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AI, Data & Emerging Technology · AI Development AI that does real work, not demos

We build AI that earns its place in your business — assistants that answer customers, automations that remove manual work, and intelligent features built right into your product.

  • AI assistants & chatbots
  • Workflow automation
  • Document & data intelligence
Start your projectHow we work

Overview

How a useful AI project actually comes together

Most AI work is not about the model. It is about picking a task where software can read, sort, draft or decide well enough to save real time, then wiring that capability into the systems your people already use. The model is one component; the data it sees, the instructions it follows and the review steps around it decide whether the output can be trusted.

The first decision is scope. A narrow assistant that handles one document type or one support queue is easier to test, cheaper to run and faster to improve than a general tool that tries to answer everything. We usually recommend starting narrow, proving value on real inputs, and widening only once the evaluation numbers and user feedback support it. It also builds trust inside the team.

Good AI work looks boring from the outside: clear limits, logged decisions, predictable running costs and a team that knows when to trust the output and when to check it. That is the standard we build toward on every engagement. We also document how the system behaves when it fails, so the people using it know exactly what to do next.

Who it’s for

Built for teams like yours

  • 01

    Operations-heavy teams

    Businesses where staff spend hours reading emails, forms or documents and re-keying the same information into other systems, and want that routine work handled with a human reviewing exceptions.

  • 02

    Software product companies

    Product teams that want to add summarisation, search, drafting or classification features to an existing app without rebuilding their stack or locking into a single model provider.

  • 03

    Leaders testing the waters

    Owners and managers who suspect AI could help but want an honest assessment of where it fits, what it would take and where it would not be worth the effort.

Why it matters

From buzzword to bottom line

Most "AI projects" fail because they start with the technology instead of the problem. We start with the task you want removed, sped up or scaled — then build the smallest AI that solves it properly.

We've shipped production AI in the real world, including ClinVox AI — a HIPAA-ready platform that turns patient consultations into complete clinical notes in under 60 seconds.

Every AI build includes

  • Use-case discoverywe find where AI actually pays off, and where it doesn't.
  • Your data, connectedgrounded in your content so answers are accurate, not invented.
  • Human-in-the-loopreview and override built in, so nothing goes out unchecked.
  • Evaluation & testingmeasured for accuracy before it ever touches a customer.
  • Cost controlengineered so your usage bill stays predictable as you scale.
  • You own itthe code, the data and the system are yours. No lock-in.

Features

AI that pays for itself

  1. 01

    AI assistants & chatbots

    Trained on your business to answer customers, qualify leads and book work — 24/7.

  2. 02

    Workflow automation

    Kill the manual, repetitive work — intake, data entry, summaries, routing and follow-up.

  3. 03

    Document & data intelligence

    Extract, summarise and search across your documents, records and knowledge base.

  4. 04

    AI features in your product

    Smart search, recommendations, generation and insights built natively into your app.

  5. 05

    Private & compliant

    Your data stays yours. Built with privacy, access control and compliance in mind.

  6. 06

    Measurable results

    We define the metric first — hours saved, leads handled, cost cut — then build to it.

In practice

Where AI earns its keep

  • Inbox and ticket triage

    Incoming emails and support tickets are read, tagged by topic and urgency, routed to the right person and given a suggested reply, so staff spend their time on the cases that actually need judgment.

  • Extracting data from paperwork

    Invoices, applications, contracts and intake forms are parsed into structured fields, checked against your rules and pushed into your accounting or CRM system, with uncertain values flagged for a person to confirm.

  • Internal knowledge search

    Employees ask questions in plain language and get answers drawn from your policies, manuals and past tickets, each with a link back to the source document so the answer can be verified.

  • Drafting first versions

    Proposals, reports, product descriptions or follow-up emails are drafted from your templates and data, giving staff a solid starting point to edit rather than a blank page. Every draft is labelled as a draft.

Process

How we work

  1. 1

    Opportunity scan

    We interview the people doing the work, review sample inputs and list candidate tasks, then rank them by time saved, error risk, data availability and how easy each one is to measure.

  2. 2

    Real-data pilot

    We build a small working version against a sample of your actual documents or tickets and score its output, so the decision to proceed rests on evidence rather than a slide deck.

  3. 3

    Production build

    The pilot becomes a hardened service with authentication, integrations into your existing systems, review screens for staff and cost limits on every model call it makes. Every call is logged for later review.

  4. 4

    Evaluation gate

    Before launch, the system runs against a held-out test set and edge cases you help us collect. It ships only when accuracy and failure behaviour meet the bar we agreed.

  5. 5

    Launch and tune

    We roll out to a small group first, watch logs and user corrections, adjust prompts or retrieval where answers fall short, then widen access once the numbers stay steady. Users can flag poor answers directly.

Deliverables

What you receive

  • Ranked list of AI opportunities
  • Pilot results scored on your data
  • Production AI service with integrations
  • Staff review and override screens
  • Evaluation test set and scoring scripts
  • Usage and cost monitoring dashboard
  • Architecture notes and operating runbook

Tools & methods

Models & APIs

  • Claude
  • GPT models
  • Gemini
  • Llama
  • Mistral

Engineering

  • Python
  • TypeScript
  • Node.js
  • FastAPI
  • PostgreSQL
  • pgvector

Cloud & ops

  • AWS
  • Azure
  • Google Cloud
  • Docker
  • Langfuse

FAQ

Frequently asked questions

Anything else about AI Development? Ask us directly.

  1. A focused AI assistant or a single automated workflow sits near the lower end; a custom model, a multi-step agent or AI features built deep into an existing product costs more. We scope against a specific business outcome, not a vague 'add AI' brief, so the quote is fixed and honest.

Insights

Further reading

Let’s work together

Have a project in mind?

Book a strategy call and we’ll show you exactly how to turn your goals into a system that generates consistent results.

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