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Process Automation

AI automation agency for B2B workflows

An AI automation agency for B2B teams: we scope, build and measure AI workflows that remove manual work from finance, sales and operations.

See how we work
Every AI step is validated, routed and measured on a KPI dashboardFlow: email or document, AI extraction, validation, human review for low confidence, update in CRM or ERP, then a KPI dashboard (demo data).1Email or documentArrives in the inbox2AI readsExtracts fields, intent3ValidationRules check confidence4Human reviewLow-confidence items5Update CRM/ERPClean record writtenLowconfidenceHigh confidence6KPI dashboardDemo dataStraight-through72%Hours saved312Exceptions9.4%Every AI step is validated, routed and measured on a KPI dashboardFlow: email or document, AI extraction, validation, human review for low confidence, update in CRM or ERP, then a KPI dashboard (demo data).1Email or documentArrives in the inbox2AI readsExtracts fields, intent3ValidationRules check confidenceHigh confidenceLowconfidence4Human reviewLow-confidence items5Update CRM/ERPClean record written6KPI dashboardDemo dataStraight-through72%Hours saved312Exceptions9.4%
What it is

An AI automation agency builds workflows on clean data and measures them

In short

An AI automation agency designs and builds workflows in which AI models handle steps that used to need a person: reading documents, classifying requests, drafting responses, extracting data or routing work. The agency connects those steps to the systems your teams already use, such as your CRM, ERP, email and shared drives, so the automation fits existing processes instead of adding another tool.

business process automationAirtable and Zapier

  1. Our approach to AI business process automation starts with the process and the data, not the model. We map the workflow, identify where judgment is needed and where it is not, and confirm the data is reliable enough to automate. Then we build with integration tools such as Zapier, Make, n8n and Airtable, combined with large language model steps where they add value.

  2. The final layer is measurement. Each workflow ships with tracking for volume, time saved and exceptions, so leadership can see whether the automation is performing and where to extend it next.

  3. This matters because AI outputs are probabilistic. A workflow that is accurate in testing can drift as inputs change. Governance and data readiness are therefore part of the service, not an afterthought: we define what each AI step may access, when a person must review its output and how errors are caught.

AI steps run on your data and are measured from day oneLayered diagram, bottom up: 1 process and data (CRM, ERP, email, shared drives), 2 integration (Zapier, Make, n8n, Airtable), 3 AI steps (read, classify, draft, extract, route), 4 measurement; governance spans all layers.AI steps run on your data and are measured from day oneHow an AI automation is built, from the data layer up4MeasurementVolume, time saved and exceptions for every workflow3AI stepsReadClassifyDraftExtractRoute2IntegrationZapierMaken8nAirtable1Process and dataCRM, ERP, email and shared drives, mapped andchecked firstStart hereGovernanceData accessHuman reviewError checksChange approvalGovernance applies to every layer.AI steps run on your data and are measured from day oneLayered diagram, bottom up: 1 process and data (CRM, ERP, email, shared drives), 2 integration (Zapier, Make, n8n, Airtable), 3 AI steps (read, classify, draft, extract, route), 4 measurement; governance spans all layers.AI steps run on your data and aremeasured from day one4MeasurementVolume, time saved and exceptions for everyworkflow3AI stepsRead, classify, draft, extract, route2IntegrationZapier, Make, n8n, Airtable1Process and data: start hereCRM, ERP, email and shared drives, mappedand checked firstGovernanceAccess, human review, error checks
Services

AI process automation services cover agents, document AI and AI workflows

Our AI process automation services cover three main patterns, plus supporting data work. A project can combine more than one.

Start here

Requests triaged before anyone opens them

AI agents triage incoming requests, look up records, draft next steps and hand off to a person when confidence is low.

  • Documents become data automatically

    Document AI, also called intelligent document processing, extracts data from invoices, contracts, forms and emails, with validation rules and human review for anything uncertain.

  • Existing processes get smarter

    AI workflows in Zapier, Make or n8n add classification, summaries or enrichment to processes your teams already run.

    Learn more about Existing processes get smarter

  • CRM data you can trust

    AI steps clean, deduplicate, categorize and enrich HubSpot or Salesforce records, so sales and marketing work from reliable data.

  • Reports that explain themselves

    Workflows assemble and check recurring reports, then add a short written summary so managers see what changed.

  • Governed AI from the start

    Each workflow has defined data access, review rules and logging, connected to your wider data governance.

Each service is built on the systems you already run, not on a new platform. Each includes human review where accuracy matters, and each reports its own results. The aim is simple: fewer manual steps, cleaner data and a clear record of what the automation did.

The cards below show what each service does and the outcome it is designed to deliver. We usually start with the one that removes the most manual hours. Each card is a starting point, not a fixed package.

If you are unsure where to start, the assessment ranks candidates by hours saved and data readiness. Each service can run on its own or together.

HubSpotBook a consultation
Use cases

AI agents for business workflow automation free time in every team

AI agents for business workflow automation look different in each team, but the pattern is the same. Find the repetitive step, automate it with checks, and give people the exceptions.

Finance clears its queue faster

Invoice extraction, expense categorization and payment follow-up drafts, with an exception queue for the CFO's team to review.

  • Exception queue for the CFO's team to review
  • Cleaner data entering the ERP

Project teams stop retyping documents

RFI and submittal triage, proposal assembly and timesheet capture for professional services and construction firms.

Leaders get a weekly summary

Workflows compile key operational numbers and a short narrative for managers every Monday morning.

These are typical starting points by team. Each one names the decision or task it speeds up, so you can see where your own first workflow might sit. A common starting point is finance or sales operations, where volumes are high and rules are clear. The first workflow sets the pattern for the rest. It also shows which data needs fixing first. Pick the closest match.

professional servicesconstruction
Benefits

The benefits of an AI automation agency show up in time, speed and data quality

Working with an AI automation agency gives you build capacity and a tested method without hiring a specialist team. You skip much of the trial and error of a first AI project, and you get workflows designed to be maintained.

The benefits below are the ones we design for and measure. Each is tracked on the workflow's own dashboard, so you can see whether it is being delivered.

Requests are now handled in hours, not daysLine chart: median handling time per week fell from about 40 hours before the workflow went live to 3 hours by week 12 (demo data).Requests are now handled in hours, not daysDemo dataMedian hours from arrival to completion, per week, last 12 weeks01020304050W1W2W3W4W5W6W7W8W9W10W11W12Workflow liveAbout 40 hours3 hoursHoursRequests are now handled in hours, not daysLine chart: median handling time per week fell from about 40 hours before the workflow went live to 3 hours by week 12 (demo data).Requests are now handled inhours, not daysDemo dataMedian hours from arrival to completion, per week02550Live3 hoursAbout 40 hW1W12

The benefits grow as more workflows share the same data foundations, monitoring and governance rules, because each new workflow is faster to build than the last. No benefit is claimed that is not measured. Each card states one benefit and how we deliver it.

Skilled staff get time back

AI handles repetitive reading, sorting and data entry, so people focus on exceptions, customers and decisions.

Items handled as they arrive

Requests, leads and documents are processed as they arrive, not when someone has time, which shortens cycle times.

Cleaner data in core systems

Validated extraction improves the data entering your CRM and ERP, and every report built on it.

Value visible from month one

Every workflow reports volume, time saved and exceptions, so the COO and CFO see the return.

AI adoption with controlled risk

Access, data handling and human review are designed in from the start, so risk stays controlled as use spreads.

Capacity that scales with volume

Workflows absorb growth in requests or documents without adding headcount at the same rate.

Challenges we solve

AI automation projects stall on data and maintenance, not on the model

An AI automation project can stall for reasons that have little to do with the model.

Four stall points, each closed before the build startsLifecycle from data to pilot, real volume, run and results, with four stall points (messy data, no exception plan, unquoted run costs, no measurement), each paired with the scoping fix.Four stall points, each closed before the build startsWhere AI automation projects stall, and the scoping step that prevents eachDataPilotReal volumeRunResultsWorks in the demo1Messy CRM or ERPdataFixData readinesscheck2No plan forexceptionsFixException planand monitoring3Run costs neverquotedFixRun costs agreedwith the build4Nobody measuresresultsFixBaseline setbefore go-liveAll four are agreed in scoping, before anything is builtPilots usually work; the stall points appear with real data, real volume and running costs.Four stall points, each closed before the build startsLifecycle from data to pilot, real volume, run and results, with four stall points (messy data, no exception plan, unquoted run costs, no measurement), each paired with the scoping fix.Four stall points, each closed beforethe build starts1DataMessy CRM or ERP dataFix: Data readiness check2Real volumeNo plan for exceptionsFix: Exception plan and monitoring3RunRun costs never quotedFix: Run costs agreed with the build4ResultsNobody measures resultsFix: Baseline set before go-liveAll four are agreed in scoping,before anything is built

We address each of these in scoping. Data readiness, exception handling, run costs and measurement are agreed before we build anything, so the workflow is designed to survive contact with real operations.

We also look at governance early. When AI tools are adopted team by team, there are often no shared rules on which data can be used or who approves changes. For US and UAE companies, those rules also need to reflect local data protection law. We set those rules for each workflow and connect them to your wider data governance, so automation scales without adding risk.

Pricing

You see the full cost of AI automation before you commit

We price AI automation by scope, and we show the full cost of ownership, not only the build. Two pricing models apply: a fixed price per build and an optional monthly run service. You see the assessment, build, run and vendor costs before you commit.

Four cost lines add up to one break-even view before you commitAssessment, build, run and vendor usage, each with how it is billed and what drives it, rolling up to a break-even comparison with manual cost. No figures shown.Cost lineHow it is billedWhat drives it1AssessmentOne-off, before buildCandidate workflows, data readiness2Workflow buildFixed project priceSystems, input variety, review steps3Run and improveOptional monthlyMonitoring, volume, extensions4Vendor usageBilled by vendorsPlatform plan, AI calls per itemBreak-even view: total cost compared with the manual cost it replacesFour cost lines add up to one break-even view before you commitAssessment, build, run and vendor usage, each with how it is billed and what drives it, rolling up to a break-even comparison with manual cost. No figures shown.Four cost lines, one break-even view1AssessmentHow it is billedOne-off, before buildWhat drives itCandidate workflows, data readiness2Workflow buildHow it is billedFixed project priceWhat drives itSystems, input variety, review steps3Run and improveHow it is billedOptional monthlyWhat drives itMonitoring, volume, extensions4Vendor usageHow it is billedBilled by vendorsWhat drives itPlatform plan, AI calls per itemBreak-even view: total cost comparedwith the manual cost it replaces

Know what to automate first

Assessment: a short, fixed-scope review of candidate workflows, data readiness and expected value, ending with a ranked plan.

A build price agreed up front

Workflow build: a fixed-scope project priced on complexity, number of systems and the review steps required.

Workflows that keep improving

Run and improve: an optional monthly service that monitors, maintains and extends workflows through analytics as a service.

No surprise vendor bills

Platform and AI usage costs are billed by vendors directly. We estimate them up front for each workflow.

A clear break-even view

Each plan compares total cost with the manual cost it replaces, so the decision rests on numbers.

Costs reviewed after launch

Actual run costs are compared with the estimate each month, and workflows are tuned or retired if they stop paying back.

The main cost drivers are the number of systems the workflow touches, the variety of inputs it handles, the level of human review and the transaction volume. A workflow that classifies one type of email costs far less than an agent that reads contracts and updates several systems.

We estimate each driver during scoping and agree a fixed price for the build. Run costs are shown per month and per transaction, so the CFO can compare them directly with the manual cost they replace. If the numbers do not justify the build, we say so before any work starts. The same logic applies after go-live: if a workflow stops paying back, we tell you. No figures are hidden. Every estimate is in writing.

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KPIs we track

ROI is measured against a baseline set before go-live

AI process automation should prove its value in numbers your leadership already tracks. We set a baseline before go-live, then report against it in a dashboard. The CFO and COO can see the return on each workflow, compare workflows with each other and decide where to invest next.

GoalMeasured return on each AI workflow
  1. Time returnedReview first
    • Hours returned to the team
    • Review time per item
  2. Speed
    • Cycle time vs baseline
    • Time to first customer reply
  3. Quality
    • Straight-through rate
    • Exception and error rate
  4. Cost
    • Cost per item vs manual cost
    • Monthly run cost vs estimate
Goal, drivers, metrics. The highlighted driver is the one to review first.

Hours returned to the team

Demo

Manual handling time before automation compared with review time after it, by workflow and month.

Work finished sooner

Demo

Cycle time from request or document arrival to completion, compared with the baseline.

More items with no touch

Demo

Straight-through rate: the share of items processed without any human intervention.

Fewer corrections

Demo

Exception and error rate: items flagged for review or corrected after processing.

Lower cost per item

Demo

Total run cost divided by items processed, compared with the manual cost per item.

Faster customer response

Demo

Time from customer request to first reply, compared with the period before automation went live.

Each metric has a named owner and a review date, so results are discussed, not just displayed. Baselines are measured on real volume, not estimated from a demo.

Frequently asked questions

AI automation agency: questions buyers ask before signing a build

These are common questions buyers ask before hiring an AI automation agency. They cover what to automate, which tools we use, how data is protected, what happens to staff, cost, timelines, regional delivery and support after launch.

Why these questions matter

They are the same questions a COO or CFO should ask any AI automation agency before signing. If an agency cannot answer them clearly, treat that as a red flag. Good answers mention data readiness, human review, measurement and maintenance. Weak answers focus only on the AI model or on how fast a demo can be built.

How to use this FAQ

Use the answers below as a checklist when you compare providers, including us. If a question is missing, bring it to a consultation. We would rather answer a hard question before the project than discover it during the build.

What a good answer looks like

A credible answer is specific. It names the systems involved, the data the AI step will see, the point where a person reviews the output and the metric that proves the workflow works. It also covers what happens when something goes wrong: how errors are caught, who is alerted and how the workflow is paused or rolled back.

Vague answers are a risk. If a provider talks only about the model, the demo or how much can be automated, ask how the workflow will be maintained in six months, who owns it and what it will cost to run. Those questions separate a pilot from a dependable business process.

Questions to ask internally first

Before talking to any AI automation agency, agree three things inside your company: which process hurts most today, who owns it, and how you will measure success. With those answers, scoping is faster and the first workflow is easier to measure against a clear baseline.

After you read the answers

Shortlist one or two candidate processes, note who owns them and estimate how many hours they take each month. Bring that list to a consultation. We will test each candidate against data readiness and expected value, and tell you plainly which one to automate first, which to postpone and which are better handled with simple rule-based steps than with AI. The answers below follow.

Look for high-volume, repetitive work that involves reading or sorting information: intake forms, invoices, emails, CRM updates and recurring reports. Good candidates have clear rules for most cases and a defined path for exceptions. They also have an owner who feels the pain today. During the assessment we rank candidates by value, effort and data readiness, so the first workflow is the one with the clearest case and the best base for the next.

We automate through APIs and integration platforms such as Zapier, Make, n8n and Airtable, connected to systems like HubSpot, Salesforce and your ERP. AI steps are added only where judgment or language understanding is needed, such as reading a document or drafting a reply. Building on APIs makes workflows less fragile when screens and layouts change, makes them easier for your team to maintain and avoids lock-in to a single automation vendor.

We design data handling with you before building anything: which data an AI step can see, where it is processed, who can access the outputs and how long records are kept. Workflows run under your own accounts and access controls, not ours. Sensitive steps can include human review before anything is written back to a system. Every workflow is logged, so you can see what the automation did, when it did it and which records it touched.

Our goal is to remove repetitive work, not people. In practice, teams spend less time on data entry and sorting and more time on exceptions, customers and decisions. Roles shift toward reviewing and improving the automation. We also offer AI and data training, so employees understand how each workflow works, trust its output and can suggest improvements. Adoption tends to be stronger when the people who run a process help shape its automation.

Yes. We are based in Dubai, deliver remotely and work with companies in any region, including the US and the UAE. Workflow design accounts for local rules, such as the UAE Personal Data Protection Law and US state privacy laws, when deciding where data is stored and processed. Reviews, support and handover sessions are scheduled across time zones, so your team always has a clear point of contact.

Each workflow ships with monitoring and documentation. You can run it internally, or we can maintain and extend it through a monthly service. Either way, we review performance against the agreed KPIs, such as hours saved, cycle time and exception rate. Prompts, thresholds and rules are tuned as real volume arrives. New cases or teams are added based on measured results, not on assumptions made before launch.

Cost depends on the number of systems a workflow touches, the variety of inputs, the level of human review and the volume of transactions. We start with a fixed-scope assessment, then agree a fixed price for each build. Platform and AI usage costs are billed by the vendors and estimated up front. You see the full cost of ownership, including running costs, before you commit to anything.

Timing for a first workflow is set in the scoping plan. It depends on data readiness, the number of systems involved and how much human review the workflow needs at launch. Results are tracked from the first day against a baseline set before launch. That lets you judge the value early, using real numbers, and decide whether to extend the automation, adjust it or move on to the next candidate on the list.

AI and data training for employees
Comparison

AI automation earns its cost only where a workflow must read or judge

Different automation approaches suit different problems. We build API-based AI automation, because it handles unstructured inputs such as emails and documents and is generally less fragile than screen-based scripts when SaaS tools change.

Only AI automation handles unstructured input without staff timeComparison of AI automation, rule-based automation and manual handling, with the unstructured input row highlighted.How it worksAI reads and judgesFixed if-then logicPeople key each itemUnstructured inputDocuments, email, textStructured fields onlyYes, at staff costMaintenanceModerateLowTraining and coverCost to runPlatform plus AI usePlatform onlyStaff time per itemBest fitJudgment-heavy workSimple handoffsRare, complex casesAI automationRule-basedManualOnly AI automation handles unstructured input without staff timeComparison of AI automation, rule-based automation and manual handling, with the unstructured input row highlighted.AI, rule-based or manualHow it worksAI automationAI reads and judgesRule-basedFixed if-then logicManualPeople key each itemUnstructured inputAI automationDocuments, email, textRule-basedStructured fields onlyManualYes, at staff costMaintenanceAI automationModerateRule-basedLowManualTraining and coverCost to runAI automationPlatform plus AI useRule-basedPlatform onlyManualStaff time per itemBest fitAI automationJudgment-heavy workRule-basedSimple handoffsManualRare, complex cases
AI automationRule-basedManual
How it worksintegrations plus AI steps for reading and judgmentfixed if-then logicpeople read, key and route each item
Unstructured inputdocuments, email and free textstructured fields onlyyes, at the cost of staff time
Maintenancemoderate, in integration toolslowongoing training and cover for absences
Best fitjudgment-heavy B2B workflowssimple structured handoffslow volume or rare, complex cases
Cost to runplatform plus AI usage per itemplatform onlystaff time per item, rising with volume

Rule-based automation, such as simple if-then workflows, is cheap and reliable for structured data but cannot read a document. Manual handling needs no build, but every item costs staff time and errors grow with volume. Many good solutions combine the first two.

In practice, we use rule-based steps wherever rules are enough, and add AI only where a step needs to read, classify or draft. That keeps costs and risk down.

How we work

Six steps take a workflow from idea to measured result

Every AI automation workflow follows the same six steps, whatever the team or tool. The steps keep scope tight, risk visible and results measurable.

From idea to measured result, step by step

Select a stage, use Next or the arrow keys

Scope. Success defined before building. Map the process, volumes, systems and exceptions, and agree the KPIs.

Step 1 of 6

The order matters. We check data readiness before building, because AI on unreliable data produces unreliable output. That check is the gate: if the data or the team is not ready, we recommend the fixes or the training that should come first, and the build waits. We launch with human review, because confidence should be earned on real volume, not assumed from a demo. And we keep improving after go-live, because a workflow that is not maintained slowly stops saving time. Each step ends with a short review with the people who run the process. The process is the same for a single workflow and for a program of ten.

The timing for a first workflow is agreed at scoping and depends on data readiness and the number of systems involved. Later workflows move faster, because the data checks, monitoring and governance rules are already in place.

operations automation
Sample views

An executive summary view shows the hours each AI automation workflow returns

Measurement is part of every AI automation we deliver. The chart shows the executive summary view on demo data: hours saved against the manual baseline, by workflow, last month. Invoice extraction returned 146 of the 312 hours saved across four workflows.

Invoice extraction returns the most hours each monthSorted bars, hours saved last month: invoice extraction 146, request triage 98, CRM record cleanup 44, weekly summary 24; no-touch rates 71%, 65%, 82% and 90% (demo data).Invoice extraction returns the most hours each monthDemo dataExecutive summary view: hours saved against the manual baseline, last month, by workflowNo-touch rateInvoice extraction146 h71%Request triage98 h65%CRM record cleanup44 h82%Weekly summary24 h90%312 hours saved across four workflows last monthInvoice extraction returns the most hours each monthSorted bars, hours saved last month: invoice extraction 146, request triage 98, CRM record cleanup 44, weekly summary 24; no-touch rates 71%, 65%, 82% and 90% (demo data).Invoice extraction returnsthe most hours each monthDemo dataHours saved against the manual baseline, last monthInvoice extraction146 hRequest triage98 hCRM record cleanup44 hWeekly summary24 h312 hours saved across four workflows
1

See hours saved by workflow

Automation performance: items processed, straight-through rate, exceptions and hours saved against baseline.

2

Know where to trust the AI more

Document AI quality: accuracy, fields corrected and review time by document type.

3

Keep run costs in check

Run cost: platform and AI usage per month and per transaction versus manual cost.

4

Prove value to the board

Executive summary: hours saved, cost per item and payback by workflow in one view for leadership.

The four views listed below show how results are reported to leadership each month, before any client data is involved, and which decision each view supports. Each chart has an insight title, so the point is clear without explanation. Demo data only.

Pick one workflow and we will cost it

Choose one process that takes too much of your team's time. We will map it, check the data behind it and give you a scoped plan with the build effort, the expected run costs and the KPIs we would use to measure it after go-live.

The output is useful whether or not you work with us. If you need a broader view first, our AI readiness assessment ranks automation candidates across the business and shows which data foundations need attention before any AI automation agency starts building.

Either route ends with the same thing: a short, costed plan your COO and CFO can review together, with clear assumptions and no commitment required. It tells them three things: what the workflow will cost to build, what it will cost to run each month, and how you will know it is working. That is enough to decide whether to proceed.

AI readiness assessment
See how we work