AI and software for supply chain and operations teams, across planning, suppliers, the goods and documents at the gate, inventory, logistics and the plant floor.
Every stage already produces a record. The chain becomes visible when the records are joined and made to agree.
In brief
EigenSpark builds AI systems and the software around them for supply chain and operations teams, end to end. We start with the item and supplier identity work, because nothing above it holds without that. We automate the documents at the gate: the goods receipt, the invoice, the e-way bill and the quality record, matched in one step. We plan demand, inventory and despatch on your own history, score suppliers on their real record, and show cost to serve by customer. Then we hand it to your team to run.
Your systems
Nothing here asks you to replace your ERP or your warehouse system.
Use cases
Identity and documents first, then planning and inventory, then logistics, then the floor.
Invoice, purchase order, goods receipt, e-way bill and quality record matched in one step at the gate, with only the differences raised.
Document Intelligence Engine →Duplicate parts, vendors and units resolved into one identity each, with the evidence for every merge kept so your team can reverse it.
Data Engineering & Platforms →Forecasts by item, channel and location built from your sales, seasonality and promotions, with the driver behind each movement named.
Supply Chain Control Tower →Reorder points and safety stock set on real variability, with slow, ageing and expiring stock raised while it can still be sold.
Supply Chain Control Tower →Every supplier scored on delivered lead time, quantity accuracy, quality rejections and document errors, by item, from your own records.
Supply Chain Control Tower →Loads built and routed against vehicle capacity, delivery windows and cost, with the plan compared against what actually ran.
AI & Data Strategy →Freight bills checked against contracted rates and actual movements, and cost pushed down to the order, the customer and the item.
Data Engineering & Platforms →E-way bills, invoices and vehicle details checked against each other and against validity before a vehicle leaves, and tracked while it moves.
Continuous Compliance Monitor →Sequences built against capacity, changeover cost and material availability, and rebuilt when the floor reports something different.
AI & Data Strategy →Inspection sheets, test certificates and rejection notes read into one record per batch, and linked back to the supplier and the process.
Document Intelligence Engine →Failure risk flagged from the signals your plant already produces, so maintenance is planned into a window you choose.
Predictive Maintenance System →Orders, stock, movements and promises in one place across sites, defined once, so two plants stop reporting different numbers.
Data Engineering & Platforms →How an engagement runs
Fix the item and supplier identity, automate one leg, then hand it over.
Two to three weeks resolving the item master, the supplier master and the unit of measure across your plants and systems, into one identity each. Everything else on this page depends on this.
One leg taken end to end: the documents at the gate, demand planning for one category, or despatch and freight. Small enough to prove in a quarter and measured against your own last one.
Planning parameters, supplier scorecards, match tolerances, routing rules and reorder logic live in a console your own team runs. A new plant, item or supplier does not need us.
Training
Ninety-minute hands-on sessions, run on your own transactions, gate documents and plant data.
Planners and buyers
Forecasting, exception handling and document work practised on your own items and suppliers, so the exercises use the parts you actually buy.
Walk away withA forecast and an exception routine for one category, built in the session.
Plant and warehouse teams
Querying transaction and historian data directly, reading the gate documents, and automating the reports rebuilt at every shift change.
Walk away withOne shift report automated, and a gate document read in without anyone retyping it.
The COO and supply chain head
How to read an accuracy claim, why master data decides the outcome, and where a model belongs in a decision that stops a line.
Walk away withA standard for accepting a forecast, and what to fix in the master data first.
Operations teams usually take Data & Analytics and Enterprise Systems, with AI & Machine Learning for the planners. The full catalogue is in Training & Enablement.
The engineering half
Four layers of ordinary engineering. The first one is the reason most planning projects fail.
A forecast is arithmetic once an item means the same thing everywhere. Until then it is arithmetic on three different products with the same name.
Your ERP stays the system of record. Most of the effort is everything at the edges: the gate, the warehouse, the transporter, the supplier and the floor.
Built for the person deciding under time pressure, with the exception, the document and the rule in one view, so a call can be made at the gate.
Defined once and used everywhere, so the plant report, the supply chain review and the ledger 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 planning team that can query its own transaction data sets better parameters.
What we offer operations teams
Most teams start with the documents at the gate or one planning category, and use three of the four.
Where an operations programme starts.
Where a single workflow starts.
AI tracks, and the foundations under them.
Where the specifics change.
The specifics change by sector. See how this lands in Manufacturing or Retail & CPG.
Security and controls
The planner decides, every exception opens onto its document, and no order places itself.
Forecasts, reorder proposals, load plans and receipt exceptions go to the person accountable for the call, with the working visible and the override kept.
A quantity difference, a rate variance or a rejected receipt links back to the invoice, the e-way bill or the inspection sheet it came from.
No purchase order is raised, no payment is released and no despatch is authorised by a system. It proposes and evidences, and your approval chain acts.
Safety stock, reorder logic, match tolerances, scorecard weights and routing rules are managed by your own team. A changed parameter does not need a release.
FAQs
End to end, across planning, sourcing documents, the gate, the warehouse, the logistics leg and the plant floor. Some of that is AI, a great deal of it is integration and data engineering, and some of it is process work with your own team. What we scope for you depends on where your chain actually loses money, which is what the assessment establishes.
No. Your ERP stays the system of record and the WMS keeps running the warehouse. The work is the identity layer underneath them, the documents at the edges, and one view across plants that the ERP was never asked to produce. Writes go back through your own interfaces.
No, that is the first piece of work. Every planning project that skips it produces confident numbers about the wrong items. We resolve items, suppliers, locations and units, keep the evidence for every merge so your team can reverse it, and hand you the list of what was ambiguous. It usually takes two to three weeks.
Yes. Most detentions come from a mismatch between the invoice, the e-way bill and the goods, or from validity running out in transit. We check all three against each other before the vehicle leaves, hold anything inconsistent, and track validity while the vehicle moves. The rule set is configuration your team edits.
We will not quote a number before seeing your history. What we do first is measure your current accuracy by item and by category, because most teams do not have that baseline, and then measure any model against your own baseline. Some categories improve a great deal and some barely move, and we report both.
It depends on the use case, and it is a question the assessment answers with your engineers, and we will not answer it in advance. Most plants already produce far more signal than they use, in historians, quality systems, maintenance logs and the ERP. We start there, and where a use case genuinely needs something more, we scope it with you.
No, and the parts that are not are usually what makes the AI work. Resolving the item master, connecting the weighbridge, reading the gate documents and building the planning engine 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.
The document work at the gate pays for itself at a single site with a busy gate, because the saving is per document. The planning and cost to serve work needs enough items, sites and orders for the pooling to be hiding something, which in practice means two or more locations or a few thousand active items.
Related
The products this ships as, the services under them, and the industries where the specifics change.
Demand, inventory and supplier reliability.
→ ProductFailure risk from the signals a plant already produces.
→ FunctionThe sourcing and award decisions ahead of the chain.
→ IndustryThe shop floor view of the same work.
→ IndustryThe channel view, where the demand signal comes from.
→ PillarThe engineering side, for systems operations runs.
→Send the receipts, the matching invoices and an export of your item and supplier masters. We call you within 48 hours and go through how many duplicates you are carrying, what share of receipts matched cleanly, and what the first automation would be.
Talk to us