OCTOLIT · AI-native
An octopus keeps most of its mind in its arms. So do we. We build intelligence that perceives, reasons and decides on its own — grown on your data, in the dark.
Orchestrate the Impossible.
eight arms · one core · move through it
The nature of it
Eight faculties, one intelligence — an arm for each. None of them needs a prompt from you, and the last one is the reason you can trust the other seven.
Pixels into meaning
Cameras on a line, a scanner in a back office, footage of a substation. Frames go in and scored records come out — and whatever the model doubts goes to a person rather than into your database.
Sound into record
Calls, dispatch radio, shop-floor audio. It transcribes, separates who spoke from whom, and surfaces the commitment somebody made at minute forty of a recording nobody was ever going to replay.
Documents into knowledge
Contracts, tickets, case files, forms scanned crooked years ago. It finds the facts, cites the line each one came from, and throws the prose away — in every language your operation actually runs in.
Grounded, never guessing
One retrieval layer that every model shares, so the same question asked in two places gets the same answer. Outcomes are written back into it, which is how the system gets better rather than merely older.
Plans, acts, revises
It breaks a goal into steps, calls the tools it needs, checks what came back, and re-plans when the result disagrees with the plan. Every branch it rejected stays on the record, which is how you audit a decision later.
Ahead of the signal
Demand, capacity, failure. Forecasts that carry the width of their own doubt, so an operator can tell a confident number from a convenient one before committing a shift or a shipment to it.
Decision into change
It writes back. Raises the ticket, reroutes the shipment, holds the batch — inside your systems, under your permissions, with every action scoped, logged and reversible. A decision nobody executes is just an opinion.
Silence over a wrong answer
Below its threshold it stops, says so, and hands the case to a person. That restraint is engineered, measured and audited — not a lucky side effect. It is also the only reason to let the other seven near anything that matters.
How it does any of this is the part we build.
Ask us in personWhere it runs
Presence
A head office in Limassol and six offices across seven time zones. Core hours run unbroken from Singapore's morning to New York's evening, and an overnight rota closes the rest — which is how support runs 24/7/365 without a night shift pretending to be a day shift.
Plotted in UTC · --:-- now
Seven overlapping windows, and a rota on the two hours they don't reach. Whatever hour it breaks in, somebody is already at a desk.
Careers
Ten seats open on a team of 250. No two alike. We hire continuously, and we read everything.
You own models end to end — framing the problem with the client, choosing the approach, and standing behind the endpoint once it serves real traffic. The evaluation suite is yours as much as the model is, and so is the pager. You will set the bar the rest of the engineering team builds against.
You build the agent layer: tool schemas, planning loops, retrieval that actually retrieves, and the refusal behaviour that keeps a fluent model from inventing things. Half the work is making it capable; the other half is making it admit what it does not know.
Why do many small models sometimes beat one large one? You work on delegation, credit assignment, and the coordination failures that only appear once a system is running at scale. Publish what is publishable, and turn the rest into something an engineer can ship.
You own the road from a notebook to a served model: training pipelines, GPU scheduling, the model registry, rollout and rollback. You will care about cost per run and about how fast a bad model can be pulled. If shipping a model comes to feel unremarkable, you have done the job.
Streaming ingestion, data contracts, and a feature store that serves the same values offline and online. It is the least glamorous layer in the company and the one that decides whether anything above it works. Bring opinions about schema design and a memory of a training-serving skew bug.
Detection, segmentation and quality scoring that has to run on hardware the client already owns, under lighting nobody controls. You will do dataset craft as often as modelling — labelling protocols, class imbalance, augmentation — and you will do some of it on-site, where the cameras are.
Entity and relation extraction across several languages, normalised and validated against a client's real schema, on documents that were scanned badly years ago. Precision comes first here: a pipeline that abstains is worth more to us than one that guesses confidently.
Demand, capacity and failure-risk models where the honest answer includes its own uncertainty. You will backtest without leaking, calibrate properly, and then explain the doubt to an operations director who has to act on the number — which is the harder half of the work.
You sit between the client's constraints and our architecture. Scope what is genuinely achievable, design the integration through whatever cloud and on-premise reality you find, and kill the pilot that cannot survive production — early, in writing, with the reasoning attached.
You own how we prove our systems behave: evaluation policy, red-teaming, model documentation, and the conformity work the EU AI Act asks of high-risk deployments. The role only works if you can block a release and defend that decision to the people whose deadline it was.
Contact us
Some of this is easier shown than written. Thirty minutes, no deck.
Omonias
Limassol, Cyprus