Your integrations were builtover decades.Modernizing them shouldn't take another one.
iVolve moves enterprise integration estates off legacy middleware onto modern platforms — with accumulated integration intelligence, deterministic automation and AI.
Real landscapes, real cutovers, real go-lives — not synthetic scenarios or lab benchmarks.
That volume was not delivered by people alone. It was delivered by iVolve — an accelerator we spent four years building, on top of ten years of transformation work.
Delivered in production across industries, regions and every major integration platform.
That experience encoded into iVolve — engineered specifically for integration modernization.
Every estate teaches iVolve a pattern the next migration no longer has to relearn.
iVolve wasn't invented in a lab. It was learned from transformation at scale, then engineered into a product that compounds with every landscape it meets.
One page. Every migration path. Measured, not estimated.
Automation factor is measured at landscape level across delivered engagements. Each row is read in tenths: solid blocks are automated on every engagement, lighter blocks are the observed spread between the low and high water marks.
The factor above is a landscape-level figure. Per interface it swings from roughly 20% to 90%, depending on complexity and how much pattern reuse the estate allows — which is why we publish a range rather than a single headline number.
The ceiling sits higher still: SAP Neo to Cloud Foundry, automated end to end through our MINT app.
Named enterprises. Real platform combinations. Delivered work, not a pilot.
Every path below has been run end to end on a live estate — the source platform they were on, and the platform they run on now.
Delivered across 50+ enterprise customers, supported by a 150+ strong global integration team and 15 years of SAP integration practice.
These enterprises moved their integration backbone. None of them could afford to get it wrong.
Manufacturing, pharma, retail, banking and energy — landscapes where an interface failing quietly means a plant, a shipment or a payment run stops. Every logo here is a production cutover, delivered.
What clients say.
“Tarento migrated 300+ webMethods interfaces, 164 B2B and 136 MFT, to SAP Integration Suite in under seven months, with zero business disruption.
This helped us retire our legacy infrastructure on time, saved associated licensing costs and enabled the shift from CAPEX to OPEX. The execution is the reason we awarded them our AMS contract.”
“Our migration from webMethods to SAP Integration Suite was a critical step towards advanced integrations. With Tarento's expertise and their iVolve solution, the transition was executed swiftly and accurately, with minimal disruption to our operations.
Their team managed interface complexities and ensured seamless partner integration, providing transparent support throughout — a faster go-live, fewer manual interventions, and smoother adoption.”
“We at Plasman Europe use Tarento for all of our integration work — overall SAP B2B / EDI integration.
They have been a key supplier in our transformation from SAP Business Connector to SAP Cloud Process Integrator, as well as in our adaption as a supplier to BMW. Highly recommend their services!”
“We had a project of considerable size that we would have struggled to deliver without our partner, Tarento. Tarento showed themselves as the real experts in the area.
They have done quick deliveries of complex scenarios and helped us through highly important periods without any downtime, keeping clear communication with all stakeholders. Flexible, technology agnostic, with a real passion to help.”
Different technologies in. Normalized integration intelligence. Modern technologies out.
Six states, one continuous transformation. Select a state to see what happens inside it, what it produces, and what it saves.
The estate as it exists today.
We take the estate exactly as it stands — undocumented, partially owned, decades deep — with no pre-work required from your team.
A complete, machine-readable inventory of the landscape
Not a collection of accelerators. A modernization system.
Select a state to open it.
Automated crawlers map every interface, dependency and payload before transformation budget is committed.
Target platform, redesign candidates and wave sequencing decided against measured complexity, not assumptions.
iFlows, mappings and business rules regenerated in target-native form, with human-in-the-loop approvals.
Test cases generated from production payloads; replay verifies migrated flows behave identically to source.
Automated monitoring, failover and DevOps keep integrations resilient long after cutover.
Three reasons enterprises modernize. Only one of them is a deadline.
A vendor end-date forces the conversation. Consolidation and AI-readiness start it long before anything expires — and most landscapes we see are driven by more than one.
For these platforms the cost of standing still doesn't arrive all at once — it arrives quietly, until the migration you delayed becomes the migration you're forced into.
Most estates run integration in three or four places at once — a legacy hub, a cloud platform already licensed, point-to-point built along the way, and whatever an acquisition brought with it. Nothing is expiring. You are simply paying for, staffing and governing several platforms to do one job.
If you already run a strategic cloud platform, every interface left elsewhere is duplicated cost.
AI-era operations need clean, event-driven, API-first integration and data that is reachable in real time. Legacy middleware wasn't designed for it, and estates split across platforms can't expose a coherent surface for agents and analytics to work against — which pushes you back to consolidation.
Modernization is what makes an AI strategy executable rather than aspirational.
One governed network of agents, across the whole modernization lifecycle.
Not an architecture diagram — an ecosystem view. It shows what runs where across strategy and discovery, migration and delivery, and operate and govern, and how the governance sits over all of it.
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AI where intelligence helps.Determinism where certainty matters.
Speed comes from knowing which work is judgement and which work is mechanics — and never confusing the two.
Accumulated integration intelligence
Decades of business logic live inside your interfaces — routing rules, exceptions, partner quirks. iVolve reads that estate first, so modernization starts from what you already know, not from a blank canvas.
AI for meaning and translation
Undocumented intent, mapping logic and scripts written in one platform's dialect are exactly where models are strong. AI carries the meaning across — and a reviewer confirms it.
Determinism for structure
Flows, routing, connectors and packaging are generated from rules and templates. Re-run the migration and the structural output is identical — testable, explainable, auditable.
Every reviewed decision is remembered, so the next landscape starts smarter than the last one.
The questions your security team will ask before they let this near production.
Integration is where your most sensitive data moves. Putting AI anywhere near it is a governance decision before it is a technology decision — so we publish our answers rather than wait to be asked.
Where does our integration metadata actually go?
iVolve runs inside your governed boundary. Interface definitions, mappings and payload structures are processed within the agreed environment — not pooled, not retained beyond the engagement, not used to train anything.
What is the AI allowed to decide on its own?
Nothing that can be determined deterministically is left to a model. AI is scoped to ambiguity — undocumented intent and edge cases — and every AI-produced artifact passes a human approval gate before it is promoted.
Does production data ever touch a model?
Discovery and transformation operate on structure and metadata. Where production payloads are required for validation, they are masked, scoped and handled under the same controls as the source platform they came from.
Can we prove to audit what changed and why?
Every transformation carries lineage from source artifact to target artifact, including which step was deterministic, which was AI-assisted, and who approved it. The evidence pack is the audit trail.
The full control model — data boundaries, model usage, retention, human-in-the-loop gates and audit lineage.
Written for security architects and CISOs, not for procurement. Read it before the first workshop, so the governance conversation is already behind you.
Know your return before you commit to the move.
Licence versus licence is not a business case. This model runs the full cost of ownership for your own landscape — migration included as a first-class variable — and tells you when it pays back.
- 01LicencesCurrent platform versus target subscription.
- 02InfrastructureServers, environments and runtime footprint.
- 03Support & operationsVendor support, monitoring, incident effort.
- 04Adapters & add-onsThe line items that rarely reach the slide.
- 05PeopleSkills, capacity and developer productivity.
- 06Migration itselfModelled by interface complexity, not assumed.Modelled
These variables change the shape of your payback curve.
If the return is thin, the model says so — and shows which assumption is holding it back. A case you can defend beats a flattering one.
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Your integration estate already contains
the intelligence needed for its next generation.
Let's evolve it.