Intelligence built for real operations

Qubiq AI designs, builds and independently validates AI agents, vision systems and automation — so what works in the demo also works on the floor.

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Applied AI studio Strategy · Build · Validate
Live agent run #4821

Inbound order → pick plan

✓Read supplier email & attachment1.2s
✓Match items to SKU catalogue0.8s
✓Check stock & slotting rules0.4s
Drafting pick plan…running
5Supervisor approvalpending
Exceptions resolved without a human 87% rolling 30-day average

Human stays in the loopHigh-risk actions wait for sign-off.

Approve
Product interface shown for illustration.
What we do

Four ways we put AI to work.

Pick one, or combine them. Every engagement ends with something running in production — and the evidence that it is safe to rely on.

01

AI agents & automation

Agents that read, decide and act across your email, documents and systems — with guardrails, audit trails and human approval where it matters.

  • LLM agents
  • Document intelligence
  • Workflow orchestration
Learn more
02

Vision & robotics validation

Inspection models and independent testing for robots and warehouse automation — simulation before deployment, acceptance checks before go-live.

  • Visual inspection
  • Simulation testing
  • Acceptance & safety review
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03

Data & ML engineering

Clean pipelines, evaluation harnesses and models you can monitor — the unglamorous foundation that decides whether AI keeps working after launch.

  • Data pipelines
  • Evals & monitoring
  • Model deployment
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04

AI strategy & governance

Find the use cases worth doing, size the risk honestly, and set up the policies, controls and reviews your regulators and your board will ask about.

  • Use-case discovery
  • Risk & controls
  • Readiness reviews
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How we think

Not a demo. A system you can trust.

Most AI projects stall between the pilot and the production floor. We work backwards from the day it has to be reliable.

01

Measure before we build

We define what “good” means — accuracy, latency, cost, failure modes — before writing a line of code, then test against it continuously.

02

Humans stay accountable

Agents propose; people approve what carries risk. Every action is logged, explainable and reversible.

03

Independent by design

We can validate systems we did not build. A second pair of eyes before go-live is cheaper than an incident after it.

4–6 wkstypical time from kickoff to a working pilot
100%of agent actions logged with an audit trail
Day 1evaluation harness, before any feature work
Process

From question to production, in four steps.

1

Discover

Map the workflow, the data and the risks. Agree the one metric that decides success.

1–2 weeks
2

Prototype

A thin, working slice on real data — enough to prove it works and learn where it doesn’t.

2–3 weeks
3

Validate

Evals, red-teaming and, for physical systems, simulation and acceptance testing.

1–2 weeks
4

Deploy & run

Roll out with monitoring, fallbacks and a clear owner. Then keep improving.

Ongoing
Where it lands

Built for the operations that keep the lights on.

Typical scenarios we work on. Detailed client stories are shared under NDA.

WarehouseRobotics

Validate grid and AMR deployments before go-live

Simulated throughput and fault testing, plus a structured safety and acceptance review against your site rules.

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Customer ops

Support agents that resolve, not deflect

Grounded in your policies and systems, with clean hand-off to a human.

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Back office

Documents in, decisions out

Invoices, contracts and forms extracted, checked and routed automatically.

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QualityVision

Visual inspection that keeps up with the line

Defect detection tuned on your parts, with confidence scores and a review queue for the edge cases.

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Engagement

Start small. Scale what works.

Scoped, fixed-outcome engagements. No open-ended retainers unless you want one.

Explore

AI Readiness Sprint

2 weeks · fixed scope

Find the use cases worth pursuing and know exactly what it takes.

  • Workflow & data review
  • Ranked use-case shortlist
  • Risk & ROI assumptions
Talk to us
Assure

Independent Validation

3–6 weeks · per system

A second opinion on an AI or robotic system before — or after — it goes live.

  • Test plan & simulation runs
  • Safety & acceptance checklist
  • Written findings & sign-off pack
Request a review
FAQ

Questions we hear first.

Don’t see yours? Send it through and we’ll answer in plain language.

Do you only build new systems?

No. We also validate systems built by others — vendors, in-house teams or off-the-shelf products — and give you a written, independent view of what’s safe to rely on.

Which models and tools do you use?

Whatever fits the job: frontier LLMs, open-weight models, classic ML and vision. We stay vendor-neutral and design so you can swap models without rebuilding.

How do you handle our data and privacy?

We work inside your environment where possible, minimise what leaves it, and agree data handling in writing up front. Nothing of yours is used to train third-party models.

How fast can we see something working?

Most pilots show a working slice on real data within three to four weeks. We’d rather show you something imperfect early than something polished late.

What happens after launch?

We hand over with monitoring, a runbook and trained owners. If you want us to stay on for tuning and new use cases, we can — but you’re never locked in.

Let’s make AI work for you

Tell us about the workflow you’d like to improve. We’ll reply within one business day.