We cut downtime and scrap, automate quality and supplier paperwork, and plan production on the data your plant already records.
Every row is a system you already run. That is where we start, and it is usually enough to prove the case.
In brief
EigenSpark builds AI systems and the software around them for discrete and process manufacturers, component suppliers and industrial product companies. We reduce downtime, scrap and energy cost using data your machines and operators already produce. We automate the paperwork around quality, suppliers and exports. And we run planning, procurement and supply chain work end to end, so a decision on the floor reaches the order book.
What we cover
Claims, certifications and export declarations, assembled from your own systems.
The starting position
A pilot that never scaled, free-text reason codes, and three systems that disagree about the same shift.
A model trained on one line, one product and one operator’s habits does not move to the next plant. Scaling has to be designed in on day one.
Twelve spellings of the same fault make the analysis worthless. Fixing what the operator types comes first, and most proposals skip it.
Each department trusts its own number. Anything quoted to a customer or an auditor has to come from one reconciled source.
Test certificates and PPAP files arrive in a dozen formats. Incoming inspection becomes people copying numbers into a spreadsheet.
How an engagement runs
Read what the plant already records, prove it on one line, then make the second plant cheap.
Two to three weeks inside your MES, ERP, quality register and maintenance logs to find what is actually there, how clean it is, and which use cases the existing data can carry today. The answer is usually more than the plant expects and less than a vendor promises.
A single use case built end to end on one line and one product family, with the people who will use it in the room. It runs against live data, in parallel with the current process, until the plant trusts the number.
Ingestion, reconciliation, the reason-code taxonomy and the rule engine are shared. The second line and the second plant are configuration, and your own team does the configuring.
Use cases
Quality and production, then maintenance and energy, then supply chain, then the back office.
Inspection results, defect codes, rework and scrap read against line, shift, batch, machine and supplier lot, so the recurring cause is ranked by what it costs.
AI & Data Strategy →Test certificates, PPAP files and certificates of analysis read on arrival and checked against your specification, with anything out of tolerance flagged.
Document Intelligence Engine →Machine logs and operator entries reconciled into one availability and performance figure, with free-text downtime reasons grouped into a taxonomy you control.
Data Engineering & Platforms →Whatever your equipment already emits, read alongside breakdown history and spares issue, to catch the pattern before a failure. Work orders drafted for you.
Predictive Maintenance System →Real consumption, production plan and lead time read into a forecast per item, with slow-moving stock surfaced and reorder points a planner can change.
AI & Data Strategy →Meter data allocated to lines, shifts and product families, so energy cost sits against the part that consumed it and a tariff change can be argued with a number.
Data Engineering & Platforms →Order history, dealer offtake, seasonality and open pipeline read into a forecast by product and region, with the assumptions visible so planning can push back.
AI & Data Strategy →Enquiries and customer specifications read against your capability, routing and cost history, and returned as a draft quotation with the margin shown.
Generative AI →Supply agreements read into a record of price escalation, delivery, penalty and quality clauses, with every change from your standard tagged by severity.
Contract Risk Analyser →Claims and declarations assembled from source systems: sales from invoices, investment from the asset register, conformity from test records, each with its source.
Continuous Compliance Monitor →Claims, dealer notes and service reports read for the failure behind them, grouped by part, batch and production window, while it is still a warranty cost.
Agentic AI →Customer drawings and specifications compared against the last revision or your own standard, returned as a difference report with the field and page recorded.
Document Intelligence Engine →The engineering half
Four layers of ordinary engineering. They decide what the second plant costs you.
We read what your equipment and your operators already record. Instrumentation and vision on physical equipment are a specialist trade and we partner for them.
The systems of record stay. Most of the effort in a manufacturing project is getting them to agree with each other.
Built for a shop floor terminal and a planner's desk, with a review step before any recommendation is acted on.
Defined once and used everywhere, so the plant report, the customer scorecard and the claim cannot disagree. Your team runs and owns it.
We also run the training that goes with it: SQL and data engineering, cloud, enterprise systems and cybersecurity, alongside the AI tracks. A team that cannot query its own data cannot specify a model either.
What we offer manufacturing teams
Most manufacturers start with one line or one paperwork problem, and use three of the four.
Where a multi-plant programme starts.
Where a single workflow starts.
AI tracks, and the foundations under them.
The teams that own this work.
Working in a specific function? See how we help Supply Chain & Operations teams.
Security and controls
A person accepts the number, every figure traces to its system, and the rules stay yours.
Inspection results, forecasts and maintenance recommendations go to the engineer, the planner or the quality head before they count. The system proposes and evidences; the plant decides.
An OEE number, a claim line or a defect rate links back to the record it came from, in the MES, the ERP or the quality register, on the shift it was captured.
Reason codes, defect categories, tolerances and reorder logic are managed in a console by your engineers. A new product family or a changed specification does not need a release.
Shop floor screens are built to hold their state through a link outage and synchronise when it returns, because a plant does not stop for connectivity.
FAQs
No, and we will say so in the first meeting. Vision systems on a moving line, instrumentation and retrofit sensing are a specialist trade with specialist vendors, and the integration work is mechanical as much as it is software. We build the analytics, the integration and the software on top, and we work alongside whoever supplies the hardware.
Both, in that order, inside the same engagement. The first phase reads what your MES, ERP, quality register and maintenance logs actually hold and scores it. Some use cases run on that data today; others need the capture fixed first, and we say which is which before you commit to anything. Improving capture is usually a taxonomy and interface change, which is a software job.
Because the second line is designed for before the first one ships. The ingestion, the reconciliation, the reason-code taxonomy and the rules are built as shared, configurable layers, and your team does the configuring. A model tuned to one line, one product and one operator's labelling habits is the thing that does not travel, so we do not build that.
No. Both stay the systems of record. Most of the work here is getting them to agree with each other and with the quality register, so that a number quoted to a customer, a regulator or a PLI claim comes from one reconciled source.
Yes, on the evidence side. We build the data path that assembles a claim or a declaration from your invoice data, asset register, bill of materials and test records, with every figure carrying the document it came from. We do not certify, audit or sign; that stays with your auditors, your certification body and the officers who hold it.
Scoping runs two to three weeks inside your systems. A first use case on one line and one product family is typically a few months to a production-grade result, including a parallel run against the existing process. The second use case is materially cheaper because the ingestion and reconciliation layers already exist.
No, and the parts that are not are usually what makes the AI work. Integration, reconciliation, the data model and the shop floor screens are ordinary engineering, and they are most of the effort. The same is true of the training: alongside the AI tracks we run SQL, data engineering, cloud, enterprise systems and cybersecurity.
In your tenancy, or on plant hardware where the line cannot depend on a link. Shop floor screens are built to hold state through an outage and synchronise when connectivity returns. Where a hosted model is used at all, what is sent to it is agreed and recorded before build.
Related
The service this ships as, the two products behind it, and the teams that own it.
The MES, ERP and quality data under every number.
→ ProductFailure patterns from the signals you already record.
→ ProductSupplier certificates, drawings and specifications.
→ FunctionThe function that owns planning, stores and despatch.
→ IndustryThe bulk material side, and the despatch chain.
→ PillarThe engineering side, for systems that run a plant.
→A downtime log, a quality register export, or a folder of supplier certificates. We call you within 48 hours and go through what the data can already carry, what the capture gaps are, and what a first use case would take.
Talk to us