07 — Core service

AI, automation, applications and integrations that remove manual work and connect the systems behind it.

What this is

Most AI projects fail on the workflow, not the model.

We build the software layer a business runs on: AI applied to real workflows, automation for the repetitive middle of a process, custom applications and portals, and the integrations that stop data being moved by hand.

Getting a convincing answer out of a model is the easy part. What decides whether something survives contact with a business is everything around it — where the knowledge comes from, what happens when it is wrong, who reviews it, and how it connects to the systems people already use.

01

Start with the task, not the technology

The projects that work begin with a specific repeated task that has a measurable cost, not a decision to use AI.

02

Design for being wrong

Confidence thresholds, review steps and escalation paths are what make an automated system safe to leave running.

03

Integration decides adoption

Software that lives in a separate tab gets used twice. Software inside the workflow gets used daily.

Use cases

When businesses come to us for this.

Staff spending hours finding answers buried in internal documents

High volumes of invoices, forms or contracts keyed in by hand

Support teams answering the same questions repeatedly

Operations run on spreadsheets that have outgrown themselves

Data moved between CRM, email and finance systems by copy-and-paste

A product that would be materially better with AI inside it

What this is

What we actually do here.

01AI Solutions & Intelligent Automation
02Business Process Automation
03Custom Web Applications & Portals
04API, CRM & System Integrations
05Chatbots & Conversational AI
06Knowledge & Retrieval Systems
07Document Processing & OCR
08Dashboards & Business Applications
09Deployment, Monitoring & Support

Technology

AI

  • OpenAI
  • LangChain
  • Ollama
  • Whisper
  • Vector databases

Application

  • React
  • Next.js
  • FastAPI
  • Node.js
  • Laravel
  • .NET

Data

  • PostgreSQL
  • MySQL
  • MongoDB
  • Redis

Delivery

  • AWS
  • Docker
  • Cloudflare
  • CI/CD

How it runs

A working rhythm, not a process deck.

01

Find the work

Map the actual task, its volume, its cost and its failure modes. If technology is not the right answer, that is a legitimate outcome.

02

Prove it narrowly

A focused prototype on real data with an agreed measure of good, before anything is committed to production.

03

Engineer it

Retrieval, guardrails, review steps, integration and the application around it — the parts that make it software.

04

Operate it

Deploy, monitor quality and running cost, and keep improving as the business and the models move.

What it should achieve

Outcomes worth measuring.

Hours recovered from work that never needed a person

Consistent handling of high-volume, repetitive tasks

Systems that talk to each other without manual copying

Capability your team can operate and extend

Questions

The things people ask.

That is the first piece of work, and it is deliberately short. We look at the task, the volume, the tolerance for error and the cost of the current approach. Sometimes the honest recommendation is better software, clearer process or a rules engine — we would rather say that early.