Sector-specific DNAAI architecture

Evolutionstarts whereAI meets industry insights.

We turn the latest AI research into platforms that run in production — and build digital twins that see what your data can’t.

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Industries
01Healthcare & Life SciencesImproving conditions→02Manufacturing & Supply ChainFrom the machine to the last mile→03Energy & UtilitiesKeeping the grid in balance→04Banking & InsuranceKnowing every client→05TelecommunicationsEvery customer, every network event→
Digital twin

Digital twins that see what your data can’t.

A digital twin is a software component that mimics the behavior of any physical system or product. It estimates hidden states, suppresses noise, predicts and raises alerts on its own.

How a digital twin worksReadiness check
Digital twins made of data: a production line, a person and a building as point clouds
How a digital twin works1. A physical system — a product, machine, production line, factory, human or house — has real states x0 to xn. 2. They reach us from sensors and from information systems (ERP, MES, CRM, BIM, LIMS) — by direct measurement, with a delay, not at all during an outage, or never for hidden states; measurements of interacting systems and noise add to them. 3. The digital twin estimates the states y0 to ym across states, time and interacting systems — also through outages — and forecasts ahead with its uncertainty. 4. One API serves predictions and forecasts, root causes and drivers, planned resolution tasks, anomalies, reports and alerts — 100% transparent, 100% available, a single point of access, one unified model, near real time.01Physical systemwhat exists02Signalswhat reaches you03Digital twinwhat it estimates04One APIwhat you get product machine production line factory human houseReal statesx₀ · x₁ · x₂ · … · xₙsome cannot be measuredDirect measurementDelayedT+1Outage✕Hidden state? Information systemsERPMESCRMBIMLIMS Interacting systemsxₙ₊₁ … xₙ₊ₘNoisein every measurementTimeStatesInteracting systemsOne 360° modelpast · present · future of every state,across all interacting systemshistoryforecastnowanomalylive Predictions, forecastsfailure risk in ~6 h Root causes, driversdriver: bearing temperature Planned resolution tasksmaintenance task created Anomaliesnew anomaly detected Reportsreport updated Alertsalert sent to operator100% transparent100% availablesingle point of accessunified modelnear real time
Research-driven

Platforms

One team, one way of working: research turned into six AI platforms that already run in production. Each is configured to your data, not sold off the shelf.

Not sure where to start?AI Capability Assessment →Digital Twin Readiness Check →All platforms →
Case studies

A case in point.

We bridge the gap between the latest AI research and real-world deployment. Explore in more detail.

Case studies
Distribution · MyIO35%better forecast accuracyInventory optimization for 20,000+ productsRead the case →Pharma · COGSSecondsto a verified manufacturing cost, instead of 3–12 monthsGenerics manufacturer: <0.1% deviation from official calculationsRead the case →Life sciences · Floxgen4,000+sequenced genomes aggregatedSimplifying genetic research — IMTMRead the case →
About

A breeding ground for innovation.

Pavel Wimmer and David Kadleček started DNAi in 2018, just the two of them. Today DNAi Group has 260+ people and its own AI platforms in production — self-funded, without investors or loans.

About usCareer
DNAi co-founders talking with a colleague on the office terrace
Wired Beyond AI podcast cover, powered by DNAi
Podcast

Beyond AI

The tireless search for the potential and limits of new technologies has connected us with Wired and top minds from around the world in a discussion that goes beyond the buzz.

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Tell us what you want to solve. We’ll show you where AI pays off.

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