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Tell us about your situation. We’ll show you how and where AI can be applied and what financial impact you can realistically expect.
Kriyanta identifies where AI can move EBITDA and cash in industrial operations — then builds and deploys the applications that capture it.
AI-ACCELERATED
AGENTIC AI WORKFLOWS
AI AGENTS THAT LEARN AND DETECT DRIFT
Kriyanta encodes industrial transformation logic into governed, agentic AI applications — connected to your enterprise data, focused on the operational levers that create financial value, and measured in EBITDA and cash.
Proprietary industrial value graph, economic models and transformation logic built from delivered industrial transformations — the context our agents reason over, not generic model knowledge.
Sales, Engineering, Procurement, Manufacturing, Finance and Organisation run on different economics. Kriyanta encodes those decision logics into applications across the value chain.
The transformation logic sits above the technology stack. It runs on your systems of record, enterprise tools and approved AI models — portable across model and platform, so it outlives whichever one wins.
Human approval gates, auditable actions and continuous impact monitoring. Commercials anchored to a validated business case.
Kriyanta starts with one CEO question: where does the organisation lose time, margin, revenue or cash — and what intervention creates the greatest value?
Not every opportunity becomes AI. The sequence is deliberate: eliminate unnecessary work, simplify what remains, automate what is deterministic, then apply AI where reasoning creates value. The result: a quantified, business-owned roadmap.
The roadmap translates the CEO question into practical decision criteria. It separates where AI creates leverage, where the operating model must change, and what should enter the AI roadmap first.
Processes where throughput can increase, manual effort can fall, cycle time can shorten, or growth can be absorbed without proportional headcount.
Opportunities that move revenue, EBITDA, cash, working capital, material cost, service revenue or margin leakage.
Areas where the bottleneck is governance, decision rights, process discipline or leadership follow-through — not automation.
Feasible use cases with clear data access, workflow ownership, human approval gates and measurable financial impact.
Kriyanta works with commercially sensitive transformation data: ERP extracts, supplier economics, HR activity analysis, finance signals and execution workflows.
Raw enterprise data is processed in the governed workflow layer — not handed wholesale to frontier AI models. Approved AI models are used only through agreed enterprise routes — client-approved, Kriyanta-governed or private — with defined no-training, retention, and deletion terms secured by enterprise-grade DPAs compliant with EU and US privacy frameworks.
Kriyanta is built for industrial leadership teams where margin, cash, capacity and complexity determine enterprise value.
For CEOs, CFOs, COOs, transformation leaders, and business-unit heads facing cost pressure, cash constraints, margin erosion, growth complexity, or performance drift.
For portfolio companies and deal teams needing accelerated value-creation diligence — translating the thesis into executable EBITDA, cash and multiple-expansion levers.
For industrial companies that must change performance materially while managing trust, discretion, supplier relationships, works councils, and long-term operating realities.
Explore our reusable AI application library across the industrial value chain. Each application is configured to the client’s data, validated by operators and connected to measurable financial impacts.
Filter by function or by impact. Demonstrated on reference datasets. Client deployments run on the client’s own data.
Installed-base opportunity mapping and service renewal or cross-sell prioritisation.
Live quote guidance that protects cost and margin, without slowing sales cycle down.
Separates justified engineering from accumulated specification complexity.
AI sourcing and RFQ execution for industrial spend.
Determines what belongs inside your factory — and what should move outside.
Reconstructs the manufacturing value stream — every step, every loss.
Turns SAP safety-stock data into a governed cash-release workflow.
Rapid diagnostic across AR, AP and inventory — agents embedded to keep the gains in place.
Execution of HR operations across the employee lifecycle — without replacing the underlying enterprise platforms.
Installed-base opportunity mapping and service renewal or cross-sell prioritisation.
Live quote guidance that protects cost and margin, without slowing sales cycle down.
Separates justified engineering from accumulated specification complexity.
AI sourcing and RFQ execution for industrial spend.
Determines what belongs inside your factory — and what should move outside.
Reconstructs the manufacturing value stream — every step, every loss.
Execution of HR operations across the employee lifecycle — without replacing the underlying enterprise platforms.
Turns SAP safety-stock data into a governed cash-release workflow.
Rapid diagnostic across AR, AP and inventory — agents embedded to keep the gains in place.
Reconstructs the manufacturing value stream — every step, every loss.
Installed-base opportunity mapping and service renewal or cross-sell prioritisation.
These demos show how an operational signal becomes a validated action — AI enrichment, human approval, execution inside your systems.
The diagram is the straightforward part. What makes these work in production is the decision logic inside each step — which deviations matter, which exceptions need a human, what happens when the data is wrong.
Each one runs today. What changes by client is the approval logic, the systems and the thresholds — the logic underneath is reusable.
Six cooperative agents resolve installed base, engineering, ERP, and data gaps before the quote manager touches the case.
Detect non-compliant supplier payments before they leave SAP, route exceptions to a human owner, and trigger the block / alert action with an auditable trail.
Turn a confirmed customer meeting into a structured account brief — pulling CRM, SAP installed-base, quote and service signals into one AI-enriched commercial view.
Move from CV intake to shortlist, manager review, interview scheduling, HR feedback capture and offer-pack preparation — with human control at every decision point.
Any system. Any source.
Your data, in industrial terms. Mapped once.
Part · Supplier · Plant · Machine · BOM · Product · Inventory · Order · Customer · Cost · Cash
Common data model · Operational relationships · Industry semantics · Mapped once, reused by every node
Reusable expert logic — one node per application
From detection to verified value.
Where approved actions land.
The Kriyanta deployment standard for moving agentic AI from promising pilots into governed industrial workflows—without surrendering data control, operator judgement or financial accountability.
What we learned building AI applications across the industrial value chain—and why value appears only when AI is embedded in real operating decisions.
Our mission is straightforward: to create financial value for industrial businesses faster than was previously possible. Kriyanta deploys a dedicated team of 25+ highly qualified AI and engineering professionals to build and implement AI applications across Europe and the Americas — compressing time, sharpening decisions, and turning operational signals into outcomes that show up in the P&L.
Kiran builds AI applications for the highest-value operational decisions in industrial businesses — across manufacturing, procurement, engineering, finance and operations.
At Kriyanta, he designs and builds the applications and operational agents themselves: diagnosing financial opportunities faster, triggering actions inside workflows, and holding gains after the transformation team has left. The applications are tested against live industrial datasets before they reach a client.
He has led large-scale EBITDA, cash and growth transformations across industrial and energy businesses. He has been a Plant Manager and a Chief Transformation Officer, across global automotive OEMs, industrial corporates and top-tier consulting — execution experience that shapes every application Kriyanta builds.
His transformation work has been featured in the Frankfurter Allgemeine Zeitung.
Based in Munich, Kiran holds a doctorate in Business Informatics and a degree in Mechanical Engineering from Delft University of Technology.
Roswitha is a senior transformation and performance improvement operator who supports businesses in complex, high-pressure situations where pace, credibility and execution discipline matter.
A former Managing Director in the Performance Improvement practice of a leading global transformation firm, she has worked with executive teams and investors across industrial and capital-intensive sectors to deliver operational, cost, EBITDA and cash improvements.
Roswitha brings a strong process perspective and a keen interest in applying AI to process optimization for immediate financial impact. At Kriyanta, she helps clients identify where AI applications can remove operational friction, accelerate decisions and convert transformation opportunities into measurable results.
Jürgen is an experienced industrial operator who turns transformation ambition into measurable EBITDA and cash impact through clear governance, financial discipline and rigorous execution.
A former Executive Board member and Chief Financial Officer across global automotive OEMs and industrial corporates, he led the turnaround and transformation of a €5 billion industrial company. He brings deep expertise in performance management, shared services and IT transformations.
At Kriyanta, Jürgen helps clients translate insights into financially grounded AI execution programs — identifying the right applications, embedding them into management processes and ensuring their impact is measurable in EBITDA, cash and operational performance.
Preetam advises Kriyanta on enterprise delivery and implementation — helping ensure AI applications and workflows are designed for operational adoption inside complex client environments.
He brings 20+ years across enterprise software delivery, SaaS, and large-scale transformation execution — spanning customer success, operational excellence, and professional services across industrial software and technology-enabled business environments.
At Kriyanta, Preetam helps shape practical delivery models, governance routines, and adoption mechanisms that connect AI capability with measurable operational impact.
Based in San Francisco, Preetam studied Computer Science and Engineering at the University of Pune.
Preeti advises Kriyanta on AI product strategy and application design, helping translate industrial transformation logic into scalable, execution-ready AI applications.
She brings 15+ years in AI/ML product strategy, workflow automation and enterprise SaaS — including Director of Product Management roles focused on turning complex operational workflows into digital products.
At Kriyanta, Preeti advises on product architecture, workflow design and human-approval logic, helping ensure applications are scalable, governed and focused on measurable business impact.
Based in Austin, Preeti studied Computer Science at the University of Pune.
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Industrial businesses hold vast reserves of knowledge, operational experience and untapped value.
Too often, releasing it takes too long — or never happens at all.
Kriyanta was founded on a simple belief: transformation can happen faster, with far fewer resources, through the focused application of AI.
We want to change how — and how fast — industrial companies realise value.
We build AI applications that reach the P&L — cost, cash, capacity or growth, wherever the lever sits.
We built the missing execution layer: AI that hands back a decision someone can act on the same day — not another model to interpret, not another dashboard to check.
One line stays non-negotiable: a person signs off before anything moves, because judgement built over years in real operations is worth protecting.
Tell us about your situation. We’ll show you how and where AI can be applied and what financial impact you can realistically expect.