AI features built for production use.
Averon integrates LLM-powered features, retrieval pipelines, agents, and intelligent automation into products and workflows.
When to bring us in
The signals worth acting on
- An AI demo needs to handle real user inputs, edge cases, and production constraints.
- Inference cost, latency, and output quality need to be evaluated together.
- Teams need clarity on data handling before deploying AI features.
Scope of work
Concrete capabilities. Useful deliverables.
Product experiences
Design AI features around a real user need and surrounding workflow.
- Conversational assistants
- Copilots
- Document processing
Retrieval and orchestration
Connect models to relevant context and multi-step workflows.
- RAG pipelines
- Vector databases
- Semantic search
- Agent orchestration
Evaluation and operations
Consider output quality, latency, cost, privacy, and monitoring as part of implementation.
- Model evaluation
- Prompt optimization
- Fallbacks and monitoring
- PII filtering
Our approach
Make the trade-offs explicit
Assess the use case
Review the user need, available data, and constraints to identify where AI may add value.
Prototype and evaluate
Test a focused workflow and examine quality, latency, and cost before expanding scope.
Integrate with the product
Implement the feature with appropriate data handling, fallbacks, and monitoring.
Deploy and improve
Observe performance in use and refine the system as product needs and evidence evolve.
Tools follow the problem
Built around your product’s constraints
The stack is a decision made around the constraints of your product, team, and operating environment.
A little more clarity
Questions before we start
Can you work with our existing team?
Yes. The engagement can sit alongside your product and engineering team, with explicit ownership, shared reviews, and documented handoff.
What happens before implementation?
Discovery frames the problem and risks. Sprint Zero defines flows, architecture, scope, and acceptance criteria before delivery begins.
How do you scope the engagement?
We start with the outcome, existing constraints, and the decisions that need evidence. The first engagement is scoped around a concrete set of deliverables.
Evaluate an AI use case
Book a strategic call to discuss the use case, data, and constraints behind your AI feature.
A 20-minute discovery call.