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Finance Engineers

Turning broken financial data

into automated operations.

Finance engineers building the finance data foundation your AI runs on. We go inside your systems, fix the data, and automate the Quote-to-Cash cycle on top — so your numbers hold up when it counts. On your existing stack. No new tools required on your side.

✓ Finance Data Foundation
✓ Quote-to-Cash Automation
✓ No commitment to start
25 days faster cash collection
1–9% revenue leakage found
60% of AI projects fail on dirty data
Data Foundation
AI-ready
COA Transactions Payments Rules
Payment Recon
Zero open gaps
100%matched
Stripe · Shopify · MercadoPago
Engine
AR Aging
Real-time visibility
live
Not discovered at month-end
Invoice to Cash
DSO reduction
−15days DSO
avg improvement
Audit Readiness
12 mo.
READY
Data quality Process docs Cash visibility Audit trail
Your existing stack
How we build on top

The problem

You grew the revenue. The financial infrastructure didn’t follow.

The problem has three layers — and they compound each other.

The data is broken

Books never formally closed for prior periods. Transactions misclassified because someone made a judgment call and the rule was never written down. Payment platforms generating entries that don’t reconcile with the accounting system. Chart of accounts built for tax compliance, not operations. This is structural — not dysfunction. Every company at this stage has it.

No real visibility

Books close 2–3 weeks after period end. Different reports tell different stories. Cash position is a guess. By the time the numbers arrive, the decisions are already made on guesswork. You’re not managing with data — you’re reconciling with history.

AI won’t fix it

Every finance AI tool on the market assumes clean, structured, connected data. Automate on a broken foundation and you get bad data processed at scale — with false confidence, at high cost. The tools aren’t the problem. The data underneath them is.

Most companies at this stage know their financial data isn’t clean. They treat it as background noise rather than the root cause of everything that breaks. When the foundation is broken, automation makes it worse. Opexi fixes the foundation first — then builds the automation that runs on top of it.

What we deliver

Two products. One sequence.

The foundation almost always comes first. Quote-to-Cash Automation runs on top of it. In some engagements both run together — in others, Foundation is delivered standalone to teams who take it from there.

01
Finance Data Foundation

We go inside your financial systems, clean the data, restructure the chart of accounts, document the rules, and reconcile what needs reconciling. The output is not a report — it is working, clean, documented foundations that your internal teams and any downstream automations can rely on.

What it covers
  • Chart of accounts restructured for operations, not just tax compliance
  • Transaction reclassification at scale — tool-assisted, not entry-by-entry
  • Formal book closing for periods that were never properly closed
  • Payment platform reconciliation — Stripe, Shopify, MercadoPago → your ERP
  • Client and vendor data brought inside the system and linked correctly
  • Rules documentation: what goes where, which exceptions exist, who resolves edge cases
Who buys it
  • Finance or data teams blocked by data quality — “our BI team can’t work with this data”
  • CEO preparing for a capital event whose accountant flagged book problems
  • Company that tried to implement an automation or AI tool and it underdelivered
  • Post-acquisition: acquired entity books need to integrate into the parent structure
02
Quote-to-Cash Automation Setup

We connect the systems that don’t talk to each other — CRM, accounting system, ERP, billing platform, payment processor — and automate the handoffs between them. Your team stops running the process manually and starts handling the exceptions only.

01
Quote to Invoice

We connect your CRM to your accounting system and build the rules engine that validates every invoice before it leaves — right amount, right terms, right contact, right timing. No manual re-entry between systems.

You stop doing
  • Manually creating invoices from CRM data
  • Chasing the sales team for contract terms
  • Discovering billing errors when the client disputes them
02
Invoice to Cash

We run systematic, AI-personalized collections sequences. Every incoming payment matched to its invoice automatically. AR aging monitored continuously — not discovered at month-end.

You stop doing
  • Sending manual reminder emails to clients
  • Reconciling payments every Friday afternoon
  • Having one person’s memory be your AR process
03
Cash to Books

We build the financial data layer that makes month-end close a confirmation, not an investigation. Automated reconciliation, anomaly detection, and a reporting pack delivered automatically — in days, not weeks.

You stop doing
  • Waiting 2–3 weeks to know last month’s numbers
  • Making decisions on data you don’t fully trust
  • Assembling a management report by hand every month

Opexi works alongside your existing accountant — not instead of them. We operate on top of your existing stack. No migration. No new tools required on your side.

What we are — and aren’t

Not advisors. Not a tool. Not a headcount play.

Most solutions in this space either advise without executing, record without fixing, or automate without cleaning first. Opexi is none of those things.

vs. Fractional CFO
They advise. We execute. A fractional CFO tells you what to fix and leaves. The gap between “what to fix” and “fixed and running” is exactly where we operate — and we maintain it after.
vs. Bookkeeper / Accounting Firm
They record the past. We fix the process. Compliance-focused bookkeeping doesn’t restructure your data for operations or automation. We fix the layer that generates trustworthy records going forward.
vs. Finance AI Tools
They assume clean data. We create it. Every finance AI tool requires clean, connected foundations to function. Some will automate regardless — and produce bad results at scale. We do the Foundation work that makes those tools actually work.
vs. AP/AR Point Solutions
They automate one piece. We run the full cycle. Automating collections or AP in isolation still leaves someone owning the handoffs. We run the full Quote-to-Cash cycle — and audit whether the foundation supports it before we build anything.
vs. Staff Augmentation
They add headcount. We reduce the need for it. Staff augmentation solves a capacity problem without solving the process problem. Our explicit goal is to reduce manual work over time — not to become permanent overhead.
vs. Automation Studios
They build and hand off. We audit, build, and operate. Generic automation studios build to spec and leave. Without finance domain expertise and ongoing ownership, automations break quietly. We own what we build.

The business case

Six data points. Why it matters to fix it now.

Three on fixing the foundation. Three on automating what runs on top of it.

Foundation
60%
of AI projects will be abandoned through 2026

Gartner predicts organizations will abandon 60% of AI projects lacking AI-ready data through 2026. 63% of organizations already admit they don’t have the right data management practices for AI — and they know it.

Gartner, Feb 2025 · 1,203 data management leaders
Foundation
12%
have data of sufficient quality for AI

Only 12% of organizations report data of sufficient quality for AI applications. The other 88% are building on foundations that will make those investments fail — whether they know it yet or not.

Informatica CDO Insights, 2025
Both
more likely to see real returns from AI

Organizations reporting significant AI returns are twice as likely to have redesigned end-to-end data workflows before selecting any tool. Fixing the foundation first isn’t a delay — it’s the differentiator.

McKinsey Global AI Survey, 2025
Q2C
25 days
DSO gap between automated and manual

Companies that automate 80%+ of invoicing collect in 30 days. Manual shops take 55 days. That 25-day spread is working capital sitting idle — compounding every billing cycle.

APQC Customer Credit & Invoicing Benchmarking
Q2C
1–9%
of revenue leaks silently every year

Mid-market companies lose 1–9% of revenue to preventable leakage — billing errors, missed renewals, wrong amounts nobody catches until an audit. Average annual impact: $500K–$2M, already earned, never collected.

MGI Research · Hyperstart, 2026
Q2C
6.4 days
median close — bottom 25% take 10+ days

APQC benchmarked 2,300 organizations. Median close is 6.4 days. The bottom quartile takes 10+. Only 18% of finance teams close in 3 days or less. Every extra day is a day leadership makes decisions on last month’s data.

APQC General Accounting Benchmarking · Ledge, 2025

The process

Four stages. From broken finance data to a running engine.

Foundation almost always precedes Quote-to-Cash Automation. The stages are not always strictly sequential — different parts of an engagement can be at different stages simultaneously.

Stage 01
Free · No commitment
Discovery

We understand your systems, transaction volume, and where the gaps are. We scope the Foundation engagement: which systems, how many periods, estimated complexity. Output: a clear picture of where to start and how.

No commitment required. You get a concrete scope and proposed starting point whether or not we work together.

Stage 02
Scoped project
Finance Data Foundation

We go inside your systems and execute the cleanup identified in Discovery. Chart of accounts restructuring, transaction reclassification, book closing, payment platform reconciliation, rules documentation. Every judgment call written down.

Output: clean, documented, AI-ready and automation-ready financial foundations. Your existing accountant stays in place — we handle the operational layer.

Stage 03
Setup + retainer
Quote-to-Cash Automation Setup

Built on top of clean foundations. We connect your CRM, accounting system, and payment processor, configure the three automation modules, and set up exception handling. We train your team on the workflow they will own going forward.

Output: Quote-to-Cash cycle running automatically. Your team handles exceptions only.

Stage 04
Ongoing
Maintenance Retainer

Foundations drift. Automations need updating as the business changes. New pricing logic, new integrations, new exception types — we keep everything running correctly as the business evolves.

Foundation retainer: data quality monitoring, rule updates, new integration cleanup. Q2C retainer: automation maintenance, exception monitoring, monthly reporting pack delivery.

Who it’s for

AI-ready finance operations — built for companies that can’t afford to wait.

  • Series A or B company, or bootstrapped and growing fastYou raised capital or grew fast — the finance infrastructure didn't follow. Your investors want clean metrics. Your team is running everything manually.
  • Your financial data isn’t clean — and you know itDifferent reports tell different stories. Tools underdeliver because the data underneath them is wrong. Manual effort is still the glue holding everything together.
  • Your stack doesn’t talk to itselfQBO, HubSpot, Stripe, NetSuite — whatever you have. They're not connected. Data lives in three places and nobody owns the reconciliation between them.
  • A capital event ahead — or a cash visibility problem nowRaising, selling, or just trying to know your cash position 30 days out. Any of these requires books you can trust and an AR cycle that actually closes.
  • You tried automation or AI — and it underdeliveredYou invested in tools that were supposed to fix the problem. They didn’t — because the data underneath them was wrong. We fix the foundation that makes those tools actually work.
Not a fit right now
Pre-Series A startups without finance infrastructure needs
Clients looking for hourly advisory
Companies that want advice but won’t delegate execution

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