Aissistor technology for NetSuite: five roles in one box
Stop bleeding time, money and opportunity on a $1,000,000/yr NetSuite overhead stack.
A typical NetSuite environment carries an administrator, a developer, a business analyst, a support specialist and a quality lead: roughly $1,000,000 a year fully loaded, modeled. Aissistor puts all five roles into one governed box at a modeled 20% to 80% lower cost, and repurposes your existing employees instead of replacing them.
Warehouse barcodes3:1 QR-code consolidationmulti-site distribution warehouse (archetype)Measured, consulting engagement
Item master80% reduction in wasteful SKUsmulti-entity distributor (archetype)Measured, consulting engagement
Accounts payable$800k/yr saved on AP costmulti-subsidiary operating company (archetype)Measured, consulting engagement
The $1M five-role calculator
What the five roles cost today, and what Aissistor absorbs
Tick the roles you staff, edit the loaded costs, choose the compression tier and enter revenue. The outputs are arithmetic on your inputs: modeled, never a quote or a promise. The published range, $200k to $800k a year on the default stack, is what this arithmetic returns between 20% and 80%.
Role by role
Five roles, one governed box, and what each person does instead
The costs are the modeled defaults from the calculator; the hours are the routine queue work that moves into the box, 55-81% of a 2,080-hour year, modeled. The person stays; the queue moves.
Role
Loaded cost, modeled
What the role spends its time on
What Aissistor absorbs
What the person does instead
NetSuite AdministratorStays on the team
$170,000 1,450 hrs/yr
User provisioning, role and permission changes, saved-search tweaks, form cleanup and the daily ticket triage.
0-4 Role Risk Index audits, saved searches and SuiteQL generated on request, form and field governance checked against the schema contract.
The internal ops lead moves from password resets and search building to vendor and customer service levels.
Senior SuiteScript DeveloperStays on the team
$240,000 1,680 hrs/yr
SuiteScript 1.0 to 2.1 refactoring, brittle User Event scripts, RESTlet maintenance and middleware debugging.
Single Entry Point router architecture, a governed MCP tool layer and production code shipped in two-week sprints.
Agency change orders stop; in-house technical staff move to product work instead of patching scripts.
NetSuite Business AnalystStays on the team
$190,000 1,520 hrs/yr
Translating executive questions into custom reports, chasing subledger variances and manual Excel reconciliations.
Chat with NetSuite: plain-English questions answered from live SuiteQL across GL, inventory, AR/AP and multi-entity consolidations.
Finance and FP&A analysts stop compiling spreadsheets and spend the time on margin.
NetSuite Support SpecialistStays on the team
$150,000 1,600 hrs/yr
Manual record creation, CSV upload error fixing, PDF invoice re-keying, warehouse scanner and scale troubleshooting.
Governed record creation and the OCR document-to-ledger pipeline, posting validated transactions after a person approves the draft.
Customer service and AP/AR clerks move to account management and cash collection.
Quality (QA), Release and Turnover DragStays on the team
$250,000 1,150 hrs/yr
A QA and release-testing lead, recruiting fees, key-person turnover and the semi-annual NetSuite release regressions.
Mandatory-field schema contracts, sandbox-to-production regression checks before each release, and SOP memory the board can read.
Key-person risk falls as every business process is codified into governed, auditable corporate memory.
Five roles
$1,000,000 7,400 hrs/yr
Routine queues across admin, development, analysis, support and QA
One governed box: AI reads freely, writes only through a person
Repurposed, not replaced
Hours and costs are modeled defaults and sum to the figures in the hero; the arithmetic is reproduced in the calculator so you can replace every figure with your own.
Two tracks, one promise
Clean data plugs in; messy data gets clean processes first
AI fails in ERP when a chatbot is bolted onto broken item masters and un-reconciled clearing accounts. We meet your environment where it is today.
Track A: good data and processes
We have good data: plug Aissistor in now.
Your chart of accounts, SKUs and core transaction workflows are structured. You want to query NetSuite in plain English, create governed records and stop manual PDF entry this month.
Fourteen days from read-only audit to Aissistor in production
Days 1-3: governed connection
0-4 Role Risk Index and schema handshake
Aissistor connects through OAuth 2.0 PKCE and MCP tooling, maps your mandatory custom fields and locks role-level segregation-of-duties guardrails before it reads a row.
Days 4-9: live activation
Chat, record creation and the OCR pipeline
Executives ask questions answered from live SuiteQL; operations teams draft validated sales orders, purchase orders and vendor bills from documents and chat, each approved by a person.
Days 10-14: repurposing handoff
The compression tier you chose
Routine admin, reporting and data-entry queues move into the box; your staff move to the exceptions, customers and margin work the queues were crowding out.
We do not have good data: build clean processes first.
Your tenant has duplicate SKUs, forty-plus colliding scripts, spreadsheet workarounds or un-integrated acquisitions. We re-architect clean, deterministic business processes first, then switch Aissistor on.
Governed logic before an intelligent surface
Phase 1: the 48-72 hour read
Forensic script, SKU and GL diagnostic
The AI technology diligence read scans every script deployment, orphaned custom field, duplicate item and entity record and clearing-account leak, without weeks of staff interviews.
Phase 2: process re-architecture
Single Entry Point and schema cleanse
Fixed-fee two-week sprints collapse colliding User Event scripts into a Single Entry Point router, deduplicate SKUs and BOMs (881 to 315 items on one measured engagement) and codify clean SOPs.
Phase 3: Aissistor on top
Chat, record creation and OCR on a clean ledger
With deterministic processes locked in, Aissistor answers from rows it cites, drafts records that pass the schema contract, and writes only through a person.
Pick the outcome you came in for, then tell us whether your data is good. Each path ends in its own next step.
Outcome 01: Attrition
A NetSuite administrator, developer or analyst gives notice, and the routine queue they carried has no second person.
We have good data
Absorb the routine queue before the next resignation.
Your records are structured and most of what the departing person does is routine: tickets, saved searches, re-keying and reports. Aissistor takes that queue in fourteen days, so a resignation becomes a handoff instead of a hiring emergency, and the people who stay move to the exceptions the queue was crowding out.
Fourteen days to a queue that does not depend on one person
Days 1-3: governed connection
Map the queue to the role
The 0-4 Role Risk Index records what each role touches and which permissions it holds, so the work is defined before anyone hands in notice.
Days 4-9: live activation
The routine tickets move into the box
Chat answers the report requests, governed record creation takes the re-keying and OCR takes the documents, each write approved by a person.
Days 10-14: repurposing handoff
Backfill on the exceptions, not the queue
What remains of the role is written down as runbooks; the hiring decision is made on the exception work that is left, not on the queue that moved.
Read the tenant before the person who understands it leaves.
When the departing person is also the one who knows where the colliding scripts and spreadsheet workarounds live, a tool bolted on top would guess. The 48-72 hour read captures the tenant as it is, from access; the foundation sprint turns the workarounds into deterministic processes that depend on no one; then Aissistor takes the queue.
Governed logic before the handover, Aissistor after it
Phase 1: the 48-72 hour read
Inventory the knowledge that is about to walk
Every script deployment, permission and workaround is read from the tenant and scored on the 0-4 Role Risk Index, without weeks of interviews with people on their way out.
Phase 2: process re-architecture
Workarounds become code and SOPs
Fixed-fee two-week sprints collapse the colliding scripts into a Single Entry Point router and write the SOPs the next person reads on day one.
Phase 3: Aissistor on top
The queue moves; the hire is scoped on what is left
With the processes deterministic, the routine queue moves into the box and the replacement role is scoped on exceptions and judgment.
Undocumented scripts, hard-coded internal IDs and workarounds that only one person can explain.
We have good data
Corporate memory, written while the knowledge is still in the building.
Your processes are sound; they live in people rather than in documents. Aissistor connects in fourteen days and the handoff phase codifies every script, permission and integration into runbooks that the next administrator, an auditor or a buyer can read, without pulling your team into interview rooms.
Fourteen days to a tenant that explains itself
Days 1-3: governed connection
Inventory what exists
The 0-4 Role Risk Index and the schema handshake list every script, role, custom field and integration, and who owns each.
Days 4-9: live activation
Every answer cites its rows
Chat answers from live SuiteQL and names the transactions it read; drafts carry the document and the rule that produced them, so how a question gets answered becomes a log rather than a memory.
Days 10-14: repurposing handoff
Runbooks and SOPs generated, then reviewed
Corporate memory is generated from the tenant and reviewed by the people who own each process; a correction by a person becomes a rule the box follows.
Document the tenant as it is, then rebuild what no one can explain.
Forty colliding scripts and spreadsheet workarounds are knowledge too, held by whoever wrote them. The read records every deployment, orphaned field and integration from access; the foundation sprint retires what cannot be explained into a Single Entry Point router with written rules; the result is logic that lives in code and SOPs rather than in a person.
A dependency map first, deterministic processes second, Aissistor third
Phase 1: the 48-72 hour read
The dependency map
From record type to script to integration, with the owners, the hot paths and what the read cannot see stated explicitly.
Phase 2: process re-architecture
Deconstruct the monolith in two-week sprints
Each sprint retires one slice of the undocumented logic into the router and ends with the runbook for what it shipped: the pattern of the legacy-monolith and 60-day M&A playbooks.
Phase 3: Aissistor on top
The box keeps the memory current
Aissistor answers from the documented tenant; resolved aliases and decisions are written to the knowledge base, so the memory does not decay when the next person leaves.
An implementation or integration that stalled: orders frozen, the go-live slipping, the integrator billing change orders.
We have good data
Finish the implementation on the data you already cleaned.
The item master and the chart of accounts survived the project; what stalled is the last mile: the reports nobody built, the records still keyed by hand, the documents still in email. Aissistor plugs into the structured tenant in fourteen days and covers that last mile with chat, governed record creation and OCR, so the go-live stops waiting on a build queue.
Fourteen days from a stalled punch list to production
Days 1-3: governed connection
Confirm what is actually open
The schema handshake checks the mandatory fields and the open items on the punch list against the tenant, so the remaining work is a list, not an argument.
Days 4-9: live activation
The punch list runs through the box
Report requests are answered from live SuiteQL, the hand-keyed records become governed drafts and the emailed documents go through OCR, each approved by a person.
Days 10-14: repurposing handoff
Exceptions handed to the team
What the box cannot resolve is a scoped list of exceptions for your team, with the change-order backlog closed.
Stop, read it in 48-72 hours, restart on deterministic processes.
A stalled implementation usually stalled for a reason the status reports do not show: duplicate items, scripts that fight each other on every save, a middleware layer nobody owns. The read finds the reason from access, without another discovery phase; the foundation sprint rebuilds the broken processes in fixed-fee two-week increments; Aissistor goes on top of a tenant that works.
The read replaces the discovery phase; the sprints replace the change orders
Phase 1: the 48-72 hour read
Find the reason it stalled
The forensic read ranks every finding by dollar exposure and effort to fix: save-path scripts, duplicate masters, the integration that times out, the clearing account that never reconciles.
Phase 2: process re-architecture
Rebuild the broken process, clear the backlog
Two-week sprints replace the colliding scripts with a Single Entry Point router and cleanse the item master while the frozen backlog is worked in priority order.
Phase 3: Aissistor on top
Go live on a tenant that works
With the processes deterministic, Aissistor covers the reports, records and documents the project never finished, and every write passes through a person.
A source system the business needs connected this month, quoted as weeks of middleware work.
We have good data
Direct, idempotent integrations on a clean schema: same or next day for a standard source.
With structured items, customers and a stable chart of accounts, a standard source (an e-commerce platform, an EDI partner, a CRM, a payment processor, a bank) connects directly to NetSuite through idempotent RESTlets, with no iPaaS layer to license. The published benchmark is same or next day for a standard source, and Aissistor’s record creation governs every record the integration proposes.
Fourteen days from the schema handshake to a second source
Days 1-3: governed connection
Schema handshake for the source
The source’s fields are mapped to your mandatory fields and item aliases, and the integration gets an idempotency key per record before it sends one.
Days 4-9: live activation
The source goes live
Records that pass the schema contract post under the approver’s role; variances and near-matches wait as drafts for a person. Same or next day for a standard source, as published in the velocity benchmark.
Days 10-14: repurposing handoff
The next source follows the pattern
The second and third sources reuse the router and the contract; your developer stops patching middleware and moves to product work.
Clean the schema the integration would inherit, then connect in days rather than weeks.
An integration built onto duplicate SKUs and colliding User Event scripts copies the mess at machine speed. The read shows every script on the save path and every duplicate the source would match against; the foundation sprint collapses the scripts into a Single Entry Point router and deduplicates the masters; then the source connects at the published same-or-next-day pace, with a schema contract to post against.
One deterministic entry point before the first record arrives
Phase 1: the 48-72 hour read
Read the save path and the masters
Script deployments on every record save, duplicate items and entities, the middleware already in place and its failure modes, all from access.
Phase 2: process re-architecture
Single Entry Point and schema cleanse
Two-week sprints give the integration one governed router instead of a stack of user-event scripts, and one canonical item and customer to match against.
Phase 3: Aissistor on top
Connect, then reconcile to the cent
The source connects directly, the revenue-spine pattern reconciles it to the ledger, and Aissistor governs every record it proposes.
The variance, the stuck order and the duplicate invoice surface at month end, from a spreadsheet, instead of the day they happen.
We have good data
Exceptions surface the day they happen, from live SuiteQL, with the rows cited.
On structured data, Aissistor answers an executive’s exception question (unbilled orders over a floor, price variances on vendor bills, open intercompany pairs) from live SuiteQL and cites the transactions it read. The 10 / 80 / 10 rule applies: 10% human intent in the question, 80% machine execution in the query and the draft, 10% human oversight on the exception that needs a decision.
Fourteen days from month-end surprises to a daily exception list
Days 1-3: governed connection
Questions under your own permissions
Every exception query runs read-only under the asking user’s role, so the controller sees the controller’s rows and nothing else.
Days 4-9: live activation
Standing queries become the exception list
The questions executives keep asking become scheduled reports; record creation and OCR flag the variance in the draft before anything posts.
Days 10-14: repurposing handoff
Analysts own the list, not the spreadsheet
The analyst stops stitching saved searches in Excel and works the exceptions the list surfaces; the box keeps the list current.
An exception report over duplicate customers and un-reconciled clearing accounts reports noise. The read measures the noise: duplicates, orphaned fields, clearing-account leaks. The foundation sprint deduplicates the masters and declares the rules an exception is measured against; then the reports run on a ledger where an exception means something, and the 10 / 80 / 10 split holds because the 10% of human oversight is spent on real exceptions.
A clean rule set before the first report
Phase 1: the 48-72 hour read
Measure the noise
Duplicate items and entities, orphaned custom fields and clearing-account leaks are counted from access, so the first exception report is not a list of data defects.
Phase 2: process re-architecture
Declare the rules in code
Two-week sprints deduplicate the masters and put the exception rules into the Single Entry Point router, with a feature flag per rule, so an exception is a rule firing rather than an analyst’s opinion.
Phase 3: Aissistor on top
Report from the clean ledger
Chat answers the exception questions with the rows cited; the document pipeline flags variances before posting; two independent readings precede any draft.
A national-chain barcode mandate, a lot-traceability audit or a segregation-of-duties finding, with the evidence scattered across spreadsheets and inboxes.
We have good data
Approval gates and audit trails on the records you already keep clean.
When items, lots and roles are already structured, compliance is evidence: who approved which record, from which document, under which role. Aissistor connects in fourteen days with role-level segregation-of-duties guardrails from the 0-4 Role Risk Index, posts every document with its source archived and linked, and writes only through a person, so the audit trail is produced by the work rather than assembled for the auditor.
Fourteen days from an audit request to a trail that is already there
Days 1-3: governed connection
Segregation of duties before the first row
The 0-4 Role Risk Index scores every role; the guardrails that keep a requester from approving their own record are locked before Aissistor reads anything.
Days 4-9: live activation
Every record carries its evidence
OCR posts the document with the original archived in the File Cabinet and linked to the transaction; lot numbers and expiry dates are read at the dock; labels follow the codified standard.
Days 10-14: repurposing handoff
The audit packet is a query
Who approved what, from which document, under which role is answered from live SuiteQL instead of assembled from inboxes.
Traceability starts with a clean item master, not with a label printer.
A standard applied to duplicate items, missing lot subrecords and shared administrator logins produces findings, not evidence. The read scores the roles on the 0-4 Role Risk Index and inventories the items, lots and documents the standard covers; the foundation sprint cleans the masters and codifies the standard (label formats, lot capture, approval steps) into deterministic processes with a live cutover; then Aissistor keeps the trail per record.
Clean masters, a codified standard, then the trail
Phase 1: the 48-72 hour read
Score the roles, inventory the lots
Roles and permissions on the 0-4 Role Risk Index; the items, lots, labels and documents the standard touches, counted from access.
Phase 2: process re-architecture
Codify the standard into the save path
Two-week sprints cleanse the item master and put label formats, lot capture and approval steps into the Single Entry Point router, then cut over live without stopping the dock.
Phase 3: Aissistor on top
Aissistor keeps the trail
Documents post with their source linked, approvals are logged per record, and every write still goes through a person.
Chat with NetSuite, governed record creation and OCR document-to-ledger
Pick a capability and a prompt. Every output below is scripted and labeled simulated: archetype names, SIM- identifiers and no live data. In production the same flows run against your tenant under your role permissions, and every write ends as a draft a person approves.
Capability 01
Chat with NetSuite: executive and analyst questions over live SuiteQL
Replaces: Ad-hoc analyst report tickets and manual Excel joins
Pick an executive question to see how Aissistor turns it into a governed, read-only query and answers with the rows it read.
Show me unbilled sales orders over $25,000 where FIFO lots are already allocated on the dock.
FROM transaction t JOIN transactionline tl ON t.id = tl.transaction
JOIN inventoryassignment ia ON tl.id = ia.transactionline
WHERE t.type = 'SalesOrd' AND t.foreigntotal >= 25000 AND billed = 'F'
Three allocated sales orders, $219,050 unbilled, every one above the $25,000 floor:
- SIM-SO-0418, regional retail archetype, $142,800: lot allocated and shipped two days ago, billing hold still checked.
- SIM-SO-0442, cold-storage archetype, $48,250: stage-verified, ready for the invoice batch.
- SIM-SO-0457, foodservice archetype, $28,000: partial fulfillment, remaining lot on the dock.
Proposed action: draft three invoices for the billing desk to approve. Nothing is written until a person approves.
Which User Event scripts on sales-order save add the most user wait this month?
Simulated output, not live data
GOVERNED SUITEQL, READ-ONLY (role: administrator)
Script and permission inventory: 4 deployments on salesorder beforeSubmit / afterSubmit
Execution log joined to deployment and owner, last 30 days
Top contributor: SIM-UE-PRICE-CHECK, 41% of the measured save time, re-reading the price book on every line.
Second: SIM-UE-TAX-SYNC, 27%, calling an external tax endpoint synchronously.
Recommendation: fold both into the Single Entry Point router; the published projected row shows what the pattern returns.
Evidence: /100x-roi-ledger/ (sales-order form latency, projected).
Reconcile consolidated Q3 gross margin across our four acquired subsidiaries after intercompany eliminations.
Simulated output, not live data
GOVERNED SUITEQL, READ-ONLY (role: group controller)
Consolidated trial balance by subsidiary, Q3, with elimination journal lines separated
Intercompany pairs matched on reference and amount
Subsidiary A (distribution archetype): 31.4% gross margin, 12 elimination lines.
Subsidiary B (manufacturing archetype): 24.8%, 9 elimination lines.
Subsidiary C (services archetype): 42.1%, 3 elimination lines.
Subsidiary D (acquired in Q2): 27.6%, 15 elimination lines, 2 intercompany pairs still open.
Consolidated: 29.9%, tied to the consolidated trial balance. The two open pairs are flagged for the controller, not resolved by the model.
Why it holds: Aissistor does not guess numbers. It translates the question into read-only SuiteQL under the asking user's own role permissions, runs it against the live subledgers and cites the transactions it read. It never writes.
Capability 02
Governed record creation: conversational and automated, approved by a person
Replaces: Manual CSV imports, form save errors and support-clerk re-keying
Select a record-creation workflow to inspect the pre-flight check against mandatory fields and the custom schema.
Create a multi-line sales order with FIFO lot allocation from a customer PO email.
Simulated output, not live data
SCHEMA CONTRACT CHECK: salesorder
- entity: regional retail archetype, matched on the PO header .......... ok
- subsidiary and location: from the customer default ................... ok
- custbody PO reference: populated from the email ....................... ok
- tax nexus schedule: assigned from the ship-to state .................... ok
Draft SIM-CUST-0310 routed to the credit desk with the near-match side by side.
The draft is created only after a person confirms it is a new customer.
Why it holds: Every standard and custom mandatory field (custbody_*, custcol_*, inventory-detail subrecords) is checked against the schema contract before a draft exists, and a person approves the write. A record that fails the contract is never proposed.
Capability 03
OCR and document-to-ledger automation: 2,881 production documents processed (measured)
Replaces: Paper clipboards, PDF invoice re-typing and delayed warehouse receipts
Select an unstructured document to see how the pipeline extracts it, cross-walks it to NetSuite records and posts after approval.
Vendor PDF invoice and freight bill to landed cost and an AP vendor bill
Simulated output, not live data
OCR EXTRACTION AND CROSSWALK: vendor_invoice_sim.pdf (3 pages, skewed scan)
- vendor: cold-chain carrier archetype, matched to the vendor master ...... ok
- PO reference: matched, open receipt balance confirmed ................... ok
- 14 of 14 vendor part numbers cross-walked to NetSuite item IDs ......... ok
- freight and duties: allocated across received lots by gross pallet weight
Posted after approval: vendor bill SIM-VB-8319 with its landed-cost allocation.
Original PDF archived in the File Cabinet and linked to the transaction; AP staff move from typing PDFs to resolving price variances.
Receiving-dock packing slip and scale weight tag to an item receipt with lot traceability
Simulated output, not live data
OCR EXTRACTION AND CROSSWALK: packing_slip_sim.jpg + scale_ticket_sim.jpg
- 9 of 9 packing-slip lines matched to the purchase order ................ ok
- scale ticket: gross, tare and net weight read; catch weight recorded ... ok
- lot numbers created per line; expiry dates read from the slip .......... ok
Draft item receipt SIM-IR-0577 ready for the receiving lead to approve on the tablet.
Lot traceability starts at the dock, not at month end.
Shop-floor production traveler and QC sign-off to a work-order completion and the scrap ledger
Simulated output, not live data
OCR EXTRACTION AND CROSSWALK: traveler_sim.pdf (handwritten quantities)
- work-order reference: matched ........................................... ok
- completed quantity 940, scrap quantity 12 with reason code ............. ok
- QC signature block: present, inspector matched to the employee record .. ok
Draft work-order completion SIM-WOC-0144 with 12 units posted to the scrap ledger account.
The production supervisor approves; the scrap lag closes the same shift.
Why it holds: Measured: 2,881 production documents through the floor-to-ledger OCR intake pipeline, with 99.4% field accuracy on the mandatory fields it extracts. The original document is archived in the File Cabinet and linked to the transaction it produced.
Where the margin goes
Six operational bottlenecks that drain EBITDA every month
Every week a team spends babysitting brittle scripts, re-keying PDFs or waiting on five separate ERP roles is margin and opportunity lost.
Bottleneck 01: payroll and agency bloat
The $1,000,000/yr five-role silo (modeled)
A separate administrator, developer, business analyst, support specialist and QA lead, plus agencies billing slow change orders, costs roughly $1M a year in the modeled stack while tickets still sit in a backlog.
What changesAll five roles in one governed box at a modeled 20% to 80% lower cost; the arithmetic on your own figures is in the calculator.
Bottleneck 02: reporting blindness
Waiting days for basic NetSuite answers
A CFO or COO asks a straightforward margin, cash or inventory question and waits days for an analyst to export three saved searches and stitch them together in Excel.
What changesChat with NetSuite runs governed SuiteQL across the live subledgers and answers with the rows it read.
Bottleneck 03: manual data entry
Employees trapped re-keying PDFs and clipboards
Skilled operations and AP staff spend hours typing vendor invoices, packing slips, customer POs and shop-floor clipboards into NetSuite, which creates lag, typos and burnout.
What changesOCR and governed record creation extract the document, validate the mandatory fields and post after a person approves.
Bottleneck 04: messy data and script debt
40+ colliding scripts and dirty item masters
Years of uncoordinated SuiteScript deployments slow every sales-order save, duplicate SKUs and break automations, and make a bolted-on chatbot guess.
What changesThe Track B foundation sprint refactors the scripts into a Single Entry Point router and cleans the schema before Aissistor is switched on.
Bottleneck 05: multi-entity and M&A drag
A 21-day close across acquired entities
Acquired companies sit on disconnected QuickBooks or legacy ERP files for months while finance reconciles intercompany eliminations and trial balances in spreadsheets.
What changesSame-day hashed M&A consolidation and OneWorld close automation: the published close went from 21 days to 3.
Bottleneck 06: key-person risk
Tribal knowledge walks out the door at turnover
When the in-house administrator or developer leaves, undocumented workflows, hard-coded internal IDs and custom integrations become a liability at audit or exit.
What changesCorporate memory and the 0-4 Role Risk Index codify every script and permission into SOPs the next person can read.
The DO5: five first-principles operating values
How DataOngoing delivers 10-50x speed without junior-consultant bloat
Every Aissistor deployment and every architectural sprint is governed by five operating principles.
01
5 roles in 1 box
Cooperative hyper-specialization
No junior analysts learning on your dime. Principal architecture plus governed AI covers the five fragmented ERP roles at a modeled 20-80% lower cost.
02
Clean data or we build it
Governed logic before an AI surface
We never layer AI over broken processes. Clean data, and Aissistor plugs straight in; messy data, and the process and schema are re-architected first.
03
Repurpose your people
10 / 80 / 10 human leverage
10% human intent, 80% machine execution, 10% human oversight. Employees stop doing robotic ERP data entry and move to judgment, customers and margin.
04
No middleware toll
Direct point-of-work integration
Scanners, scales, OCR documents and banking APIs post directly into NetSuite through idempotent RESTlets, with no iPaaS layer to license or babysit.
05
100x time / 10x fee
Basis-point proof with a source class
Every automation is tracked in the public 100x time ledger with its source class, and the 10x fee guarantee is written into every engagement letter.
Engagement models and timelines
Structured 3, 6, 9 and 12-month roadmaps, next to the fixed-price sprints
Whether you are plugging Aissistor into a clean environment or transforming a multi-entity portfolio company, every engagement has fixed deliverables, transparent economics and no open-ended hourly billing. Each roadmap is quoted after the diligence read.
3months
Foundation and fast plug-in
Process cleanse plus the core box
For single-entity operators ready to remove immediate reporting, script and OCR bottlenecks.
No roadmap or tier carries a published price. Fixed deliverables are quoted after the diligence read, with no hourly billing, and the read is credited in full against the roadmap it opens.
Need a single fixed-price entry sprint first?
The productized offers stand on their own. The diligence read is the entry point to every roadmap and is credited in full against it.
A flat-fee, AI-driven technology diligence read on a NetSuite or ERP target in 48-72 hours, with a quantified risk matrix and a costed remediation plan. 100% of the fee is credited to downstream remediation.
Basis-point leverAvoided discount at close: the finding moves into the purchase agreement.
Your time2-3 hours: one access grant, one read-out.
Turnaround48-72 hours from access
Price$12,500 flat fee, 100% credited to remediation
A fixed-price, fourteen-day production sprint that removes one named margin leak or bottleneck in a portfolio company with working code and a schema-governed agent where one belongs. If it does not run in production, the milestone is not billed.
Basis-point leverLabor, freight, working capital or throughput, stated in basis points on the company’s revenue in the SOW.
Your time3-5 hours: scope sign-off, one mid-sprint review, acceptance.
A fixed-scope 100-day program that consolidates acquired entities into one governed NetSuite OneWorld tenant with an agentic close: chart of accounts harmonization, automated intercompany eliminations and a consolidated period close compressed from roughly 21 business days to 3.
Basis-point leverClose speed and working capital: margin by product line in week one; DSO and inventory decisions made on current numbers.
Your time6-10 hours across the program: chart of accounts decisions and two reviews.
Turnaround100 days
PriceFixed scope, quoted after a one-week consolidation assessment
Edge devices and an AI document-intake (OCR) pipeline posting directly into the ledger, quoted per site: no re-keying, no per-user warehouse licenses, and documents processed by an agent instead of a clerk.
Basis-point leverLabor and freight basis points: re-keying labor removed, carrier re-weigh penalties eliminated, document handling time collapsed.
Your time3-4 hours per site: floor walk and device list sign-off.
Turnaround2-3 week sprint per site
PriceQuoted per site after a device and document audit
Replace 6-to-9 month post-acquisition ERP data migrations with a 24-hour cryptographic NetSuite cutover. Seal the historical GL rows, master crosswalks, and FSMA 204 lot traceability into 4 NetSuite Custom Records (customrecord__do_ma_*) with SHA-256 row hashing, Binary Merkle Tree verification, and a live date-range GAAP P&L console.
Basis-point leverPost-close consolidation speed and QoE audit certainty: Day-1 consolidated GAAP P&L visibility tied to the source exports, with no 9-month SI migration drag.
Your time3-4 hours: raw archive drop, COA mapping sign-off, Merkle root acceptance.
An SDF bundle that syndicates NetSuite inventory, lot age and wholesale price tiers to Google, Facebook, Instagram, TikTok and YouTube, turns SKU photos into motion video ads, takes the B2B order through a checkout that posts the NetSuite transaction, and measures every campaign against a Day-0 baseline in realized gross margin. $15,000 flat setup, $2,500 per month to run the engine.
Basis-point leverGross margin on advertised inventory: no spend on stocked-out SKUs, aged lots cleared before write-down, and every ad dollar read against realized gross profit in the ledger.
Your time3 hours: catalog and floor sign-off, ad account access, launch review.
Turnaround14 days from account access
Price$15,000 flat setup, $2,500 per month to run the engine
The evidence behind the box
Everything else on this site is supplementary evidence of Aissistor at work
M&A consolidation done faster, customizations shipped in two-week sprints, cost and shipping recovery, documents posting straight into the ledger: each page names the capability it demonstrates and the roles it offloads. 104 proof cards, 12 ledger rows and 10 workflow designs back this.
Warehouse barcodes3:1 QR-code consolidationA radical transformation in barcodes for the warehouse: 3:1 QR-code use across pack, ship, fulfill and receive, with intercompany effects. Multi-site distribution warehouse (archetype). Measured, consulting engagement
Item master80% reduction in wasteful SKUsAn 80% reduction in wasteful SKUs through better data modeling. Multi-entity distributor (archetype). Measured, consulting engagement
Accounts payable$800k/yr saved on AP cost$800k saved per year on AP cost, a separate outcome from the published $280K/yr labor recovery. Multi-subsidiary operating company (archetype). Measured, consulting engagement
Founder track record80+ subsidiaries, $1B+ revenue flows80+ subsidiary companies automated, 100+ currencies, $1B+ in revenue flows over 13 years. Founder track record, pre-DataOngoing
The 100x time ledger
100x ROI for time invested means that for every hour an operating partner, CFO or CTO puts into an engagement, the automation returns about one hundred hours of work or the equivalent margin over twelve months. 100x is the design target for every engagement. The ledger below shows measured rows between roughly 15x and 90x on time, one self-reported internal row near 80x, and projected rows above 100x. Every figure states its source class so it can be checked.
Measured, consulting engagement Observed in a DataOngoing client environment. Client shown as an industry archetype.
Founder track record, pre-DataOngoing Delivered by Kyle Castor as an employee (2013-2025), as stated on the public resume of record. Not a DataOngoing engagement.
Self-reported, internal Measured on DataOngoing’s own operations, with the manual baseline estimated.
Projected Arithmetic on a measured baseline with stated assumptions. Not yet observed after deployment.
Modeled composite Illustrative scenario built from several engagements. Figures illustrate assumptions, not a named result.
Document intake, governed AI workers, the two-model consensus gate, voice intake, the single entry point router, the diligence read, a live cutover and a revenue spine, each with its approval points and evidence.
The founder track record behind the ledger: 80+ subsidiaries, 100+ currencies, $1B+ revenue flows. Watch the one-minute brief.
We do not replace people with AI. We take the clerical work off them.
The operators, clerks and floor leads already inside a portfolio company hold the institutional memory, customer trust and edge-case judgment that no consultant or model can fabricate. Our business cases are built on repurposing that time, not on layoffs: the clerical load moves into code, and the people move to exceptions, customers and judgment.
Principle 01
Institutional memory stays
Cutting operators to save labor cost severs the knowledge of which customers need custom packaging, how a carrier misroutes pallets and where the legacy database quietly fails. The team stays and gets telemetry instead of clipboards.
Principle 02
End human middleware
An employee who spends twenty hours a week copying data between NetSuite, carrier portals, EDI translators and spreadsheets is working as software glue. We automate the glue and hand the hours back.
Principle 03
Capacity without headcount inflation
A roll-up should not have to double administrative headcount to double transaction volume. Existing staff move to exceptions and customer work while the volume moves through code.
Where the hours go
Three roles that absorb most of the clerical load in a distribution or manufacturing portfolio company: what a sprint removes, where the time goes, and a published row that shows the mechanism with its source class.
Warehouse and dock supervisorsStays on the team
What a sprint removesRe-printing dropped label batches, un-jamming print spoolers, disputing carrier re-weigh fines.
Where the hours goDock sequencing, driver queues and carrier detention, with weights and lots captured at the scale.
We win the floor first. Then we win the boardroom.
The diligence read needs no interviews. When a sprint reaches the floor, the first conversations are with the floor leads, shipping clerks and billing staff who live with the friction, not the steering committee. When the first sprint removes their daily friction, they become the sponsors of the next one. Read the floor-lead dialogue.
Every design above puts a model in front of your records at some point. Three questions settle whether that is acceptable to your counsel and your limited partners, and the architecture answers them before a model is chosen.
What leaves
The rows the task needs, nothing more
A worker receives a small packet of live rows assembled by code, with personal data masked where the task does not need it. Reads return live rows; writes are drafts a person approves; prompt, rows, model and approver are logged per record.
Which model
Your choice. We recommend Claude, Gemini or OpenAI
Anthropic’s, Google’s and OpenAI’s models through their commercial endpoints on your own account, under terms that exclude training on your prompts. When data cannot leave, the same designs run on open-weight models on your hardware or on dedicated GPU capacity through TensorWave.
Who reads it afterwards
No consumer chat tools, no pooled learning
Enterprise or API endpoints under a signed agreement, nothing from one engagement training or prompting another, and our own access removed when the engagement ends. The vendor, endpoint, retention setting and masking rules are written into the SOW.
Ready to stop bleeding time, money and opportunity?
Put five NetSuite roles in one box, or let us fix your processes first
DataOngoing.ai is the Aissistor technology and AI automation consultancy of DataOngoing LLC. DataOngoing.com is the NetSuite managed-services practice. Same founder, same engineering standard, built to repurpose your team rather than replace it.
Talk directly with the principal architect, not a sales rep. Pick the roadmap or readiness track in the dropdown and describe the queue that hurts most: chat, record creation, OCR or role overhead.
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