AI for a named operating problem
Not an innovation theatre layer. One system tied to work people already need to complete.
built right.made smarter.
ai solutions and integrations, engineered into the software you actually run — not bolted on after.
what we build
Wired into what you already run
Permissions, data, handoffs, and exceptions designed around your current operating environment.
Full-stack, built to hold up
Interface, intelligence, integrations, evaluation, infrastructure, and ownership in the same build.
most AI projects
die as demos.
capabilities
discovery & AI audit
LLM applications
RAG & semantic search
workflow automation
platform & APIs
cloud & infrastructure
how we think
The useful question is rarely “where can we add AI?”
Start with the work.
Find the uncertainty.
Design the control.
Prove the operating result.
production-ready means
evaluated
Quality is tested against a task-specific benchmark before release.
observable
Teams can see cost, latency, failures, drift, and user outcomes.
controlled
Permissions, escalation, and human review live inside the workflow.
owned
Your team gets the code, infrastructure, documentation, and runbook.
A prototype answers “can it work?” Production answers “can we run it tomorrow, understand it next month, and change it next year?”
who Coznix is for
A first AI project with real stakes
You need a clear opportunity, a justified architecture, and an honest path to production.
A pilot that stalled at the demo
You have evidence of value but not the controls, integrations, or operating model to release it.
A capable team already at capacity
You need a senior product-engineering unit that can own a hard stream and leave the system stronger.
what we build with
language modelsretrievalagentsevaluationNext.jsAPIscloudobservability
Provider-flexible by design. The stack follows the operating constraint, not a preferred logo.
common questions
Can we start before our data is perfect?
Usually. We define the minimum trustworthy data path, test it, and make the gaps explicit in the roadmap.
Will you use our current systems?
Where they are sound, yes. Replacing familiar tools without a clear operating reason creates risk instead of value.
How quickly do we see working software?
A useful vertical slice should appear in weeks. The exact sequence depends on access, risk, and the workflow being changed.
What happens after launch?
We can operate alongside your team, transition ownership, or continue into the next proven scope. The handover is designed from the start.
how we work
scope the real problem
Map the work, evidence, constraints, and the decision AI is meant to improve.
ship something usable
Put one end-to-end slice in front of real users and measure the hard assumption.
harden and hand over
Add tests, observability, governance, documentation, and a clear operating owner.
Bring the workflow that is slow, fragile, or too dependent on a few people.