Automation that removes the work nobody wanted
We automate the repetitive parts of a business process and, where it genuinely helps, put a language model behind a step that used to need a person reading something. Always inside software you control, with the decisions auditable.
What we do, and what we do not claim
Key benefits
What this changes for your business.
Hours back, measurably
We pick processes where the time saved can be counted before the work starts, so the result is not a matter of opinion.
A person stays in the loop where it matters
Automated steps that carry risk route to a human with the evidence attached, rather than deciding silently.
Your own content, actually findable
Semantic search over documents, tickets and knowledge bases, answering from your material rather than from the open web.
Your data boundary is explicit
What is sent to a model provider, what is retained and what never leaves your infrastructure is written down before anything is built.
What we deliver
The things you actually receive.
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Document processing
Extracting structured data from invoices, contracts and forms, with confidence thresholds and a review queue.
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Intelligent search and Q&A
Search across your own documents that understands the question, with citations back to the source.
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Classification and routing
Tickets, emails and requests sorted to the right queue with the right priority, and a fallback when confidence is low.
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Drafting assistants
First drafts of replies, summaries and reports inside your own tools, with a person approving before anything is sent.
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Workflow automation
The unglamorous part: scheduled jobs, event triggers, approvals and integrations that remove a manual handover.
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Internal productivity tools
Small applications that take a recurring internal task and reduce it to one screen.
Core capabilities
The engineering disciplines this service draws on.
Technologies we use
The stack we would reach for, and what each part is for.
OpenAI API
A hosted model API for classification, extraction and drafting. Used as a component inside a product, with the business rules still ours.
Claude API
Anthropic’s model API, used the same way: a capable component behind our own validation, logging and access control.
Laravel
A mature PHP framework for secure, maintainable server-rendered applications and APIs, with authentication, queues and testing built in.
PHP
The language behind a large share of the web, and a fast, strictly typed one since PHP 8.
Node.js
A JavaScript runtime suited to real-time features and API gateways, where many connections spend most of their time waiting.
TypeScript
Static types over JavaScript. On a codebase several people maintain, it turns a class of runtime bugs into compile-time ones.
PostgreSQL
A relational database with strong support for JSON, full-text search and geospatial data, for models that outgrow plain tables.
Redis
An in-memory store used for caching, queues and rate limiting — the difference between a page that waits on the database and one that does not.
Elasticsearch
A search engine for when `LIKE %term%` stops being an answer — typo tolerance, relevance ranking and faceted filtering.
Technology adoption
Technologies in this stack are publicly documented as being used by organisations including those below.
These organisations are named as documented users of the technologies listed. They are not clients of Vertex Arc, and their inclusion does not imply any relationship with or endorsement of Vertex Arc.
Industries we serve
Sectors where this service tends to fit well.
- Professional Services
- Healthcare
- Logistics
- SaaS
- Education
- Media & Publishing
Our delivery process
How an engagement runs, from first conversation to ongoing support.
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Discovery
We work out what the software has to do, who uses it, and which constraints are real. The output is a written scope, not a proposal.
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Architecture
Data model, boundaries, integrations and infrastructure decided and agreed before anybody writes application code.
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Design
Flows and interface, including the empty, error and permission states that decide how the product actually feels.
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Development
Built in reviewable increments against a conventional structure, with tests around the parts that would be expensive to break.
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QA & security
Functional testing, performance checks, and a review of authentication, authorisation and dependency risk before launch.
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Launch
Deployment, monitoring, and a period of close attention while real traffic finds what staging did not.
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Continuous improvement
Patches, upgrades and new work through the support system, so the product keeps being maintained rather than quietly ageing.
Use cases
What this looks like as a finished product.
Inbound request triage
Incoming emails and forms classified, summarised and routed, with the original always one click away.
Invoice and document intake
Line items extracted and matched against purchase orders, with only the mismatches reaching a person.
Internal knowledge search
Staff asking a question in plain language and getting an answer from your own policies, with the source cited.
Why Vertex Arc
We say when you do not need AI
If a deterministic rule solves it, we build the rule. It is cheaper to run, easier to test and it does not hallucinate.
No claims about models we did not build
We integrate established provider APIs. We do not describe them as our own technology, because they are not.
Cost is designed in
Token budgets, caching and model selection per task, so a feature does not become an unbounded monthly bill.
Auditable by design
Every automated decision records its input, its output and its confidence, so it can be reviewed rather than trusted.
Frequently asked questions
Does our data get used to train someone else’s model?
What if the model gets it wrong?
Do you build custom models?
Have a project in mind?
Book a 30-minute call with the engineers who would do the work, or send us the details and we will come back to you.