AI automation
AI automation services, measured in time returned to your team
AI automation services can handle repetitive parts of a workflow while people keep the decisions that need context, judgement or care. We count the manual touches before and after, then report the difference in terms your team can verify.
- trigger: invoice arrives in the AP inboxPDF from the supplierrule
- tool: extract_lines(invoice)header and line items readagent
- tool: three_way_match(po, receipt)quantities and prices comparedrule
- reason: code each line to the ledgercoding kept as a draftagent
- gate: price variance, AP owner reviewsPO and receipt attachedperson
- done: posted to NetSuiteaudit trail per invoiceagent
- trigger: refund request openedZendesk ticketrule
- tool: read_order(order_id)order and payment historyagent
- reason: apply the refund policylimit and terms checkedagent
- tool: draft_reply(policy cited)held for reviewagent
- gate: over the limit, a team lead decidessent with the full contextperson
- done: refund issued, ticket solvedreply and refund loggedagent
- trigger: offer signednew hire record in Workdayrule
- tool: collect_documents(new_hire)ID, tax and bank formsagent
- tool: validate_fields(forms)each check result keptrule
- gate: HR confirms employment eligibilitythe evidence attachedperson
- done: accounts opened before day oneevery system loggedagent
- Years building AI systems
- 6Automation work since before language models were good enough to read a document.
- AI engineers
- 30+The people who build it are the people who run it afterwards.
- Workflow families
- 8Each one has its own page, its own metric and its own failure modes.
- Fewer manual follow-ups in provider credentialing
- 30 to 45%From our provider credentialing case study, where onboarding a provider also went from 2 to 4 weeks to about 1 to 2.
Systems in the loop
Where our AI automation services pick up work and hand it back
NetSuiteInvoices
QuickBooksBookkeeping
Dynamics 365Orders
WorkdayOnboarding
SharePointDocuments- NetSuiteInvoices
- QuickBooksBookkeeping
- Dynamics 365Orders
- WorkdayOnboarding
- SharePointDocuments
SalesforceLead routing
ZendeskTicket triage
JiraChange requests
Microsoft TeamsExceptions
SlackApprovals- SalesforceLead routing
- ZendeskTicket triage
- JiraChange requests
- Microsoft TeamsExceptions
- SlackApprovals
What we do
What AI automation services actually cover
Traditional automation follows a fixed action after a trigger. When an input varies, it usually pauses for review. AI automation can read less predictable documents, apply agreed rules, draft a response, and route uncertain or sensitive cases to the right person with the context attached. The aim is not to remove people from the process; it is to help them spend more time on the exceptions that benefit from their judgement.
There are several sensible ways to automate a workflow. A no-code platform may be enough for steady inputs, modest volume and few exceptions. A configurable product can suit a standard workflow that needs help connecting to your systems. A custom build becomes useful when documents or messages vary, the exception path matters, and your team needs a clear explanation of how each decision was reached.
If a simpler option fits, we will explain that on the first call. The rest of this page covers the workflows we support, a step-by-step example, document handling, and separate build and running costs.
- The unit
- Touches, not jobs
- A touch is one moment a person has to stop and handle something. You can count your own before you call us, and the promise is made in that unit.
- What stays human
- The decisions with consequences
- Every workflow we build names the cases a person keeps, in writing, before the first line of code. That list gets shorter over time and never gets to zero.
- When we say no
- Low volume, few exceptions
- When inputs are consistent, volume is modest and exceptions are rare, a no-code platform may be the more proportionate choice. We confirm that during the workflow review.
- What you get
- The count, both sides
- A step-by-step teardown of the workflow as it runs today and as it runs after, with the touches and the elapsed time on each. Yours whether or not you buy the build.
01Sub-services
The eight workflows we take on
Each of our AI automation services is a family of workflows rather than a single process, and each has a page of its own with the systems, the exception rules and the cost for that workflow alone.
- 01
Business process automation
The general case: a process that crosses three or four systems and stalls at every handover between them.What we measureManual follow-ups: 30 to 45% fewer, in provider credentialing - 02
Intelligent document processing
Reading what arrives as a PDF, a scan or a photograph, and getting the fields out reliably enough to act on.What we measureTime to find a clause in large PDFs: as much as 50 to 60% less - 03
Invoice processing automation
Capture, three-way match against the purchase order and the receipt, coding, and posting to the ledger.What we measureThe share of invoices posted without a touch - 04
Contact center automation
Classifying what came in, gathering the context the answer needs, and drafting a reply that cites the policy behind it.What we measureTickets asking what happens next: 20 to 30% fewer, in a lending agent's trial usage - 05
Back office automation
Reconciliations, checks and transfers that cross several systems and are often shared across teams.What we measureCompleting an application form: about 50 minutes down to under 15 - 06
Customer onboarding automation
Collecting what a new customer has to provide, checking it, and opening them in every system they need before they go live.What we measureOnboarding a provider: 2 to 4 weeks down to about 1 to 2 - 07
Insurance claims automation
The adjuster's preparation on every claim: triage, coverage checks, reserve and settlement drafts and deadline letters, with every determination left to a licensed adjuster.What we measureFile preparation time per claim, before and after - 08
Automated reporting
The recurring reports your team rebuilds by hand, built from source on a schedule, every figure tied out and the commentary approved before it is sent.What we measureTime producing and reviewing performance data: up to 40 to 50% less than by hand
02The teardown
One workflow, before and after
Every engagement in our AI automation services opens by counting what happens now: who touches the work, in what order, and how long each step waits. Here are two of those counts. The figures below are typical figures, not a client's: yours come from your own tally in the first step.
Today
- Touches
- 6
- Elapsed
- 5 days
- AP clerkOpen the invoice, rename it, file it3 min
- AP clerkType the header and the lines into the ledger8 min
- AP clerkSearch for the purchase order and compare by eye6 min
- waitEmail the buyer about a line that does not match2 days
- BuyerConfirms what was delivered, replies10 min
- AP leadSecond review, checks the coding4 min
- AP leadApprove and add to the payment run2 min
After
- Touches
- 1
- Elapsed
- Same day
- agentRead the invoice, pull the header and every lineUnder 1 min
- agentMatch against the purchase order and the receiptSeconds
- AP ownerReviews only what fell outside the threshold2 min
- agentPost to the ledger and write the audit trailSeconds
- A person keeps
- Any new supplier, any invoice with no matching purchase order, and every variance above the threshold agreed at kickoff.
- Systems crossed
- Shared inbox, extraction, ERP, approval chain, banking file
- When it stops
- A mismatch goes to the accounts payable owner with the purchase order, the receipt and the extracted values shown side by side, so the decision takes seconds rather than a search.
Today
- Touches
- 3
- Elapsed
- 2 days
- Tier 1Read it, categorise it, tag it4 min
- waitWaits in the queue for an available reviewer1 day
- Tier 1Look up the order and the payment history6 min
- waitReassigned because the policy needs checking1 day
- Tier 2Write the reply, issue the refund8 min
After
- Touches
- 1
- Elapsed
- Under 1 hour
- agentClassify, pull the order and payment contextSeconds
- agentApply the refund policy, draft a reply that cites itSeconds
- Tier 1Approves or edits before it sends2 min
- A person keeps
- Disputes, cancellations, anything above the refund limit, and every message where the customer is already unhappy.
- Systems crossed
- Helpdesk, order history, payments, email
- When it stops
- Low confidence on the intent, or a tone the model is unsure about, and it stops: the draft and the full context go to a named person rather than to a queue.
03Documents
Which documents automation can process, and where people review
The honest answer varies more by document than by technology. These are the rates our AI automation services plan against: typical figures, not a client's and not best-case figures from a demo, and every one of them assumes the document arrives the way it usually arrives rather than the way it should. Yours are measured on a sample of your own documents in the first step.
| Document | What makes it hard | Typically goes through untouched | What a person still does |
|---|---|---|---|
| Structured invoice | Nothing much, until a supplier changes their template without telling anyone. | 80 to 90% | Confirms new supplier layouts once, then never again for that supplier. |
| Purchase order | Usually clean, but line items split across pages break the mapping. | 75 to 85% | Resolves split or continued lines. |
| Remittance advice | One payment against many invoices, in whatever layout the payer's bank produces. | 50 to 70% | Allocates the remainder when the total does not reconcile. |
| Delivery note | Photographed on a phone, at an angle, often with a signature over the text. | 40 to 60% | Reviews captures that are blurred, cropped or otherwise unreadable. |
| Claim form | Handwriting, ticked boxes, and free text that contradicts the boxes. | 30 to 50% | Resolves every contradiction, because an incorrect reading here can become a regulated error. |
| Scanned contract | The fact you want is a clause, not a field, and its meaning depends on three other clauses. | None, by design | Reviews every extraction. This one assists, it does not decide. |
04Fit
Where the automation sits in your stack
Our AI automation services sit beside your systems, not between them. Nothing is replaced, nothing proxies your traffic, and if the automation is switched off on a Friday the workflow goes back to the way it ran on Thursday. That constraint rules out some designs and it is worth what it costs.
Every connection gets its own credential, scoped to the objects it needs and nothing else, and every write is logged with the input that caused it. When a reviewer asks why a particular invoice was coded that way, the answer is available as a record rather than a reconstruction.
Finance and ERP
- NetSuite
- SAP
- Microsoft Dynamics
- Xero
- QuickBooks
- Sage
Service and support
- Zendesk
- Salesforce Service Cloud
- Freshdesk
- Intercom
- Jira Service Management
Documents and storage
- SharePoint
- Google Drive
- Box
- Dropbox
- Amazon S3
Messaging and mail
- Microsoft 365
- Google Workspace
- Slack
- Microsoft Teams
Anything else
- REST
- GraphQL
- SFTP
- Database
- Screen level, where there is no other way
05Delivery
How our AI automation services run, step by step
- 011 week
Count it
We sit with the people doing the work and count the steps, the waits and the exceptions. You end this step holding the teardown of your own workflow and a fixed quote for the build, whether or not you go ahead with us.
- 023–6 weeks
Build the narrow version
One workflow, one document type, the exception path built first rather than last. You end this step with it running against real inputs in a place where nothing it does is final.
- 031–2 weeks
Run it in parallel
The automation and your team do the same work at the same time and we compare every disagreement. You end this step with the measured rate on your own volume, which is the number the business case should have been built on.
- 04Agreed in scope
Hand over the switch
Monitoring, alerting, the runbook and the off switch, in your hands and tested. You end this step able to turn it off without calling us, which is the thing that makes it safe to leave on.
06Cost
What AI automation services cost
One workflow
Build $10k–$53k · Run cost modelled at your volumeA single process, one document type or one message type, one system of record. The shape most first engagements take.
A workflow family
Build $16k–$82k · Run cost modelled at your volumeThree or four related processes sharing one extraction layer and one exception queue, across two or three systems.
A function
Build $26k–$153k · Run cost modelled at your volumeEverything one team does that fits this pattern, with routing between the workflows and a single view of what is waiting on a person.
Run it for you
$9k–$25k a month, based on volume and support needsWe hold the pager. Model changes, supplier template changes, rate monitoring and the monthly report on what the automation did and what it refused.
The pod
Who works on an AI automation project
Five roles on a typical engagement of our AI automation services. The same people stay on it from the first count through to the handover, and you have their names before a contract exists.
| Role | Owns | Allocation |
|---|---|---|
| Automation engineer | The workflow logic, the exception rules and what the system is not allowed to decide | Set in scope |
| AI engineer | Extraction, classification, confidence thresholds and the cost per item | Set in scope |
| Integration engineer | Connectors, credentials, retries and what happens when a system is down | Set in scope |
| Delivery lead | The count, the fixed quote, your people's time and what stays out of scope | Set in scope |
| Process owner, yours | The rules, the thresholds and the decision about what a person keeps | Set in scope |
07 · Industries
AI automation services by sector
- InsuranceClaim intake is the obvious candidate and the hardest one, because a misread field becomes a regulated error rather than a rework.
- BankingOnboarding and reconciliation, where the control framework already exists and the automation has to fit inside it rather than beside it.
- HealthcareReferrals, prior authorisation and coding, on documents that arrive as faxes more often than anyone outside the sector expects.
- ManufacturingSupplier documents and quality records, against plant systems that predate the API and will outlast this project.
- LogisticsDelivery notes and proof of delivery photographed in a yard, where the input quality is the entire engineering problem.
- RetailSupplier invoices and customer messages with a seasonal peak that decides the architecture more than the average volume does.
- ProcurementPurchase orders, three-way matching and supplier onboarding, where the saving is per transaction and the volume makes it real.
- HRJoiners, leavers and the work they trigger across systems shared by several teams, which is why coordination often remains manual.
08 · Work
Workflows we have rebuilt
- Healthcare · Provider credentialingProvider onboarding: 2 to 4 weeks down to about 1 to 2Invitations, documents and status updates run as one tracked workflow, with 30 to 45% fewer manual follow-ups. The file review stays with the credentialing team.Read the case study
- Government services · Application formsAverage completion: about 50 minutes down to under 15Passport and visa forms chosen, filled in and checked by rule-based assistants before submission. The passport office still decides every application.Read the case study
- Dental practices · Business performance reportingUp to 40 to 50% less time producing and reviewing business performance dataOperational and financial data brought into one platform with dashboards, measured against manual reporting. What the numbers mean for the business stays with the practice.Read the case study
Read before you buy AI automation services
All guides- GuideAI agent costWhat automation, agents and assistants cost to build and to run, by tier, and the factors that move the number.
- GuideAI automation companiesWhich firms publish a working automation for a client you can look up, and the questions to ask any firm before a shortlist turns into a contract.
FAQs
Questions buyers ask about AI automation services
Clear answers on cost, when a simpler option may fit, how exceptions are handled and who owns the system afterwards.
How much do AI automation services cost?
One workflow is typically $10k–$53k to build. A workflow family is usually $16k–$82k, and automating a whole function is typically $26k–$153k. Managed operation is $9k–$25k per month. We quote build and running costs separately because exceptions shape the build while volume shapes ongoing cost.
Could we just use a no-code platform instead of AI automation services?
Often, yes, and we would rather say so now. If your inputs arrive in a consistent format, your volume is modest and your exception rate is low, a no-code platform will do the job for tens of dollars a month and you do not need to buy AI automation services at all. It stops working when the input varies, when the exception path matters more than the happy path, or when someone has to be able to explain a decision months later. That is the point where a built system earns the difference, and if you are not at that point we will tell you on the first call.
What is the difference between AI automation, RPA and intelligent automation services?
Rule-based automation follows a script against a predictable screen or file, and stops the moment reality varies from the script. AI automation reads what actually arrived, decides which of your rules apply, and routes what it is not confident about to a person. In practice most workflows want both: deterministic steps where the input is fixed, and a model only where judgement is genuinely required. That combination is what some firms sell as intelligent automation services. Using a model for a step that a rule handles is how automation gets expensive for no reason.
Which tasks are actually worth automating?
Look for high volume, many manual touches and decisions that follow rules your team can explain. Start with the workflow where the people doing the work can identify the step that takes the most time. Automation may not be proportionate for work that happens only a few times a month, changes completely each time, or still requires personal review in every case.
Will AI automation services work with the systems we already run?
The connector groups above list what we connect to most often, and anything with a REST or GraphQL API, an SFTP drop or a database is straightforward. Where there is no interface at all we work at the screen level, which is reliable enough in practice and roughly doubles that part of the build. The automation sits beside your systems rather than between them, so switching it off returns the workflow to how it ran before rather than breaking it.
What happens when the automation produces an incorrect result?
It should stop rather than guess, and that is a design decision made before the build. Every workflow gets a confidence threshold and an exception route to a named person, with the full context attached so the decision takes seconds. Errors that do get through are caught by the parallel run in step three and by monitoring afterwards. You also get an off switch you can use without calling us, which is the thing that makes it reasonable to leave running.
How long do AI automation services take?
The workflow review usually takes one week and ends with a teardown and fixed quote. A narrow working version typically follows in three to six weeks, then runs alongside your team for one to two weeks so disagreements can be reviewed on your real volume before responsibility is widened.
How secure is it, and where does our data go?
It runs in your cloud account and region unless you agree another arrangement. Every connector receives the narrowest credential it needs, retention is agreed before ingestion, and document content is not used to train shared models. We provide current security and compliance evidence during scope so your reviewers can assess it directly. If content cannot leave your network, we design within that boundary and explain the model options available.
Who owns the automation once it is built?
You do. The code, rules, prompts and connector configuration sit in your repository, and the runbook is written so another engineer can take it over. We plan the handover with your named owner, or continue under a separately agreed managed-service arrangement.
Should we hire an AI automation agency or do this in-house?
We are the agency, and we deliver our AI automation services ourselves rather than subcontracting them. If you already have an internal automation team, the pattern that works is us building the first workflow with two of your engineers in the room, then your team taking the next ones. That is a smaller engagement for us and it is usually the right answer for a company that intends to do a lot of this.
Send us one workflow. We will help you map it.
Describe one process in a few lines: what arrives, who touches it, and where it waits. An engineer replies within two business days with the touch count as we read it, the part we would automate first, the part we would leave alone, and a build and run figure for that workflow.
Reply within two business days · The teardown is yours either way · We will tell you if a platform would do it