Services
AI-native architecture and engineering
Move from experimentation toward maintainable, measurable AI-enabled applications.
We design software platforms in which AI is integrated into the system's architecture rather than added as an isolated feature.
That includes how a model is chosen, how context is retrieved, how tools are orchestrated, how quality and latency are judged, and what happens when a person needs to take the next step.
What you get
- AI-enabled application and platform architecture
- LLM integration and model selection
- Retrieval-augmented generation (RAG)
- Tool-using AI agents and workflow orchestration
- Model evaluation, latency optimization, and observability
- Human review and fallback mechanisms
Where it helps
Put the model inside the platform, not beside it
Answer from retrieved, authorized context
Orchestrate tool use inside a workflow
Measure quality, latency, and fallback
Technologies
- LLM integration
- RAG
- Model routing
- Evaluation pipelines
- Workflow orchestration
How an engagement runs
01
Define the boundary
Name the business problem, the system boundary, and the constraints before choosing a model.
02
Connect trusted context
Decide what the model may see, how it is retrieved, and whether it is current and authorized.
03
Evaluate on real cases
Judge quality, latency, and failure against representative scenarios, not a single demo.
04
Leave a fallback
Observability, a human review path, and a defined owner for when the model should not proceed.
Questions
- How can AIG ALGO help us adopt AI?
- We can help assess the business problem, evaluate relevant use cases, design the solution architecture, connect AI capabilities to enterprise data and systems, and define evaluation, governance, and operational requirements. The scope depends on your needs and the services agreed upon.
- Can AI be integrated with our existing applications?
- Yes, in many cases. Integration options include APIs, event streams, database access through approved services, retrieval pipelines, and purpose-built adapters. The appropriate approach depends on the existing architecture, security boundaries, data quality, and integration capabilities.
- How do you measure success?
- We establish baseline measurements and agreed acceptance criteria. Depending on the project, these may include accuracy, processing time, latency, error rates, operational cost, manual intervention, workflow completion, or other business-specific outcomes.
- What if a proof of concept is all we have?
- A promising proof of concept is only the beginning. Production adoption requires data quality, evaluation, access controls, integration, latency management, observability, and operational ownership.