AI and software for finance and accounting teams, across the close, payables, receivables, tax and reporting.
Nobody signs a number they have not checked. The system does the assembly and shows its working.
In brief
EigenSpark builds AI systems and the software around them for finance and accounting teams. We automate the reconciliations that hold up the close: three-way matching, banks, intercompany, GST credit and vendor statements. We draft the accruals, the commentary and the board pack from the same numbers. We track the compliance clocks that carry a real cost, from the 45-day payment rule to the credit claim deadline. Then we hand it to your team to run.
Your systems
Nothing here asks you to replace your ERP or your accounting software.
Use cases
The close first, then tax and compliance, then working capital, then reporting and the back office.
Invoices matched against orders and goods receipts, with only the exceptions passed to a person, each carrying the document and the difference.
Document Intelligence Engine →Statements read against the ledger daily, with matches cleared and breaks explained by probable cause, so month-end starts from a short list.
Data Engineering & Platforms →Open orders, receipts, contracts and last period’s pattern read into draft accruals, each with its basis shown for the controller to accept or change.
AI & Data Strategy →Purchase records matched against GSTR-2B and what you accepted in IMS, with mismatches and reversal risk flagged in the week they arise.
Continuous Compliance Monitor →Your vendor master classified against Udyam registration, ageing tracked on the right clock per vendor, and the disallowance exposure shown while you can act.
Continuous Compliance Monitor →Approval chains, edit logs and journal entries checked against your own control design, with exceptions listed as they happen.
Continuous Compliance Monitor →Claims checked against your policy and the bills attached to them, with only the doubtful ones passed to a reviewer and the rule each breaches named.
Agentic AI →Invoices aged by customer and by reason, with follow-up drafted in your own tone and ordered by what is actually collectable, for a person to send.
Agentic AI →Receivables, payables, committed spend and seasonality read into a forecast by week, with the assumptions visible so treasury can argue with it.
AI & Data Strategy →Movements explained against budget and prior period with the driver behind each one named, assembled into the pack from the ledger’s own numbers.
Generative AI →Customer and supplier contracts read into a register of price terms, escalation, penalties and renewal dates, with every change from your standard tagged.
Contract Risk Analyser →Invoices, orders, receipts, challans and bank advices read into your accounting system, with anything doubtful passed to a person.
Document Intelligence Engine →How an engagement runs
Get the reconciliations working, automate one task, then hand it over.
Two to three weeks connecting your ledger, banks, GST data and vendor statements into one reconciled view, with every break listed and explained. Everything else on this page depends on this.
One task taken end to end: three-way matching, bank reconciliation, or the GST credit check. Small enough to prove in a quarter and specific enough to measure against last month.
Match tolerances, policy rules, vendor classifications and account mappings live in a console your own team runs. A new bank, a new vendor or a changed rule does not need us.
Training
Ninety-minute hands-on sessions, run on your own ledger, statements and close calendar.
The finance team
Prompting, drafting and checking, practised on your own reconciliations, schedules and commentary. The exercises use last month’s numbers.
Walk away withA drafted variance commentary and the routine for checking it, ready for the next close.
Analysts and MIS
SQL against the ledger and the subledgers, the schedules that feed the pack, and the parts that get rebuilt by hand every month.
Walk away withOne monthly schedule automated during the session, running on your own data.
The CFO and controllers
Where a model can sit inside a control environment, what evidence an auditor asks for, and how to read a vendor’s accuracy claim.
Walk away withA written standard for approving AI in finance, and the questions to put to a vendor.
Finance teams usually take Data & Analytics and AI & Machine Learning, with Leadership & Executive Readiness for the people who sign off. The full catalogue is in Training & Enablement.
The engineering half
Four layers of ordinary engineering. The first one decides whether anything above it reconciles.
Nothing here works until a vendor and an account mean the same thing in every system. It is the least interesting layer and it decides the outcome.
Your ERP and your accounting software stay the systems of record. Most of the effort is everything that arrives from outside them.
Built for the person who signs the number, with the working shown so a match or an accrual can be argued with before it is accepted.
Defined once and used everywhere, so the management pack and the statutory schedule 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 ledger cannot specify a model either.
What we offer finance teams
Most teams start with a reconciliation or a document workflow, and use three of the four.
Where a finance 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 Healthcare.
Security and controls
The controller signs, every figure traces to a document, and nothing pays itself.
Matches, accruals, commentary and payment proposals go to the person accountable for the number, with the working visible and the override recorded.
A match, an accrual or a variance line links back to the invoice, the statement or the ledger entry it came from, on the date it arrived.
No payment is released, no journal is posted and no return is filed by a system. Finance approves, inside the approval chain you already run.
Match tolerances, policy rules, vendor classifications and mappings are managed by your own team in a console. A changed rule does not need a release.
FAQs
We will not quote a number before seeing your close. What we do first is measure it: which tasks sit on the critical path, how many hours go into each reconciliation, and where the waiting happens. The reconciliation hours are the part that moves most, because that is the part a system can do while people sleep. You get the baseline before anything is built, so the improvement is measured against your own baseline.
No. SAP, Oracle, Tally or whatever you run stays the system of record. Most of the work here is getting it to agree with the bank, the vendor statement and the GST portal, and giving finance a place to work the exceptions. Postings go back into your own system through its own interfaces.
Yes, and it is one of the more useful places to start. Input tax credit is now tied to the records you accept, or are deemed to accept, in IMS. We match your purchase records against GSTR-2B and the IMS position, report what is missing, what is mismatched and what credit is at risk of reversal, and track the claim deadline. The rule set is held as configuration your tax team edits, because these notifications keep coming.
No. We prepare and reconcile, and we show the working. Filing stays with your team and your tax consultants, and so does the signature on it. The same applies to journals and payments: the system proposes and evidences, and a person in your approval chain acts.
Yes. A smaller finance team on Tally or Zoho usually sees the payables, receivables and GST credit work land fastest, because the data is in one place and the manual effort is concentrated. The reconciliation and document intake work is the same shape whether the ledger is Tally or SAP.
No, and the parts that are not are usually what makes the AI work. Connecting the bank feed, cleaning the vendor master, mapping the chart of accounts and building the rule 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.
In your own cloud tenancy. Ledger, banking and vendor data is commercially sensitive and it stays inside your boundary. Where a hosted model is used for text work such as drafting commentary, what is sent to it is agreed and recorded before anything is built.
The document intake, GST credit and payment exposure work pays for itself at almost any size, because the obligations carry no exemption for a small company. The close automation and forecasting work needs enough transaction volume and enough people spending time on reconciliation to be worth doing, which in practice means a finance team of five or more.
Related
The products this ships as, the service under them, and the industries where the specifics change.
Tax, policy and control checks against a live rule set.
→ ProductInvoices, receipts and advices read into your ledger.
→ ServiceThe reconciliation layer everything else sits on.
→ IndustryWhere the same work meets PLI claims and supplier documents.
→ IndustryA smaller finance team, and one job at a time.
→ PillarThe engineering side, for systems finance runs on.
→Send the checklist, plus one bank statement and the ledger for the same period. We call you within 48 hours and go through what reconciles on its own, where the breaks are, and what taking a week out of the close would involve.
Talk to us