How is this different from just buying Zapier or n8n?

Those are the tools, not the outcome. The hard part is deciding what to automate, handling the failure cases, and making the automation survive contact with real data. A workflow that breaks silently is worse than no workflow, so we build in error handling, alerting and logging, then hand you documentation for how it all fits together.

How long does an automation build take?

A single focused workflow such as lead routing or a reporting pipeline usually ships in one to two weeks. Multi-system builds involving a CRM, billing and support tooling typically run four to eight weeks. We scope the smallest useful piece first so you see something working early rather than waiting on a big-bang launch.

Do we need AI, or is this just workflow automation?

Often you do not need AI at all, and we will say so. Deterministic automation is cheaper, faster and more reliable for anything rule-based. AI earns its place where the input is unstructured language or the decision needs judgement: classifying inbound enquiries, drafting replies, summarising calls, extracting data from documents.

Who owns the automations you build?

You do, entirely. Workflows live in your accounts, credentials stay yours, and you get the documentation. If you stop working with us, nothing switches off and nothing is held hostage.

What tools do you build on?

n8n, Make and Zapier for orchestration, OpenAI and other LLM providers for the AI layer, and direct API integrations where a platform connector is not good enough. We pick based on what your stack already uses and what you can maintain, not on what we prefer.

AI Automation

AI Automation Services: Your Team Thinks, We Automate the Rest

Vedixx is an AI automation agency. We build systems that take over the repetitive, rule-based work your team repeats every week — lead routing, onboarding, reporting, support triage — using n8n, Make, Zapier and large language models. Every workflow is documented, monitored, and owned by you.

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What is an AI automation agency?

An AI automation agency designs and builds systems that carry out repetitive business processes without human intervention, combining workflow automation tools with artificial intelligence. Unlike a software vendor selling a fixed product, an agency audits how a specific business actually operates, then builds custom workflows connecting that company’s existing tools — typically a CRM, help desk, billing system and communication channels — and maintains them as the business changes.

What's included

Automation audit

We map what your team actually does each week, time it, and rank tasks by hours recovered against build effort. You get the priority list as a deliverable even if you never hire us to build it.

Workflow design & build

Orchestration in n8n, Make or Zapier depending on your stack — with error handling, retries, dead-letter queues and alerting, so a failure surfaces immediately instead of silently dropping records for three weeks.

AI where it earns its place

Language models applied only to the parts that genuinely need judgement: classifying enquiries, drafting replies, summarising calls, extracting structured data from unstructured documents.

System integration

Your CRM, help desk, billing, forms and spreadsheets connected through documented APIs, so data stops being re-keyed between tools that were never introduced to each other.

Monitoring & observability

Every run logged with inputs and outputs, failures alerted to a channel a human actually reads, and a dashboard showing what ran and what did not. An automation nobody can observe is a liability.

Handover & documentation

Architecture diagrams, credentials held in your own accounts, and a written explanation of how each workflow behaves under failure. You can hand the whole thing to another developer tomorrow.

How an automation actually runs

Almost every workflow we build follows this shape. The steps most agencies skip are the last two — which is precisely why so many automations quietly stop working within a few months of launch.

1
Trigger
A form submission, inbound email, webhook, new CRM record, or a schedule. This is where the process starts and where deduplication has to happen.
2
Enrich
Pull the context the decision needs: company data, order history, previous conversations, account status. Bad decisions are usually missing-context problems.
3
Decide
Rules where the logic is deterministic, a language model where the input is unstructured or genuinely needs judgement. Most steps should be rules.
4
Act
Write to the CRM, send the reply, book the slot, raise the ticket, notify the owner. Validated before it writes, so bad output never reaches a customer.
5
Log & alert
Record what happened and why, and alert a human when something fails or falls outside expected bounds. This is what makes the system maintainable.

Where it pays for itself

Instant lead routing

The problem: Enquiries land in a shared inbox overnight and on weekends. Whoever gets to them first replies, sometimes hours later, sometimes twice, sometimes not at all. Nobody can say which leads were followed up.
What we build: A workflow that captures every enquiry, enriches it with company data, scores it against your qualification criteria, assigns an owner by territory or specialism, creates the CRM record, and posts it to the right channel with a one-line summary.
The result: Response time falls from hours to seconds, every lead has a named owner, and duplicate outreach stops.

Client onboarding

The problem: A signed contract kicks off fifteen manual steps across five tools — folder creation, kickoff scheduling, invoice raising, welcome sequence, access provisioning. Steps get skipped when the team is busy, and the skipped ones surface as complaints later.
What we build: One trigger on contract signature that provisions accounts, creates the project structure, schedules kickoff, raises the first invoice, and starts the communication sequence — with a checklist that tracks completion rather than trusting memory.
The result: Onboarding runs identically every time and takes minutes of human attention instead of an afternoon.

Support triage

The problem: The support queue mixes password resets with genuine escalations. Senior staff spend their morning reading tickets to decide which ones matter, and urgent issues sit behind trivia.
What we build: An AI classification layer that reads each incoming ticket, tags it by intent, urgency and product area, drafts a reply for the routine categories, and escalates anything ambiguous with a summary attached.
The result: Routine tickets are answered immediately and escalations reach a human first, not last.

Reporting that assembles itself

The problem: Someone spends the first morning of every month exporting CSVs from four platforms, pasting them into a spreadsheet, and rebuilding the same charts. The report is stale before it is circulated.
What we build: A scheduled pipeline that pulls from each source API, normalises the data into one store, calculates the metrics consistently, and publishes a live dashboard plus a written summary.
The result: A recurring day of manual work disappears and the numbers are current rather than a month old.

CRM hygiene

The problem: Records are duplicated, half the fields are empty, deal stages are out of date, and nobody trusts the pipeline number in the weekly meeting.
What we build: Continuous deduplication, enrichment from external sources, stage-change validation, and nudges to owners when a record goes stale — running quietly in the background rather than as a quarterly cleanup project.
The result: The pipeline figure becomes something you can plan against instead of argue about.

Document and invoice processing

The problem: Invoices, purchase orders and supplier documents arrive as PDFs and get typed into the accounting system by hand, with the transcription errors that always follow.
What we build: Extraction of structured fields from each document, validation against expected ranges and existing records, automatic entry for clean matches, and a review queue for anything that fails a check.
The result: Most documents process without a human, and the ones that need attention are flagged with the reason.

How it works

1
Map the work
A working session to list the repetitive tasks, who does them, how often, how long they take, and what breaks when they are missed. Most teams have never counted this, and the weekly total is usually the moment the project justifies itself.
2
Pick the first constraint
We scope the single workflow with the best ratio of hours saved to build effort — deliberately not the most impressive one. An early, visible win funds the rest and proves the approach to the people who will have to trust it.
3
Design for failure first
Before building the happy path we define what happens when an API is down, a record is malformed, or a model returns nonsense. Automations do not fail loudly by default; that behaviour has to be built in.
4
Build and test on real data
Automations are built against your actual records, including the messy historical edge cases, because synthetic test data hides exactly the failures that matter in production.
5
Ship, monitor, expand
Live with logging and alerting from day one, watched closely for the first two weeks, then handed over with documentation. Once stable and measured, we move to the next constraint on the list.

Typical timeline

Phase	Duration	What you get
Automation audit	3–5 days	Prioritised list of automatable processes with estimated hours recovered and build effort for each. Yours to keep.
First workflow	1–2 weeks	One production workflow live, monitored and documented — usually lead routing or a reporting pipeline.
Core integrations	3–6 weeks	CRM, help desk and billing connected, with deduplication, validation and error handling across the set.
AI layer	2–4 weeks	Classification, drafting or extraction added where judgement is genuinely required, with evaluation against real inputs.
Handover or retainer	Ongoing	Full documentation and diagrams, plus optional monitoring and iteration as your processes change.

When to use rules, and when to use AI

This is the decision most AI projects get wrong, and it is expensive in both directions. Rules are cheaper, faster and reproducible; models handle ambiguity but cost more and can be wrong in ways that are hard to predict. A good automation uses both, in the right places. Most agencies will not tell you that roughly eighty percent of a typical build needs no AI at all.

	Deterministic rules	Language model
Best for	Fixed logic: routing by territory, threshold checks, scheduled exports, field mapping.	Unstructured input: free-text enquiries, call transcripts, PDFs, mixed-intent messages.
Cost per run	Effectively zero.	Per-token, and it compounds with volume and context size.
Reproducibility	Identical output for identical input, every time.	Variable. Needs evaluation, constraints and output validation.
Failure mode	Breaks visibly — an error you can trace.	Fails plausibly. Produces confident, wrong output unless guarded.
Maintenance	Change the rule when the process changes.	Prompts, context and model versions all drift and need review.

Technologies we build on

Orchestration

n8n (self-hosted or cloud)
Make (Integromat)
Zapier
Custom Node.js services

AI layer

OpenAI (GPT)
Anthropic (Claude)
Open-weight models
Vector search & retrieval

Data

PostgreSQL
Airtable
Google Sheets
Supabase

Interfaces

REST & GraphQL APIs
Webhooks
Queues & schedulers
Internal dashboards

Systems we connect

HubSpotSalesforcePipedriveZendeskIntercomSlackNotionAirtableStripeShopifyWooCommerceGmail & OutlookWhatsApp Business APICalendlyXero & QuickBooksGoogle Workspace

Who this is for

Agencies & professional services

Onboarding, reporting and client communication are the same steps repeated per client — the clearest automation case there is.

E-commerce

Order exceptions, returns, supplier updates and post-purchase sequences, connected across store, help desk and finance.

B2B & SaaS

Lead enrichment, trial-to-paid nudges, churn signals and usage-based alerts routed to the right owner.

Real estate

Enquiry routing by area, viewing scheduling, document collection and follow-up sequences that do not depend on memory.

Healthcare & clinics

Appointment reminders, intake forms and records admin — built with the data handling those obligations require.

Logistics & operations

Status updates, exception handling and supplier communication where delays are expensive and manual chasing is the norm.

What drives the cost

We do not publish fixed prices, because an honest number depends on your systems rather than on our rate card. What we can tell you is exactly what moves the figure — and if a project is not worth the build cost, we will say so during the audit rather than after the invoice.

Number of systems involved

A workflow inside one platform is straightforward. Each additional system adds authentication, rate limits, field mapping and a new way for things to break. This is the single biggest cost driver.

Quality of your existing data

Clean, consistent records make automation cheap. Duplicates, free-text fields where there should be options, and three conflicting sources of truth all mean cleanup work before anything can be automated reliably.

How much judgement is required

Rule-based routing is quick. Anything needing a model requires evaluation, prompt work, output validation and ongoing review — a materially larger commitment than a deterministic workflow.

Volume and reliability requirements

A workflow running fifty times a day is different engineering from one running fifty thousand times, and a process where failure costs a customer needs redundancy that a nice-to-have does not.

Compliance and data handling

Regulated data, residency requirements or a rule that nothing leaves your infrastructure will point toward self-hosted tooling and open models, which changes both build and running costs.

Who maintains it afterwards

Building for handover to your team costs more up front — more documentation, simpler architecture, fewer clever shortcuts — and less over the following year. Worth deciding before we start, not after.

Fixed-scope audit

You want the prioritised list and the business case before committing to a build. Deliverable is yours regardless of what happens next.

Project build

A defined set of workflows, scoped and quoted after the audit, delivered with documentation and handover.

Ongoing retainer

Processes change and automations need maintaining. Suits teams treating automation as continuous rather than a one-off project.

What you end up with

Leads routed and responded to in seconds instead of hours
Manual re-keying between systems largely eliminated
Reporting that assembles itself rather than consuming a morning
Support questions triaged and answered before a human sees them
Documented workflows you own, running in your own accounts
Logs and alerts showing what ran, what failed, and why

Key takeaways

Most of a good automation build uses no AI at all — deterministic rules are cheaper, faster and reproducible, and a competent partner will tell you where AI is unnecessary.
The work that makes automation durable is error handling, logging and alerting, not the happy path. Automations that fail silently are the norm, and they are worse than no automation.
The best first project is the one with the highest hours-saved to build-effort ratio, not the most impressive demo.
Automation quality is capped by data quality. Duplicated and inconsistent records mean cleanup comes first.
You should own the workflows, the credentials and the documentation. If leaving your agency would break the system, the system was built wrong.

Related services

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AI Integration

Connecting AI models to the systems you already run.

Workflow Automation

Multi-step processes that run without supervision.

Business Process Automation

End-to-end process redesign, then automation.

CRM Automation

Pipeline hygiene and routing that maintains itself.

Marketing Automation

Lifecycle campaigns triggered by real behaviour.

WhatsApp Automation

Conversational flows on the channel this market actually uses.

FAQ

Questions? Answered.

We find the repetitive, rule-based work your team does every week and replace it with systems that run on their own. In practice that means connecting the tools you already pay for, adding AI where judgement is genuinely needed, and leaving a documented workflow you own. Typical first projects are lead routing, client onboarding, reporting, and support triage.

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