Process audit
We measure where the hours actually go before proposing anything, and rank candidates by hours saved against effort.
14 / Services
Automate the work nobody should be doing by hand
Workflow automation and AI integrations that remove repetitive manual work — built on the systems you already run, with a human in the loop wherever it matters.
- Projects delivered
- 50+
- Years of experience
- 10+
- Countries served
- 15+
- Typical first phase
- 3–8 wks
START WITH THE WORK NOT WITH THE MODEL
Most AI projects fail for an unglamorous reason: they start with the technology and go looking for a use. The ones that succeed start with a specific piece of work that is repetitive, high volume and expensive in human hours — and then ask whether automation, AI or a simple rule would remove it.
Quite often the answer does not need AI at all. Copying orders between two systems is an integration problem. Chasing approvals is a workflow problem. Both are cheaper and more reliable to solve with ordinary software, and we will say so rather than sell you a model.
Where AI does earn its place is in the messy middle: reading unstructured documents, summarising long threads, classifying incoming requests, drafting responses a person then approves. In those cases we build the automation around the model carefully — with validation, fallbacks and a human review step — because a confidently wrong answer at scale is worse than no automation.
SIGNS YOU HAVE SOMETHING TO AUTOMATE
If several of these are true, there is almost certainly an automation worth building.
Book a process audit- Common warning signs
- Someone spends hours each week copying data between systems
- Growth in orders means hiring more people to process them
- Information arrives as PDFs and email and is retyped by hand
- Mistakes come from tiredness and repetition rather than judgement
- Reports are assembled manually every week
- Your team is doing work that is obviously mechanical
WHERE AUTOMATION ACTUALLY PAYS
Three patterns that come up in nearly every business we look at.
Data movement
The same record typed twice
Orders, leads, invoices and stock copied between systems by a person. This is pure integration work, it is reliable once built, and it usually returns the investment fastest of anything on this page.
Documents
Information trapped in PDFs and email
Invoices, forms, contracts and spec sheets read by hand and retyped into a system. This is where AI genuinely helps — extracting fields, with confidence thresholds and a review queue for anything uncertain.
Triage
Sorting and routing requests
Incoming enquiries, support tickets and applications classified and routed, with a draft response prepared for a person to approve. The human stays in charge; the sorting stops eating the morning.
WHAT THE WORK INVOLVES
Automation that survives contact with a real business, not a demo.
Integrations
Connecting the systems you already run, so data moves without a person in the middle.
AI where it fits
Extraction, classification, summarising and drafting — with prompts, validation and thresholds tuned on your real data.
Human in the loop
Review queues and approval steps wherever a wrong answer would be costly.
Guardrails & fallbacks
What happens when the model is unsure, an API is down, or input looks unusual. Designed in, not discovered later.
Monitoring & reporting
Logs, alerts and a simple report of what ran, what was flagged and what it saved.
If a process is not written down, automating it just makes the confusion faster.
Find your first automationWHAT WE AUTOMATE
The processes businesses most often hand over first.
Order & lead sync
Store, CRM, accounting and ERP kept aligned without manual entry.
Document processing
Invoices, forms and contracts read, extracted and filed with a review step.
Support triage
Tickets classified, routed and drafted for human approval.
Content operations
Product descriptions, translations and metadata drafted at scale, then edited.
Reporting
Recurring reports assembled and delivered instead of collated by hand.
Internal assistants
Search and question answering over your own documents, with citations.
HOW WE BUILD IT
Practical tools, chosen so you are not locked to one vendor.
Your existing systems
CRM, ERP, store, helpdesk and accounting, connected over their APIs.
Custom services
Laravel or Node services for the logic, queues and retries.
AI models
Commercial or open models chosen per task, behind an interface we can swap.
Dashboards
A simple interface for review queues, logs and results.
HOW A PROJECT RUNS
Five stages, and a deliberately small first automation.
-
01
Audit
We follow the work, measure the hours and list candidates ranked by value against effort.
-
02
Pick one
We start with a single process that is high volume, well understood and low risk if it needs correcting.
-
03
Prototype on real data
Built against your actual records, not a clean sample, and measured for accuracy before anyone relies on it.
-
04
Deploy with a human step
Released with a review queue, so the team sees what it does before trusting it unattended.
-
05
Measure, then widen
Hours saved and error rates reviewed, then the next process is automated using what we learned.
HOW LONG IT TAKES
A first automation usually runs 3–8 weeks. Later ones are faster, because the plumbing already exists.
| Stage | Typical time | What you receive |
|---|---|---|
| Process audit | 1 week | Measured hours, ranked candidates, recommendation |
| Prototype | 1–2 weeks | Working prototype on your real data, with accuracy numbers |
| Build | 1–3 weeks | Integrations, queues, review interface, guardrails |
| Pilot with review | 1–2 weeks | Live with a human approval step and monitoring |
| Review & extend | Ongoing | Hours saved measured, next process selected |
WHAT AUTOMATION COSTS
Priced per automation after the audit, so you can judge it against the hours it removes. We also estimate running costs — model usage and infrastructure — because an automation that costs more than the person it replaced is not a saving.
Get your estimate- What moves the price
- How many systems have to be connected, and how good their APIs are
- Whether the inputs are structured data or messy documents
- Accuracy required, and the cost of a wrong answer
- Volume, which drives both infrastructure and model cost
- How much review interface the team needs
- Whether one process or a series is in scope
RECENT WORK
NeuralSync
Enterprise AI Workflow Automation
High-volume business logic, CRM updates and data pipelines were running on manual operations work — slow, expensive and easy to get wrong.
Read case study
Nexus AI
Investor-Ready AI SaaS MVP
A founding team needed a working product to put in front of investors — without an in-house engineering team and without a year of development.
Read case studyRELATED SERVICES
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Laravel applications, APIs and portals — built for businesses whose workflow does not fit any off-the-shelf tool, and maintained so they still work in three years.
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Updates, backups, security monitoring, uptime checks and the small changes you need every month — handled by the team that builds websites for a living, not a support queue.
Learn moreAI AUTOMATION QUESTIONS
What businesses ask before automating a process.
Still have a question? Ask us on WhatsApp
Frequently just automation. If the inputs are structured and the rules are clear, ordinary software is cheaper, faster and more reliable. AI earns its place with messy inputs like documents and free text.
Work that is repetitive, high volume and well defined. Judgement calls, relationships and exceptions stay with people — usually helped by automation rather than replaced by it.
That depends on the task, and we measure it on your real data during the prototype before anything goes live. Where accuracy is not high enough to run unattended, the automation prepares the work and a person approves it.
Not without your agreement. We choose providers and settings according to your requirements, and where data cannot leave your environment we look at models that can run inside it.
It is designed for that: confidence thresholds, review queues, logs and alerts. Every automation we build assumes it will be wrong sometimes.
In our experience it removes the dull parts of jobs rather than the jobs. The realistic outcome is that the same team handles more volume without hiring for coordination work.
Model usage and infrastructure, which scale with volume. We estimate these up front and monitor them, because an automation that costs more than the manual work is not worth keeping.
Yes. The point is to automate around the systems you already use, not to replace them.
With a process audit: we measure where the hours go and rank the candidates. Often the first recommendation is a simple integration rather than anything involving AI.
You do — code, prompts, configuration and documentation are handed over, and nothing is locked to a single vendor where we can avoid it.
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