AI & Automation

Practical AI and automation for real workflows

Connect your systems, automate repetitive work, and add AI features where they genuinely help — with clear limits, human review, and agreed success measures.

A good fit if

  • Your team loses hours to repetitive data entry
  • Information is scattered across tools that don't talk to each other
  • You want to add AI features to a product responsibly
  • You need internal tools built around a specific process

What we offer

Three ways we put AI and automation to work

01

AI Integration

We add language-model features to your products and internal systems — drafting, summarising, classifying, and searching your own documents — with guardrails and human review where accuracy matters.

  • Summarising documents, emails, and tickets
  • Search across your own knowledge base
  • Classifying and routing incoming requests
  • Drafting assistance with human approval
02

Workflow Automation

We connect the tools you already use and automate the steps between them, so data moves reliably without manual re-entry.

  • Syncing data between CRM, finance, and operations tools
  • Automated reports and notifications
  • Lead, order, or ticket intake pipelines
  • Scheduled jobs with logging and alerts
03

Internal Business Tools

We build focused internal applications — dashboards, admin panels, and approval flows — that give your team one place to get work done.

  • Operations dashboards
  • Approval and review queues
  • Admin panels over existing data
  • Back-office tools for support teams

Our principles

How we keep AI practical

  • Start with the workflow, not the model

    We find where time is actually lost before deciding whether AI, simple automation, or neither is the right fix.

  • Human review where it matters

    Outputs that affect customers or decisions are reviewed, with clear fallbacks when the system is unsure.

  • Your data, handled carefully

    We design around your privacy and compliance requirements and choose providers accordingly.

  • Measured, not assumed

    We agree how quality will be evaluated and test against real examples before anything goes live.

Scope & deliverables

What we can deliver

We keep AI projects small to start and expand once they prove useful. Typical deliverables include:

  • Automation opportunity assessment
  • AI feature design and prototyping
  • Integrations between existing tools
  • Retrieval over your own documents
  • Evaluation sets and quality checks
  • Internal dashboards and admin tools
  • Monitoring, logging, and cost controls
  • Documentation and team handover

Our approach

How we approach AI and automation

  1. 01

    Find the bottleneck

    We map the workflow and identify where automation could save meaningful time.

  2. 02

    Prototype with real examples

    We test a small version against your actual data before committing to a full build.

  3. 03

    Build with guardrails

    Validation, fallbacks, logging, and human review are part of the production version.

  4. 04

    Monitor & refine

    We track quality and cost after launch and adjust prompts, rules, and integrations.

Technology

Tools and capabilities

We choose tools to suit your project, team, and existing systems. These are typical options, not a fixed stack.

AI models & tools

  • OpenAI API
  • Anthropic Claude
  • LangChain
  • Hugging Face
  • pgvector
  • Evaluation tooling

AI engineering

  • RAG pipelines
  • Voice agents & AI calls
  • Agents & tool calling
  • Chatbots & assistants
  • AI workflows & automation
  • AI integrations (CRM, helpdesk, internal tools)

Automation

  • Webhooks
  • Scheduled jobs
  • Queue-based workers
  • Workflow platforms where suitable

Integration

  • REST & GraphQL APIs
  • CRM & helpdesk APIs

Application

  • Python
  • TypeScript
  • Node.js
  • Django

Start a project

Have a workflow that should be easier?

Describe the repetitive work and the tools involved. We'll give you an honest view of what AI or automation could change.