpret documents, generate structured outputs, and support conversation.
We design these systems around your data, users, and operating requirements, with attention to information access, answer quality, application integration, and deployment cost.
We define what information the application can use, which outputs are acceptable, and how those outputs will be evaluated.
For a knowledge assistant, the priority may be finding the right source and answering from it. For a document workflow, it may be extracting required fields into a validated schema. For a conversational product, it may be maintaining context while staying within a defined role.
The architecture follows those requirements.
Capabilities & Practice
We develop retrieval-augmented generation systems that connect language models with approved organizational knowledge.
The implementation can include document ingestion, parsing, chunking, indexing, search, reranking, and source attribution. We design retrieval around document permissions and establish how updated or removed content is reflected in the system.
We build workflows for extracting, classifying, summarizing, and organizing information from documents.
The design specifies document types, required fields, output schemas, validation rules, and review conditions. Processing exceptions are surfaced rather than silently accepted, and uncertain or incomplete results can be routed for human review.
We develop assistants for defined information and interaction tasks.
The implementation can include conversation state, context management, knowledge retrieval, structured responses, and handover to a person or another system. We establish what the assistant should answer, what it should ask, and when it should decline or escalate a request.
We assess prompting, retrieval, model selection, and fine-tuning against an existing baseline.
Where adaptation is justified, we prepare task-specific examples, training configurations, and evaluation sets. Candidate models are compared against the same acceptance criteria before being selected for integration.
We evaluate the parts of the system that affect the user’s task, including retrieval relevance, support for answers in source material, extraction quality, response consistency, latency, and cost.
Depending on the deployment, optimization work may include context reduction, caching, batching, quantization, or model routing. Changes are tested for both performance and task quality.
We assess managed model APIs, self-hosted models, and hybrid architectures against data sensitivity, required capabilities, expected usage, and operational capacity.
The design establishes where data is processed, how credentials are managed, and how model or provider failures are handled. Provider-specific data handling terms are reviewed as part of the architecture decision.
Typical deliverables include the application workflow, data-processing and retrieval components, model integration, prompt and configuration assets, evaluation datasets, deployment configuration, and operating documentation.
Where fine-tuning is included, the engagement also defines the training assets and model artifacts to be delivered, subject to applicable model and data licenses.
Build an application that connects model capabilities with the right information, evaluation, and operating controls.
We evaluate them against different requirements. We consider retrieval for access to approved knowledge and fine-tuning for demonstrated task or behavioral needs. A solution may use either approach, both, or neither.
That depends on the agreed architecture. We establish data-processing boundaries before implementation and assess self-hosted or hybrid options where required.
No. We design for validation, source checking, evaluation, and appropriate escalation rather than promising error-free outputs. The acceptable use of the system depends on the task and consequences of mistakes.