Data & AI services for manufacturers — advise, build, transform, run.
SCIKIQ teams set your data and AI strategy, build the platforms, transform supply chain, plants, quality and the channel, and run what we build — for automotive OEMs and suppliers, other manufacturers and trading & distribution businesses. Our SCIKIQ Data Fabric, pre-built accelerators and a supervised digital workforce of AI agents are how we deliver it faster.
Manufacturing data and AI programmes stall because every one starts from zero.
Siloed ERP, plant, engineering, quality and channel systems, knowledge held as free text and long implementations mean value arrives late — or not at all.
Patterns we found in delivered manufacturing and distribution work — not industry statistics.
1 · No governed data foundation
SAP, MES, PLM, QMS and distributor systems don't agree — master data is duplicated and lineage is unknown.
2 · Services firms rebuild from scratch
Each project re-invents pipelines, models and controls, so timelines and fees grow.
3 · Software alone doesn't change the process
Point products solve one use case and leave integration, data and adoption to the manufacturer.
4 · AI pilots never reach production
Proofs of concept built without data, controls and an operating model stay on the shelf.
Manufacturers are moving from dashboards and copilots to standing agents that watch, quantify and route.
Agents now scan programmes, suppliers, plants and the channel, put a money value on what they find and route it to an owner; people decide.
So what for a manufacturer: the operating model moves from “a person reads the reports” to “agents do the work, a person supervises many agents” — which makes validation, autonomy limits and audit trails the deciding capabilities, especially where quality, safety and recalls are involved.
Capability counts of the SCIKIQ accelerators; demonstration data is modelled or sample data.
A platform that arrives with industry IP and the team to run it — so manufacturers pay for outcomes, not reinvention.
Services
Nine service lines across advise, build, transform and run — delivered by teams who know automotive, plant operations, supply chain and distribution.
SCIKIQ Data Fabric
The governed foundation: the 4C method, 268 data sources, metadata, lineage, data quality and security.
Accelerators
Enterprise Cockpit 360, the Agentic Decision Platform, the SAP + DMS Data Model and the R&D Knowledge Assistant — plus cross-industry accelerators for reconciliation, close and FP&A.
Supervised digital workforce
AI agents that do the routine work end to end, with people approving exceptions and every step logged.
Nine service lines cover the lifecycle, organised the way manufacturers buy them.
Select a stage on the wheel, or start from your role.
Set direction and the rules the data must meet.
Data & AI StrategyMaturity assessment, AI operating model, business case Data Governance & Compliance DataMaster data, traceability, quality & trade-compliance lineageEngineer the platforms and the AI that runs on them.
Data Engineering & Platform ModernisationSAP, MES, PLM, QMS & DMS data on one platform AI & Agentic EngineeringStanding agents, GenAI on documents, grounded assistantsChange how a manufacturing function works, end to end.
Supply Chain & ProcurementSupplier risk, PPAP & certificates, planning Plant Operations, Quality & ReliabilityOEE, warranty & quality, predictive maintenance Commercial, Dealer & DistributionPrimary + secondary sales, schemes, dealer health Aftermarket, Service & WarrantyParts, service cases, warranty recoveryKeep it healthy and improving after go-live.
Managed ServicesDataOps, MLOps & AgentOps under SLAsEvery service line runs on the same framework, accelerators and digital workforce. All services in detail →
A repeatable 4C method turns raw manufacturing data into production-ready capabilities in 30–45 days.
- Step 1 · Week 1–2Connect268 data sources: SAP / ERP, MES and machine data, PLM, QMS and warranty, dealer and distributor systems, and documents; batch and streaming.
- Step 2 · Week 2–4CurateStandardise, cleanse and enrich: MDM, de-duplication and data quality rules.
- Step 3 · Week 3–5ContextualizeMetadata catalog, critical data elements, multi-hop lineage, ownership and policy.
- Step 4 · Week 4–6ConsumeData products, APIs, BI, NLQ GenAI studio and the agents behind each accelerator.
Twelve capability layers give every manufacturer one blueprint — and one way to measure progress.
The same layers we build and score in the maturity assessment. Hover a layer to see what it does.
Runs in your cloud tenancy or on-prem. Code, ontology and agents are handed over — you own what we build. Explore the blueprint →
MIT CISR found enterprises at stages 3–4 perform well above their industry average financially, while those at stages 1–2 perform below it. Source
Manufacturing and cross-industry accelerators mean no service line starts from a blank page.
Pre-built, configurable starting points on the framework. Hover or tap a tile.
Manufacturing accelerators COO · CPO · CFO
Finance & cost CFO
Operations & automation COO
Data Governance & Compliance CDO
Four accelerators turn scattered plant, supplier and channel data into one decision system.
Capability counts; demonstration data is modelled or sample data — capabilities, not client results.
Agentic Decision Platform. An intelligence feed, not a dashboard: every alert has a money impact, an owner and a clock, and value is counted once in a ledger. Illustrative alert shape.
27 agent roles across 8 manufacturing domains — your people supervise the exceptions.
Agents work end to end across R&D, supply chain, plants, quality, maintenance, the channel, aftermarket and finance. Policy decides what goes straight through; people approve the exceptions — and quality, safety and recall decisions always stay with people.
Obsolescence Watcher · L0
Engineering Change Analyst · L1
PPAP & Certificate Chaser · L2
Purchase Order Drafter · L2
Three-Way Match Agent · L3
Capacity Conflict Watcher · L0
Shift Handover Writer · L2
Defect Triage Assistant · L1
Compliance Evidence Collector · L2
Work Order Prioritiser · L1
Spares Planner · L2
Dealer Health Monitor · L0
Scheme & Claim Validator · L2
Quote Assistant · L2
Service Case Assistant · L1
Warranty Recovery Drafter · L2
Value Ledger Keeper · L1
Variance Commentary Writer · L2
Trade Compliance Checker · L1
Click a bar to see the agents in it.
Agent designs from our AI & Agentic Engineering practice — not client results. The live control-room demo runs on banking sample data.
An agent does the legwork on every case; a person makes the call when policy says so.
Illustrative replay: a supplier invoice doesn't match the purchase order and goods receipt. The Three-Way Match Agent works it.
- 1Source systemsA supplier invoice arrives that doesn't match its purchase order and goods receipt — a quantity was part-received and the unit price differs.
- 2Data fabricChange-data capture lands the invoice, PO and goods receipt from SAP MM and FI within minutes, quality-checked, as governed data products.
- 3Metadata & ontologyAll three resolve to the same supplier, part and plant on the golden master record, with lineage back to each source.
- 4ContextPrice agreements, tolerance rules, similar past mismatches and the payables procedure are assembled — only what this analyst may see.
- 5AgentPlans and calls tools through the secure AI gateway:
get_invoicefind_po_and_receiptsget_price_agreement; proposes a match or a supplier query with evidence and a confidence score. - 6PolicyKill switch, autonomy level, confidence ≥ 0.90 and a difference within tolerance decide: straight through, or to a person.
- 7Human gateThe accounts-payable lead reviews the evidence on one screen and approves, edits or rejects the proposal.
- 8ActionA governed, typed action posts the match — or drafts a supplier query or debit note — limited, idempotent and reversible.
- 9AuditEvery step, tool call and decision is recorded; outcomes feed the evaluation that decides whether autonomy can be raised.
Autonomy is set per agent and raised only on evidence — inside hard guardrails.
Confidence threshold
Straight-through only if confidence, amount limit and evidence checks pass.
Amount limits
Purchase orders, credit notes and invoice differences above the limit always go to a person.
Named human owner
Approves, edits or rejects every exception; quality, safety and recall decisions always need a person.
Everything logged
Every plan, tool call and decision; overrides feed evaluation. Personal data masked in prompts.
Kill switch
One control halts every agent at once.
What could a supervised agent squad free up in your business?
Move the sliders to your own volumes. Agents work the cases; people review only the exceptions.
Estimate only, not a quote or a SCIKIQ result. Manual hours = cases × minutes ÷ 60. Supervised hours = cases × (1 − STP share) × review minutes ÷ 60. Hours saved = manual − supervised. FTE = hours saved ÷ 150. Cost saved = hours saved × cost per hour. Supervisor capacity = 150 h × 60 ÷ ((1 − STP share) × review minutes). Excludes platform and run costs.
Faster than services alone, a better fit than software alone.
How the SCIKIQ model compares with the usual ways manufacturers deliver data, AI and agentic automation.
| Criterion | Big-4 / SIservices only | Point productssoftware only | In-house build | SCIKIQplatform + accelerators + delivery |
|---|---|---|---|---|
| Time to production value | 6–12 months | 3–6 months + integration | 12–18 months | 30–45 days |
| Manufacturing data models & domain depth | Generic methods | One use case | Build | Pre-built |
| Reusable data foundation | Rebuilt per project | Vendor-specific | Build | SCIKIQ Data Fabric |
| Ready-made accelerators | Varies | Single product | None | Manufacturing + cross-industry |
| Supervised AI agents in operations | Pilots / PoCs | Copilot features | Build & govern | Agent squads with guardrails & AgentOps |
| Process change & adoption | Yes | Left to the manufacturer | Partial | Yes |
| Run & continuous improvement | Separate contract | Product support | Internal team | Managed services |
| Total cost of ownership | High | Medium–High | Very High | Low |
Big-4 / SI (6–12 months), point products (3–6 months + integration) and in-house builds (12–18 months) are compared on larger screens.
Four phases, each ending with something you keep — and three ways to buy it.
Discovery & Planning
You receive a maturity assessment and target blueprint, with a prioritised roadmap.
Gate: pilot scope signed offAnalysis & Design
Target-state design and ontology, mapped to your controls; agent autonomy agreed with operations, quality and finance.
Gate: design authorityBuild & Deploy
Landing zone as code, pipelines, accelerators and agents configured and tested on real data.
Gate: go-live readinessSupport & Embed
Runbooks, evaluation suites and knowledge transfer to a trained team.
Gate: handover sign-offStaff Augmentation
Architects, data and AI engineers embedded in your teams, under your delivery lead.
Project Delivery
Outcome-based delivery of an accelerator or platform build by a SCIKIQ pod, with phase gates.
Managed Services
We run and improve the platform, models and agents — DataOps, MLOps and AgentOps under SLAs.
A 30–45 day accelerator pilot proves value on one use case before you commit to scale.
Fixed scope, fixed timeline, agreed success criteria — and a scale-up business case at the end.
One accelerator & its agent squad
Typically the decision platform for one risk class, warranty analytics for one product family, or primary + secondary sales for one region, with agents starting at “act with approval”.
2–3 source systems
e.g. SAP, the DMS and price & scheme masters — or warranty claims, service reports and build records.
One business unit
e.g. one plant, one product family or one sales region, with a named owner and SMEs for rules and UAT.
SCIKIQ pod
Engagement lead, data engineer, domain SME, AI / agent engineer.
Success-criteria targets are agreed in week 1.
Three steps take us from this conversation to value in production.
Start small with one accelerator, prove it, then scale on the same framework.
Scoping workshop
Walk through your data landscape and pain points, pick the pilot use case and agree success criteria.
1–2 weeksAccelerator pilot
Deploy one accelerator and its agent squad on the framework against live data, and measure the result.
30–45 daysScale & embed
Roll out across business units and further accelerators through project delivery or managed services.
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