AI
Agents, retrieval and generative pipelines built as products — with evaluation, budgets and failure states designed in from the first week.
THE MODELIS Amaterial.
PlayLab is an independent creative technology studio in Yerevan, founded in 2026, that builds AI systems for companies worldwide: agents with real tool use, retrieval grounded in a client's own material, evaluation harnesses, and generative pipelines for image, film and language. We design, build and ship the system itself — not a strategy deck about one.
We treat a model the way we treat film stock or type: something with a grain, a cost and a failure mode. Useful in the hands of someone who knows what it is for, embarrassing in the hands of someone who does not.
Agentic systems
Tool use, memory and permissions wired into real workflows, with the failure states designed as carefully as the happy path.
Retrieval and knowledge
Grounding a model in a company's own material so its answers can be checked, cited and trusted by the people who wrote the source.
Generative pipelines
Image, film and language production lines built to run repeatedly — the same look, the same voice, the thousandth time as the first.
Evaluation
The unglamorous part. Test sets, scoring, cost and latency budgets. Without it, an AI product is a demo with good lighting.
NO CHAT WINDOWBOLTED ONTOTHE CORNER OFA DASHBOARD.
Frame the judgement
We find the decision the system is really making and write it down in plain language before a single prompt is drafted.
Build the evaluation
Test sets and scoring come before the product. If we cannot measure a good answer, we cannot ship one.
Wire the system
Retrieval, tools, permissions and fallbacks — assembled as software, with the failure states designed as carefully as the happy path.
Tune in the open
We run it against real material with the people who own that material, then tighten until the boring cases are boring.
Keep it honest
Monitoring, regression runs and a standing review, because models drift and so do the workflows around them.
NO DECK.NO PITCHTHEATRE.
- Discovery sprint, then a build engagement
- 8 – 16 weeks
- One call, one page of written intent
- What kind of AI work does PlayLab take on?
Agentic systems with tool use and permissions, retrieval-grounded knowledge products, evaluation and monitoring, and generative image, film and language pipelines. We work end to end: decision map, evaluation, build, tuning, then the monitoring that keeps it honest.
- How long does an AI engagement take?
A discovery sprint first, then a build engagement — typically eight to sixteen weeks from first call to a system running against real material.
- Do you build on top of existing models or train your own?
We build on frontier and open models and treat the model as one material among several. Fine-tuning happens when the evaluation says it earns its cost, not by default.
- How do you know an AI system actually works?
Test sets and scoring come before the product. If a good answer cannot be measured, it cannot be shipped — so every build carries an evaluation harness with cost and latency budgets attached.