AI products and features
Customer-facing or internal AI built into the product and stack you already run.
AI products, agents, and internal systems—from the first evaluation set to production monitoring and handover.
System liveProduction path
What entersA product or operational workflow
AI workModels, tools, and business logic
Control planeEvalsAccessLogs
What leavesAn operated AI systemOwned by your team
What we build
Useful AI, inside real software.
Products, features, and operational systems built into the stack that runs them.
Agents and automation
Tool-using workflows with explicit permissions, approvals, and recovery paths.
Knowledge systems
Search and answers grounded in your documents, data, and internal tools.
Voice and multimodal AI
Systems that understand calls, documents, images, and mixed inputs.
Case studies
Production systems, with measurable outcomes.
Three examples of the engineering behind the product, workflow, and operating result.
Production engineering
The model is one dependency.
We choose OpenAI, Claude, open models, or a combination against the actual workload—not before it.
Evals before optimization
Real examples define what good looks like and catch regressions before release.
Bounded tools and permissions
Every action has an explicit scope, approval path, and recovery path.
Performance and cost visible
Tracing and monitoring show how the system behaves under production load.
Built into your stack
Code, infrastructure, tests, and runbooks stay with your engineering team.
How we work
Validate. Build. Hand over.
Validate
Test the core workflow with real data and clear acceptance criteria.
Build
Integrate the model, tools, data, interface, evaluations, and monitoring.
Operate
Launch, learn from production use, improve the system, and transfer ownership.
Practical questions
Before we start.
Do you build with OpenAI and Claude?+
Yes. We build with OpenAI, Claude, open models, and mixed-provider architectures. The workload determines the model—not the other way around.
Can you take an existing AI prototype into production?+
Yes. We test what already exists, keep the useful parts, and complete the evaluation, data, security, integration, monitoring, and operating layers needed for production.
Can you work inside our existing stack?+
Yes. We integrate with your APIs, cloud, identity, data, observability, and release process. We do not require a separate platform.
Who owns the finished system?+
You do. We hand over the code, infrastructure, evaluation baseline, monitoring, and runbooks needed to operate and extend it.
Start with one workflow
Bring one valuable workflow.
We will help define what to test, what to build, and what success must look like.