Deeplinq connects your ERP, MES, and document systems to AI agents that work against concrete operations — without forcing a multi-year platform migration.
In private preview with selected industrial and services enterprises across EMEA.
Platform infrastructure across your ERP, MES, and document systems — where your team deploys AI agents against concrete outcomes, without a stack rebuild.
Industries is the surface for organizations sharing four signals.
ERPs running fifteen years. MES and SCADA tuned to your lines. Document archives with quality records, contracts, specifications. The data is there. It hasn't been accessible to AI.
A POC that worked in isolation. A vendor demo against clean sample data. Six months later, still no connection to real systems. The model was never the blocker.
From the board, operations, customers asking questions your competitors already answer. The pressure is real. The path forward is not obvious.
Suppliers, distributors, logistics partners. Decisions depend on information in other organizations. Chain orchestration is where the next margin point comes from.
If two or more describe your situation, the rest of this page is for you.
Four sub-segments share the same problem: valuable data trapped in legacy systems, AI stalled on integration. Angles differ.
Production data split across ERP, MES, SCADA, quality systems. Predictive maintenance, defect reduction, throughput need data the systems cannot share.
Connect the systems that run your plants. Agents against operational KPIs — not dashboards observing the problem.
HACCP documentation, supplier traceability, quality records, multi-site coordination. Hundreds of formats and a regulator expecting chain visibility.
Document intelligence against quality and traceability archives. Supply chain orchestration reading your actual operating reality.
Multi-vendor catalogs, demand signals fragmented, procurement eating margin. Chain intelligence out of reach because data never lines up.
Catalog orchestration across vendors. Demand sensing across channels. Agents reading end-to-end, not silo by silo.
Working hypothesis. Every number below is an estimate — not a promise.
The workflows you chose first — document extraction, report preparation, request triage — measurably faster.
Target range: 30–60% cycle-time reduction on the bounded workflows in scope.
Agents reading production data, quality archives, supplier inputs — surfacing patterns dashboards miss. Fewer late defects.
Target range: 10–25% reduction in defect escape rate on workflows in scope.
HACCP records, technical specifications, contracts, operating procedures. Queried in plain language, citations to source.
Target range: 50–80% time reduction on document lookup and synthesis.
Throughput and document-ops gains compound into measurable operating cost reduction on scoped workflows.
Target range: 15–30% operating cost reduction on first deployment's scope.
Ranges based on design-partner pilots underway. Case studies as partners authorize.
Seven operational use cases across four sub-segments. Each runs against existing systems — no data migration, no rebuild.
Agents read sensor history, maintenance logs, production context from MES and SCADA. Flag anomalies before line stops. Recommend intervention windows that fit your schedule.
Primary fit: industrial / manufacturing.
Query HACCP records, inspection reports, CAPA documentation, supplier certificates. Synthesized answers with citations. Surface compliance gaps before audit.
Primary fit: agri-food. Secondary: any regulated operational archive.
Reconcile catalogs across dozens of suppliers. Detect pricing drift, missing specifications, inconsistent attributes.
Primary fit: distribution. Secondary: industrial procurement.
Agents trained on your methodology, past engagements, domain documentation. Augment practitioners on proposal preparation, analysis, research synthesis.
Primary fit: services B2B. Secondary: internal centers of excellence.
Read signals across ERP, supplier portals, logistics feeds, external data. Surface disruption early. Re-plan at the pace of disruption.
Primary fit: agri-food, distribution, industrial.
Agents read inspection data, in-line measurements, operator reports. Pattern-match against historical defect signatures. Flag drift before the customer.
Primary fit: industrial / manufacturing, agri-food.
Operators, technicians, field teams ask in plain language. Answers from procedures, specifications, historical records — with citations.
Primary fit: all four sub-segments.
Most AI initiatives stall on the same wall: data trapped in systems never built to share. SAP ECC tuned fifteen years. Oracle EBS holding financial truth. MES and SCADA calibrated line by line. Document systems where real knowledge lives.
Deeplinq connects against these systems — not a replacement layer, not a data lake. Connectors reading and writing against SAP ECC and S/4HANA, Oracle EBS, Sage X3, Microsoft Dynamics, MES and SCADA variants, document stores.
Why this works
15+ years of enterprise data integration expertise. Architectural discipline from regulated production systems. Connectors built by people who have operated them in production.
Your legacy is where your operational truth lives. Deeplinq meets it there.
For most Industries workloads, a managed deployment is the fastest path to 90-day value. Deeplinq runs in our cloud, connects to your systems, you produce measurable gains inside the window.
When sovereignty matters — agri-food traceability hosted in-country, distribution workflows with residency clauses, industrial process IP — deeplinq supports sovereign cloud in your region or fully on-premise inside your perimeter. Same platform across all modes.
For the full sovereignty architecture — four deployment modes including air-gapped, model-agnostic bi-category, and evidence-layer mechanics — see /banking-regulated.
You choose the mode that fits the workload.
12 weeks, 4 phases, co-constructed. From customer-side preparation to in-production operations, every phase is mapped — including the prerequisites on your side.
Three-phase sequencing. Scope calibrated against the first signed deployment.
Connector deployment against your in-scope systems. First working agent running end-to-end on a narrow, measurable slice. You see it work on real data by week 4.
Additional agents added against the workflow scope. Accuracy tuning on real operational inputs. Integration with existing tooling. Baseline metrics captured so the 90-day outcome is measured, not asserted.
Production deployment on the scoped workflow. Outcome measurement against the Week 0 baseline. Iteration cycle open — a feedback loop that does not stop at go-live.
The 90-day window is a discipline, not a demo. A pilot without measurable outcomes by day 90 is a pilot that did not work.
The first 90 days are about what deeplinq does inside your organization — connecting your systems, deploying agents against your workflows, measuring what changes.
Over time, the organizations that run on deeplinq will have the option to work with each other through it. A supplier publishing the specifications your operations team needs to query. A distribution partner exchanging demand signals that currently travel through weekly calls. A services firm sourcing an expertise your practice does not keep in-house.
On your terms, when you choose. The platform stays inside your perimeter. The value extends across the chain.