Built for teams who ship AI in production.
Konic Labs is a production AI engineering partner. We exist because too many AI projects stop at demos — we stay for ownership, measurement, and iteration.
Who we are
Konic Labs is an engineering partner for production AI and intelligent software systems. We sit with AI and platform teams on workloads that have to survive real traffic, real constraints, and real ownership.
Why we exist
Companies do not need another agency that wraps a foundation model and calls it a product. They need partners who will define a task, measure it honestly, and deploy something they can operate.
Our line, used sparingly: Innovate. Automate. Accelerate. Innovate on the model boundary. Automate the work that should not stay manual. Accelerate the path from experiment to production — without pretending every problem needs the largest model in the room.
Our Principles
Architecture before the model
Engineering over packaging
The right size for the job
Measurement is respect
Control is a requirement
Fit over fashion
Why architecture first?
The shape of the system precedes the model
Workload, boundary, and measure define the architecture. Model choice, serving path, and iteration follow — so tools stay replaceable.
Boundary before cleverness
Where inference must live is decided before scale.
Ownership is designed in
Contracts and release paths keep the system operable after go-live.
How we work with clients
Engagement shapes — from a focused fit check to a long-term operating partnership.
| Signal | Fit check | Delivery | Partnership |
|---|---|---|---|
| ScopeWhat we own together | Workload · boundary · fit | Ship a bounded system | Iterate after go-live |
| Ownership | You decide next | Your team inherits | Shared operating loop |
| Proof | Clear yes or no | Baseline · release path | Regression · cost · quality |
How we work
A shared path from workload to production — so everyone knows where we are, what we proved, and what ships next.
01
Discover
Map the production workload, owners, constraints, and where inference must live.
02
Define
Lock the task boundary, success criteria, failure modes, and evaluation baseline.
03
Specialize
Adapt a compact model to the repeated task — not a generic wrapper around a frontier call.
04
Evaluate
Compare quality, cost, latency, and control against the agreed broad-model baseline.
05
Deploy
Ship a private or on-prem serving path that fits the infrastructure that already owns the work.
06
Iterate
Tighten through data feedback, versioned releases, and regression checks after go-live.
What does partnership look like?
We stay for the hard part
Models shift, products change, and yesterday’s baseline can fail tomorrow. We prefer relationships where we refine the boundary and release path with the same owners who run the system.
Not a handoff cliff
Iterate feeds Discover — production is designed for feedback.
Slower to sell, better to run
Splashy pilots are easy. Operable systems take partnership.
Trust questions
- How do you prove claims?
- Against agreed baselines on the workload — quality, cost, latency, and control. We do not publish guaranteed savings percentages on this site.
- Why are Case Studies still patterns?
- Named stories require clearance. Until then we publish the engagement structure we will use — so you can judge how we think without fabricated clients.
- Will you work inside our stack?
- Yes when it fits. Technologies explains why we reach for each tool — and when we recommend something else.
- Do you only do AI?
- Production AI is the center of gravity. Custom software, APIs, cloud, and WordPress engineering show up when they are the spine that makes specialization operable.
Talk with an engineering partner
Bring a production constraint or a repeated AI workload. We will be direct about fit — including when specialization is the wrong move.