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Private preview · EMEA·AI agents for operations that can't pause for cloud uploads.

Measurable operational gains, in 90 days.

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.

SAP ECCS/4HANAOracle EBSSage X3Microsoft DynamicsMESSCADAWMSTMSHACCP archivesLIMSDMSPIMEDIERTMSTelematics
§ 01 — Your systems, orchestrated

Your systems, orchestrated.

Industrial workflows run on systems that don't talk to each other. deeplinq orchestrates across your existing stack — agents read across silos, agents write back with audit. Nothing leaves your perimeter.

Your systems01 / 03

Legacy stack

  • ERP · Core systems
  • MES · SCADA
  • WMS · TMS
  • CRM
  • Document stores
  • Spreadsheets · files
deeplinqInside your perimeter

Orchestration layer

  • Connectors
  • RAG engine
  • Policy gate
  • Agents
Outcomes03 / 03

Business outcomes

  • Cross-system answers
  • Decisions accelerated
  • Compliance evidence
  • Audit-ready trails
  • Process resilience
Six categories of legacy systems flow into deeplinq's orchestration layer and out into business outcomes — everything inside your perimeter, every step traceable.
The pitch

AI that works against the systems you already run.

Platform infrastructure across your ERP, MES, and document systems — where your team deploys AI agents against concrete outcomes, without a stack rebuild.

§ 02 — Who this works for

Who this works for

Industries is the surface for organizations sharing four signals.

  • SIGNAL · 01

    You carry valuable data in systems built over decades.

    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.

  • SIGNAL · 02

    You have tried AI, and integration killed the pilot.

    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.

  • SIGNAL · 03

    You are under pressure to ship AI capability.

    From the board, operations, customers asking questions your competitors already answer. The pressure is real. The path forward is not obvious.

  • SIGNAL · 04

    Your value chain reaches beyond your four walls.

    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.

§ 03 — Where this applies

Where this applies

Four sub-segments share the same problem: valuable data trapped in legacy systems, AI stalled on integration. Angles differ.

  • SEG · 01 · Industrial & manufacturing

    Industrial & manufacturing

    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.

  • SEG · 02 · Agri-food

    Agri-food

    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.

    Jump to agri-food deep-dive ↓
  • SEG · 03 · Distribution

    Distribution

    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.

  • SEG · 04 · Transportation

    Transportation

    Rail operations, logistics and last-mile run on systems that don't talk — SCADA, ERTMS, TMS, WMS, ERP, telematics, CRM. Visibility is partial, reconciliation is manual.

    Connectors across rail, logistics and last-mile stacks. Agents surface sourced context during incidents, harmonize supply-chain state, and feed CSRD reporting against your own data — inside your perimeter.

Agri-food operations

Deployed against the data you already run on.

HACCP records, quality archives, supplier files, ERP purchasing — connected to AI agents that work on your production reality, with measurable operational gains inside a 90-day horizon.

How this works, in practice

The four workflows where AI earns its 90 days

Agri-food operations generate data no two systems agree on: HACCP and CAPA on one side, supplier certificates and logistics on another, quality inspection and operator reports on the floor, procedures across site-by-site practice. deeplinq organizes this into four workflow families — HACCP and quality document intelligence, supplier and supply chain intelligence, quality operations, specification and procedure lookup.

Ranges reflect pilots underway with design partners. Case studies as partners authorize.

The sections below open each workflow: what deeplinq reads, what agents do, what you measure.

HACCP and quality document intelligence

Your HACCP documentation is a living archive. CAPA records, supplier certificates, inspection reports, deviation logs — scattered across document management, shared drives, site archives, and legacy quality software each site configured differently. The institutional knowledge is in there. It has not been queryable in practice.

deeplinq deploys agents against this archive. Ask in plain language — "show every CAPA related to supplier X in the last 18 months", "which supplier certificates expire before the next audit window" — and receive cited answers a quality manager can verify.

Compliance gaps surface before the audit does. Missing signatures on a CAPA trail. A supplier certificate renewed with a non-matching scope. A deviation logged without the follow-up inspection attached. The agents read what is there and flag what is not.

deeplinq does not certify that your HACCP posture will pass an audit — no platform can. What it does is make the gaps visible with enough lead time to close them. The audit-readiness work stays with your quality team. The time they spend looking for records becomes time they spend on the gaps.

Supplier and supply chain intelligence

Supplier files. Logistics feeds. ERP purchasing. Supplier certificates and audit trails in quality archive. Harvest reports, transport documents, procurement contracts. Most agri-food groups open four to six systems and reconcile by hand under time pressure a disruption imposes.

deeplinq reads across these systems against your own data. Agents pull procurement history from ERP, cross-reference certificate status, match logistics signals against delivery windows — and surface the pattern that matters: a supplier whose certificate no longer covers a product you are still purchasing, a logistics route degrading faster than the weekly report shows, a contract renewal with unflagged pricing drift.

Disruption surfaces earlier. A harvest delay, a port closure, a supplier audit finding — signals exist in your systems before they reach the Monday meeting. Agents read them at the pace the disruption moves.

Multi-tier visibility stays inside your perimeter. deeplinq works against the supplier data your organization already holds — contracts, certificates, audit histories, delivery records, purchasing trails. Your supply chain intelligence is built from your own archive, against your own systems. The platform does not ask your suppliers to integrate, federate, or exchange. What you already have, activated.

Quality operations on your production lines

Inspection data from QA. In-line measurements from instrumented equipment. Operator reports captured at end of shift. Historical defect signatures going back years, site by site. Agri-food quality happens on the floor — and the floor produces more data than any dashboard reads.

deeplinq deploys agents against this operational data. They pattern-match current inspection results against historical defect signatures specific to each line, product, season. Drift surfaces as signal before customer complaint. An unusual in-line reading correlates with a supplier batch change three days earlier. A cluster of end-of-shift operator observations matches a defect pattern Site A resolved four years ago.

Multi-site replication compounds the value. A pattern resolved on one site becomes actionable knowledge on others without a six-month rollout. Agents read across site archives where the group has permitted shared access — Site A resolution applied to Site B early signal, with originating context preserved.

Customer-impact drift surfaces upstream. A microbiological trend in raw material supply. A temperature excursion whose shelf-life effect is subtle. A specification edge-case correlating with a recurring reject rate downstream. The patterns are already in your data. deeplinq reads them before the complaint arrives.

Specification and procedure lookup, across sites

Operators on a production line. QA teams preparing for inspection. Supplier-facing teams answering certificate questions. A new site manager onboarding. In most agri-food groups, the answer to "how does this work here" lives in procedures written ten years ago, revised three times, stored in two systems, and read in practice from a binder on a shelf.

deeplinq deploys plain-language queries against the procedure library, technical specifications, historical operating records, and site-level documentation. An operator asks "what is the cleaning sequence for line 4 after a product changeover" and receives the current procedure with citation to the document of record. A QA analyst asks "what was the specification range for parameter X on product Y between 2019 and 2023" and receives the answer grounded in versioned specification history.

Multi-site is where this becomes a coordination instrument. Sites that inherited procedures from different acquisitions, different eras, different local practices — agents read across the group's documentation with the permissions the group sets. Knowledge that was site-local becomes queryable group-wide.

Deployment modes

What the data requires, when it requires it

For many agri-food workloads, a managed deployment is the fastest path to the 90-day outcome. For others, the data sets the constraint.

Traceability records regulators expect hosted in-country. Recipe and process IP — formulations, kill-step parameters, aging protocols, proprietary method descriptions — that constitute competitive moat and belong on no vendor's cloud. Supplier data carrying contractual residency clauses your legal team enforces.

deeplinq supports three deployment modes — managed, private cloud in your region, or on-premise inside your perimeter. Same platform across all three. Air-gapped configurations available where the data profile requires it.

The data shapes where the platform runs. Not the other way around.

How this starts

Where this starts

Agri-food operations reward the discipline of starting narrow. One workflow family. One site, or two. A 90-day horizon measured against a baseline captured before the work begins. What gets proved on that scope is what scales to the rest.

The four workflows above — HACCP and quality document intelligence, supplier and supply chain intelligence, quality operations, specification and procedure lookup — map to the systems and data you already have. The question is which one earns the first 90 days in your operation.

§ 04 — What you measure at 90 days

What you measure at 90 days

Working hypothesis. Every number below is an estimate — not a promise.

  • 01Metric · 01

    Throughput on bounded workflows

    The workflows you chose first — document extraction, report preparation, request triage — measurably faster.

    Pilots show meaningful cycle-time reduction on the bounded workflows in scope.

  • 02Metric · 02

    Quality and defect signal

    Agents reading production data, quality archives, supplier inputs — surfacing patterns dashboards miss. Fewer late defects.

    Pilots show fewer late defects on the workflows in scope.

  • 03Metric · 03

    Document operations at scale

    HACCP records, technical specifications, contracts, operating procedures. Queried in plain language, citations to source.

    Pilots show substantial time reduction on document lookup and synthesis.

  • 04Metric · 04

    Operational cost envelope

    Throughput and document-ops gains compound into measurable operating cost reduction on scoped workflows.

    Pilots show measurable operating cost reduction on first deployment's scope.

Ranges based on design-partner pilots underway. Case studies as partners authorize.

§ 05 — What agents actually do

What agents actually do

Seven operational use cases across four sub-segments. Each runs against existing systems — no data migration, no rebuild.

  • 01Use case

    Predictive maintenance on existing MES data

    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.

  • 02Use case

    HACCP and quality document intelligence

    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.

  • 03Use case

    Multi-vendor catalog orchestration

    Reconcile catalogs across dozens of suppliers. Detect pricing drift, missing specifications, inconsistent attributes.

    Primary fit: distribution. Secondary: industrial procurement.

  • 04Use case

    Expertise augmentation on knowledge work

    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.

  • 05Use case

    Supply chain sensing

    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.

  • 06Use case

    Quality operations across production lines

    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.

  • 07Use case

    Specification and procedure lookup

    Operators, technicians, field teams ask in plain language. Answers from procedures, specifications, historical records — with citations.

    Primary fit: all four sub-segments.

§ 06 — Legacy integration

Your legacy is an asset. Not a blocker.

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.

§ 07 — Deployment & residency

Deployment choice, when your use case requires it

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.

§ 08 — How 90 days unfolds

How 90 days unfolds

Three-phase sequencing. Scope calibrated against the first signed deployment.

  1. 01Phase 01

    Weeks 0–4 — Connect, extract, first agent

    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.

  2. 02Phase 02

    Weeks 4–8 — Expand, refine, measure

    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.

  3. 03Phase 03

    Weeks 8–12 — Production, measure, iterate

    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.

§ 09 — What's next

What comes next

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.