AI AGENCY
SYS-10 · Data automation

Somebody in your building is retyping a number that already exists.

It came in on an invoice, or an order, or an export from a system that won't talk to the other system. So a person reads it off one screen and keys it into another. We build the pipe that does it instead — faster, at a fraction of the cost, and without the typo.

See pricing Cost your manual work
Data pipeline monitor 3 pipelines nominal
PIPE-01 / Intake

Documents in

Invoices, purchase orders, packing slips, bills of lading, applications. AI reads the fields and writes them to the system of record — seconds instead of minutes, each.

PIPE-02 / Sync

Systems in step

ERP, CRM, ecommerce, accounting, spreadsheets. One record per customer, per SKU, per order — updated everywhere the moment it changes anywhere.

PIPE-03 / Reconcile

Exceptions surfaced

Mismatches, duplicates, missing fields and stale records get caught and queued. A held record is cheap. A wrong number in a report is not.

Every pipeline ships with monitoring, retry logic and an exception queue. You find out when something breaks — not six weeks later when the numbers don't add up.
The shape of it

Six stages. No hands.

Every build we ship follows the same skeleton, whether it is moving three hundred invoices a month or three million rows a night. The complexity lives in the rules, not the architecture.

01

Capture

Email, EDI, API, SFTP, portal scrape, or a PDF someone dropped in a shared folder.

02

Extract

AI reads the document or the payload and pulls structured fields out of unstructured mess.

03

Validate

Format, range, required fields, duplicate check, match against a PO or a customer record.

04

Transform

Units, currencies, SKU mappings, tax codes, naming conventions. Their format becomes yours.

05

Load

Written into the ERP, CRM, warehouse or spreadsheet that is actually the system of record.

06

Reconcile

Anything that doesn't agree gets queued for a human. Everything else never needed one.

The exception queue is the whole trick. Automation that demands perfect inputs fails on contact with a real business. Ours assumes roughly one in ten records will be strange, routes those to a person with the context attached, and handles the other nine without asking.

What we build

Move it. Clean it. Use it.

Most businesses have all three problems and only notice the first one. Data that won't move gets a person assigned to carry it. Data that isn't clean quietly corrupts every decision downstream. Data nobody can query gets ignored entirely.

Move it

Integration between systems that were bought in different decades by different people for different reasons.

  • Document data extractionAIInvoices, POs, packing slips, quotes, applications, remittance advice. Read once, keyed never.
  • ERP ↔ CRM ↔ ecommerce syncCustomers, orders, inventory, pricing and status flowing both directions on a schedule you set.
  • Accounting & AP integrationBills coded, matched to a PO and posted, with exceptions held rather than guessed at.
  • EDI & vendor feedsTrading partner files parsed, normalized and loaded, including the ones that arrive as an emailed spreadsheet.
  • Legacy & on-prem connectorsSystems with no API still have a database, a file drop or a screen. We work with all three.
  • Spreadsheet eliminationThe workbook that three departments depend on and one person understands becomes an actual pipeline.

Clean it

Data decays from the moment it is collected. Cleaning once is a project; cleaning continuously is a system.

  • Deduplication & mergeFuzzy matching across name, address, domain and phone. The same customer stops being four customers.
  • Validation at entryAddresses, emails, phone numbers, tax IDs and part numbers checked before they enter the system, not after.
  • EnrichmentFirmographics, contact detail, geocoding and SIC/NAICS appended automatically to thin records.
  • StandardizationOne format for names, addresses, units, SKUs and categories, applied retroactively and enforced going forward.
  • Decay monitoringRolling re-verification so records don't quietly go stale between audits.
  • Reconciliation rulesThree-way match, inventory-to-ledger, invoice-to-PO, payout-to-order. Breaks get surfaced, not absorbed.

Use it

Clean data in a system nobody queries is an expensive filing cabinet. This is where the work pays for itself twice.

  • Scheduled reportingThe Monday numbers, built and delivered before Monday. No export, no VLOOKUP, no version four.
  • Live dashboardsSales, margin, inventory, AR aging and pipeline against a single source, refreshed on your cadence.
  • Data warehouse buildsBackboneA modeled warehouse so questions get answered in a query instead of a two-day fire drill.
  • Threshold alertingMargin slips, stock runs short, an account goes quiet, a batch fails — you hear about it the same day.
  • AI-ready datasetsStructured, labeled and consistent, because a model trained on your mess will confidently repeat it.
  • Audit trailsEvery transformation logged and reversible. When finance asks where a number came from, there's an answer.

Commonly connected

NetSuiteQuickBooksSage EpicorInforSAP B1 SalesforceHubSpotZoho ShopifyMagentoBigCommerce SnowflakeBigQueryPostgres SQL ServerGoogle SheetsSFTP / EDI Custom API

Month-end, today

  • Someone exports four reports and reconciles them by hand in a workbook nobody else can open.
  • Two departments quote different revenue figures in the same meeting, and both are defensible.
  • An invoice sits for nine days because it was emailed to a person who was on leave.
  • The same customer exists three times, so nobody trusts the account history.
  • A pricing error ships for six weeks before anyone catches it.
  • The report you actually wanted would take a day to build, so nobody asks for it.

Month-end, automated

  • The reconciliation ran overnight. What didn't match is a short list, already assigned.
  • One number, one source, one definition — and a lineage trail if anyone wants to argue.
  • The invoice was captured, coded, matched and queued for approval on arrival.
  • One customer record, deduplicated on ingest and re-verified on a rolling schedule.
  • The price mismatch alerted the day the feed disagreed with the ERP.
  • The report already exists, because building it once was the point.
Published benchmarks

What the research says this is costing you.

These are third-party figures from named studies, not our results. Several are enterprise-weighted, so the absolute dollars will be smaller in a fifty-person company — the mechanism is the same, and that is the part worth reading.

7.6 hrs Lost per knowledge worker per week to automatable tasks — 44 working days a year Sapio Research / Frends 2026
$12–$30 Fully loaded cost to process one invoice by hand APQC / Ardent Partners
~$2.78 Cost per invoice for best-in-class automated AP teams Ardent Partners 2025
69% Automation potential of data processing work specifically McKinsey Global Institute
Manual vs. automated — the published gap
Measure Manual Automated What actually changes
Cost per invoice $12–$30 $1–$5 A 60–80% compression. The gap widens with volume because automated processing has near-zero marginal cost per additional document.
Processing time per invoice ~12.5 min ~1.2 min On 500 invoices a month that is roughly 94 hours recovered — about 2.3 FTE-weeks.
Cycle time, receipt to payment 8–17 days under 1–3 days Short cycles are what make early-payment discounts capturable. A 2/10 net 30 term annualizes near 36%.
Exception rate ~22% ~9% Top performers also auto-resolve 60–70% of the exceptions that remain, rather than escalating all of them.
Data entry error rate 1–4% per field ~0.3% In financial services the fully loaded cost of correcting a single data error runs $53–$98 once detection and investigation are counted.
Biggest reported time drain Data entry, 33% — Report generation from multiple sources is next at 25%, and error checking ties it. All three are the same integration problem wearing different hats.
Reported ROI — 200–600% yr 1 Typical published payback on AP automation is 3–9 months; Forrester documented 248% three-year ROI on broader enterprise automation.
White paper case studies

Three builds, costed in the open.

Read this first: these are modeled scenarios — published industry benchmarks applied to representative business profiles. They are not client results and no client is being described. Every input is shown, every assumption is set deliberately low, and the arithmetic is yours to check. Real named outcomes will replace these as they become publishable.

Model 01 · Distribution

Six hundred invoices, keyed by hand

Document extraction + AP

Profile: a regional distributor processing roughly 600 supplier invoices a month across two AP clerks. Invoices arrive by email, PDF and occasionally fax. Coding and PO matching are manual.

Invoices / mo
600
Manual cost
$7,200
Automated cost
$2,448
Modeled saving / mo
$4,752

Against the Manifold package at $1,797/mo, the modeled net is roughly $2,950 a month and the $3,995 build pays back in about six weeks. Neither clerk is made redundant in this model — both are moved onto vendor management and discount capture, which is where the second wave of return comes from.

Show the full model & assumptions
  1. 600 invoices × $12 fully loaded = $7,200/month. APQC and Ardent Partners publish $12–$30 for predominantly manual AP; we model at the floor of that range, and it excludes downstream error correction, which research suggests adds another 25–40%.
  2. 528 clean invoices × $3, plus 72 exceptions still handled at $12, = $2,448/month. Best-in-class published figures land near $2.78 per automated invoice; we round up because a first-year program is not best-in-class, and we deliberately keep charging ourselves the full manual rate on everything the machine hands back to a person.
  3. Time: 12.5 minutes per invoice down to roughly 1.2. That is about 113 hours a month returned across the two clerks — most of a full-time role, redeployed rather than removed.
  4. Exception rate assumed at 12%, against a published ~9% for top performers and ~22% for everyone else. Seventy-two invoices a month still touch a human, and the model pays full manual price for every one of them. That is the design, not a failure of it.
  5. Early payment discounts excluded entirely. Cycle time falling from roughly 8 days to under 2 makes 2/10 net 30 terms capturable — an effective annualized rate near 36% on the discounted invoices. We leave this out because capture depends on cash position, not on us.

What this model excludes: duplicate payment prevention, which automated matching catches routinely; the audit and close-cycle benefit; and the cost of the AP clerk who leaves and takes the undocumented process with them.

Model 02 · Professional services & back office

The Monday report that eats Thursday

Reporting + system sync

Profile: a twelve-person operations and finance team pulling numbers out of four systems by hand. Weekly management reporting, monthly reconciliation, and a standing argument about which figure is right.

Hours lost / mo
158
Loaded rate
$38/hr
Modeled saving / mo
$6,000
Net of retainer
$4,200

The number that matters here isn't the salary line — it's the latency. A weekly management report typically takes about 2.4 hours to produce and is already stale when it is presented, because the data was pulled a day or two before the meeting. Automating the assembly does not just save the hours. It moves the decision forward by two days, every week.

Show the full model & assumptions
  1. 12 staff × 7.6 hours/week on automatable tasks = 91 hours/week. The 7.6-hour figure comes from the 2026 State of Integration and AI report by Sapio Research, across European knowledge workers.
  2. × 40% addressable by data automation specifically = 36.5 hours/week, or 158/month. Not all automatable work is data work. Data entry and transfer was cited by 33% of respondents as the biggest single drain and report generation by 25%; 40% is a deliberately narrow read of that.
  3. × $38/hour fully loaded = $6,004/month. Adjust for your market — this is a mid-market administrative loaded rate, not a salary.
  4. Net of the $1,797 retainer: roughly $4,200/month. The $3,995 build pays back inside the first quarter.
  5. Recovered hours are redeployed, not eliminated. Modeling this as headcount reduction would produce a larger number and a worse business. Reported outcomes are consistent on this point: 90% of knowledge workers say automation improved their job, largely because the removed work was the work nobody wanted.

What this model excludes: decision quality, which is the actual prize and is not measurable in a spreadsheet; the compounding cost of two departments planning against different numbers; and the 9.3 hours a week researchers attribute to knowledge workers simply searching for information.

Model 03 · Ecommerce & multi-channel

The catalog that's wrong in three places

Catalog & inventory sync

Profile: 14,000 SKUs maintained across an ERP, a webstore and a marketplace listing. Price and stock updates are pushed manually, in batches, when someone gets to it.

Online revenue / mo
$450k
Oversell cancellations
1.4%
Catalog upkeep
25 hrs/wk
Modeled recovery / mo
$9.7k

Stale catalog data fails in two directions at once. Stock you have but haven't listed doesn't sell; stock you've sold but don't have becomes a cancellation, a refund and a customer who checks a competitor next time. Neither shows up as a line item — which is exactly why it runs for years.

Show the full model & assumptions
  1. Oversell cancellations at 1.4% of $450,000 = $6,300/month in reversed orders. Real-time inventory sync does not eliminate these, but a batch-to-live change removes most of the window they occur in. We model 70% recovery, or $4,410.
  2. Catalog upkeep at 25 hours/week × $32 loaded = $3,470/month. Automated sync doesn't take this to zero — call it 80% removed, or $2,776 — because someone still owns merchandising decisions.
  3. Pricing drift: 2% of orders shipping on outdated pricing, averaging 5 points under. On $450,000 that is roughly $450/month of pure margin leakage. Small, and it compounds silently.
  4. Listings not published: 3% of active SKUs missing from a channel at any given time. Modeled at a $2,000/month opportunity cost, which is a guess and is labeled as one.
  5. Total modeled: approximately $9,600/month against a $1,797 retainer. The build here sits nearer the top of the Manifold range because marketplace connectors are fussier than they look.

What this model excludes: the marketplace account health penalty for cancellation rate, which in some programs is severe and non-linear; returns processing labor; and the customer lifetime value lost when a first-time buyer's order is cancelled.

ROI model

Cost your own manual work.

Same conservative assumptions as the models above. Nothing is transmitted — this runs entirely in your browser.

Modeled monthly return Live calculation

Invoices, orders, POs, packing slips — anything keyed by hand.

Saved on document processing
$0
Modeled at $3 per document automated — above the best-in-class published figure.
Labor recovered and redeployed
$0
7.6 automatable hours per person per week, of which 40% is data work specifically.
Modeled monthly return, net of retainer
$0
—
Build fee paid back in
—
Time to recover the one-time setup at the modeled run-rate.
Assumptions: manual document cost as entered, automated cost of $3 per document, 7.6 automatable hours per knowledge worker per week, 40% of that addressable by data automation specifically, 12% of documents still requiring a human. Recovered labor is modeled as redeployed capacity, not headcount reduction. This is a model, not a quote or a forecast.
Pricing

Three packages. One build fee. No mystery.

The build is one-time work and is priced as such. The monthly is not a software license — it is somebody watching the pipelines, handling the exceptions your team escalates, and fixing things the week a vendor changes an API without telling anyone. Ninety-day initial term, then month to month.

PKG-01

Conduit

One bottleneck, removed properly. For businesses with a single obvious place where data gets carried by hand.

$597/ month

Market range for comparable work: $500–$2,000/mo

One-time build$1,995
  • 2 automated pipelines across up to 3 systems
  • Daily sync on a schedule you set
  • AI document extraction, up to 500 documents/month
  • Validation rules & duplicate detection
  • Exception queue with email alerting
  • Monitoring, retry logic & failure alerts
  • One scheduled report or export
  • Documented handover — you own the logic
  • Email support, two business day response
Start with Conduit
Most chosen PKG-02

Manifold

The whole back office wired together. This is where all three case models on this page were priced.

$1,797/ month

Market range for comparable work: $2,800–$7,000/mo

One-time build$3,995
  • Up to 8 pipelines across up to 6 systems
  • Hourly sync, or event-triggered where the system allows
  • AI document extraction, up to 5,000 documents/month
  • Deduplication, merge & enrichment on ingest
  • Automated reconciliation — three-way match, ledger, inventory
  • Standardization & retroactive cleanup of existing records
  • Live dashboard plus scheduled reporting
  • Threshold alerting on the metrics you nominate
  • Full audit trail & data lineage
  • Monthly pipeline health review
  • Shared Slack or Teams channel, same-day response
Start with Manifold
PKG-03

Backbone

Multi-entity, high-volume, or wired into something the vendor stopped supporting in 2011.

$3,900/ month, from

Market range for comparable work: $8,000–$25,000/mo

One-time build, from$7,500
  • Everything in Manifold, without the ceilings
  • Unlimited pipelines, systems and documents
  • Near-real-time sync and change data capture
  • Data warehouse design & build
  • Custom connectors for legacy and on-premise systems
  • Multi-entity, multi-location, multi-currency
  • Migration off an existing integration platform
  • AI-ready dataset preparation
  • Named data engineer, weekly working session
  • Priority build queue & SLA
Scope a Backbone build
Platform costs, billed to you directly
Where a build needs a third-party pipeline tool, warehouse or OCR credits, the account sits in your name. Budget $100–$500/month at typical mid-market volumes. You keep it if we part ways.
Where volume changes the price
Document count and sync frequency are the two real cost drivers. Both are stated on the tier, both are adjustable mid-term, and neither triggers a surprise invoice — we tell you before you cross a ceiling, not after.
What is never extra
Monitoring, break-fix, exception handling, pipeline changes inside your tier, documentation, reporting and the monthly review. If it's in the tier, it's in the price.
Where these prices sit — published 2026 market rates
What you're buying Typical published range Ours
AI automation build, one-time $2,500–$15,000+ $1,995–$7,500+
Ongoing monitoring retainer $500–$5,000+ / mo $597–$3,900+
Small business, 2–3 workflows automated $1,000–$3,500 / mo Conduit, $597
Median SMB–midmarket automation retainer (US/EU) $2,800–$7,000 / mo Manifold, $1,797
Enterprise retainer with a dedicated team $8,000–$25,000 / mo Backbone, from $3,900
ETL / iPaaS platform licence alone (no operator) $100–$1,999+ / mo Tool only — nobody watching it

That last row is the comparison worth making. A pipeline tool is not a solution any more than a table saw is a kitchen. The licence is the cheap part; the expensive part is somebody who knows which fields matter, what the exception should do, and why the vendor's file has had a trailing tab character since March.

Governance & risk

Automation makes bad data faster, too.

That is the honest risk in this category, and it is worth stating before you buy rather than after. A pipeline built without validation doesn't fix a data problem — it industrializes one. Here is how we build against that.

Validation before velocity

Nothing gets loaded until it passes the rules. Format checks, range checks, referential integrity and duplicate detection run on ingest, because prevention is materially cheaper than correcting decay after it has propagated through your reports, scoring models and routing rules.

Fail loud, never silent

Every pipeline is monitored with retry logic and an exception queue. A broken feed raises an alert the same day. A quietly wrong number in a dashboard is the failure mode that actually costs money, and it is the one we design hardest against.

Least privilege & your credentials

Scoped, revocable service accounts in your own systems, secrets held in your vault, no shared logins and no standing access we don't need. You can cut us off in one action without breaking the pipelines.

Lineage you can audit

Every transformation is logged and reversible, so when finance asks where a figure came from there is a traceable answer rather than a confident guess. This matters more the moment you start pointing AI at the same data.

How it works

Three weeks, not three quarters.

Numbered because it genuinely is a sequence — each step depends on the one before it.

01

Trace

We follow one record end to end — where it enters, every hand it passes through, every system it gets retyped into. The map is usually the first time anyone has seen the whole path.

02

Build

Pipelines, validation rules, transformations, exception routing and monitoring, built against your real systems and tested on your real records before anything writes to production.

03

Run

It goes live in parallel first, so you can compare the automated output against the manual one before you trust it. Then we watch it, tune it, and hand you the documentation.

Built on the Emerald Coast · delivered nationwide
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Questions

Asked before every engagement.

What actually is data automation?
Software that moves, validates, cleans and reconciles information between systems without a person retyping it. Concretely: AI pulls the fields off an invoice, writes them into your ERP, matches them against a PO, syncs the result to your CRM and accounting package, flags anything that doesn't reconcile, and has the report built before Monday. It is unglamorous and it is usually the highest-return automation a business can buy, because the work it replaces is happening every single day already.
How much does it cost?
$597/month for a two-pipeline starter build, $1,797/month for a full multi-system program, and from $3,900/month for custom warehouse and real-time builds — plus a one-time build fee of $1,995, $3,995 or from $7,500. Published 2026 figures put AI automation builds at $2,500–$15,000+ with monitoring retainers of $500–$5,000+, and the median SMB-to-midmarket automation retainer at $2,800–$7,000/month. We sit at or below the floor of every one of those bands.
What does bad data really cost?
Gartner's widely cited average is $12.9 million a year, and roughly 60% of organizations don't measure it at all. MIT Sloan research with Cork University Business School puts annual revenue loss from poor data quality at 15–25%. Both are enterprise-weighted, so the absolute dollars are far smaller for a fifty-person company — but the mechanism is identical: the cost never appears at the point of failure, it surfaces downstream as a lost deal, a wrong decision or a compliance problem, long after anyone could trace it back.
Do I have to replace my ERP or CRM?
No — that is the entire point. We build against what you already run, including systems with no modern API, using database connections, file drops or scheduled exports where necessary. Rip-and-replace is a two-year project with a well-documented failure rate. Connecting what you have is usually a three-week one, and it does not require your team to learn anything new.
What happens when something breaks?
It eventually will — an API version changes, a vendor alters a file format, someone adds a column. That is what the monthly fee is for. Every pipeline ships with monitoring, retry logic and an exception queue, so a failure arrives as an alert to us and a held record for you. The dangerous failure mode is not a pipeline that stops; it is one that keeps running and is quietly wrong.
Will this eliminate jobs on my team?
That is your call, not ours, and we will model it either way. Our case models assume redeployment rather than reduction, because the recovered hours are usually the hours nobody wanted — reporting says roughly 90% of knowledge workers feel automation improved their job for exactly that reason. If your intent is headcount reduction, say so during scoping so the build targets the right processes; the design differs.
Who owns the pipelines afterwards?
You do. Accounts in your name, credentials in your vault, logic documented and handed over. If you leave, the pipelines keep running and you get what you need to maintain them. We would rather earn the renewal than engineer a hostage situation.
Can you work alongside our IT team or MSP?
Yes, and it usually goes better when we do. They know the network, the access policy and where the bodies are buried in the legacy system. We handle the pipeline layer and coordinate with them on credentials, change windows and security review. We are not trying to become your IT department.

Benchmark sources

  1. Gartner — cost of poor data quality, averaging $12.9M per organization annually; also the finding that roughly 60% of organizations do not measure it, and $53–$98 as the fully loaded cost of correcting a single data error in financial services.
  2. MIT Sloan Management Review with Cork University Business School — 15–25% of annual revenue lost to poor data quality.
  3. Sapio Research, commissioned by Frends — State of Integration & AI 2026: 7.6 automatable hours per knowledge worker per week (44 working days a year); data entry and transfer cited by 33% as the largest time drain, report generation by 25%.
  4. APQC and Ardent Partners — AP Metrics That Matter: $12–$30 fully loaded manual cost per invoice, roughly $2.78 for best-in-class automated teams, 9% vs 22% exception rates, 3.1 vs 17.4 day cycle times.
  5. IOFM — invoice processing time and cost benchmarks; Institute of Finance & Management AP Benchmarking Report.
  6. McKinsey Global Institute — 69% automation potential for data processing tasks.
  7. Forrester — 248% three-year ROI on enterprise automation deployment; published AP automation payback periods of 3–9 months and 200–600% first-year ROI.
  8. IBM Institute for Business Value, 2025 — 43% of chief operations officers name data quality as their most significant data priority.
  9. Databox SMB Reporting Survey — 71% of SMBs generate reports manually; average weekly management report takes 2.4 hours to produce.
  10. Integrate.io, Hevo and published vendor pricing pages — 2026 ETL and iPaaS platform cost ranges. Taskip and Digital Agency Network — 2026 AI automation build and retainer pricing surveys.

Figures on this page are published third-party averages. Several are enterprise-weighted and will overstate the absolute dollars for a small business, though not the mechanism. The case studies are explicitly modeled scenarios rather than client results, and are labeled as such wherever they appear.

Pick the worst process you have.

Tell us the one everybody complains about — the export, the reconciliation, the report that takes a day. We'll trace it end to end and tell you what automating it is worth. That answer is free and takes about twenty minutes.

Book the trace
Book a call

Tell us what's being retyped.

Twenty minutes. Bring the systems you run, roughly how many documents move through them, and the process your team complains about most — that is usually where we start.

Email
sales@aiagency777.com
Phone
(850) 684-4339
Based in
Navarre, Florida

We reply within one business day. No sequences, no drip, no nonsense.