Models
LLMs and ML models selected for task quality, latency, cost, privacy and deployment constraints.
AI ENGINEERING
AI Technology
We combine models, retrieval, orchestration, enterprise integration and evaluation into solutions designed for real operational environments.
01
Technology building blocks
The right architecture depends on the workflow rather than a fixed technology stack.
Knowledge & Retrieval
Document stores, structured data, embeddings, search and retrieval pipelines for grounded responses.
Orchestration
Workflow engines, agent tools, state, approvals, retries and exception paths.
Integration
APIs, events and connectors that let AI read from and act on enterprise systems.
Evaluation & Observability
Scenario suites, quality metrics, traces, operational monitoring and regression checks.
02
Engineering for production
A production AI system needs software engineering discipline around the probabilistic components.
Deterministic controls
Business rules and permissions should remain explicit where appropriate.
Fallbacks
Define what happens when confidence is low, data is missing or a downstream system fails.
Testing
Validate normal, edge and adversarial scenarios before release and after model or prompt changes.
Cost & performance
Monitor token/model usage, latency, throughput and infrastructure economics.
03
Integration architecture
Users and business systems → experience layer → agents/workflows → knowledge/data → enterprise systems → governance and observability. Specific implementation varies by environment and deployment constraints.
NEXT STEP
Find a workflow worth automating.
Technology choices should follow business requirements, data sensitivity, integration constraints and measurable quality targets — not the other way around.