AI applications
Customer-facing and internal applications that combine models, data, software, and clear user decisions in one operating surface.
AI Engineering
AI systems built for real operations.
brimtech designs and builds AI applications, retrieval-augmented generation systems, agent workflows, and the production controls required to operate them with confidence.
What we build
The application matters as much as the model.
A useful AI system needs the right data, workflow, permissions, interfaces, evaluation, and recovery behavior. We engineer the complete system around the job it must perform.
RAG and knowledge systems
Retrieval-augmented generation systems with ingestion, metadata, permissions, retrieval, source references, freshness, and evaluation designed together.
Agent workflows
Tool-using agents with bounded authority, explicit state, approval points, retries, audit history, and a path for human recovery.
Document intelligence
Extraction, classification, validation, and review workflows for documents that feed operational systems and decisions.
Evaluation and reliability
Evaluation suites, production traces, cost and latency measures, regression checks, failure analysis, and release criteria.
AI integration
APIs, event flows, data access, identity, permissions, and operating tools that connect AI behavior to the systems around it.
System architecture
An AI application is a chain of accountable decisions.
Model output is one step. The production system must control what context enters, which tools can run, what evidence is retained, and when a person takes over.
Inputs
Application requests
Documents + knowledge
Operational data
Orchestration
Retrieve
Reason
Use tools
Manage state
Controls
Evaluated behavior
Permissions, evidence, guardrails, latency, cost, and failure handling
Outcomes
User decision
System action
Human review
RAG systems
Retrieval is a data system.
A RAG system succeeds when it finds the right authorized evidence, shows where an answer came from, and makes missing or stale knowledge visible.
- Source ingestion, parsing, metadata, and versioning
- Access control carried through retrieval and generation
- Search, reranking, context construction, and source references
- Evaluation against representative questions and source evidence
- Freshness, feedback, and operations for changing knowledge
Agent workflows
Autonomy needs an operating boundary.
We design agents around specific jobs, tools, and consequences. Each workflow defines what the agent can observe, what it can change, when approval is required, and how an incomplete or incorrect action is recovered.
- Explicit goals, state, tools, permissions, and stop conditions
- Human checkpoints for consequential or irreversible actions
- Idempotency, retries, timeouts, and rollback where actions can repeat
- Traceable evidence for decisions and external tool calls
- Evaluation before release and monitoring after deployment
Focused production assessment
Agent Production Audit
A two-week assessment for a team already running an AI agent or preparing to ship one. We establish where it fails, what it costs to operate, how reliability is measured, and what should be fixed first.
AI Engineering
Start with the job, the evidence, and the consequence.
Tell us what the AI system must do, what information it can use, and what happens when its output is wrong or incomplete.