AI Solutions

AI-powered features including document processing, smart search, chatbots, and automated workflows using OpenAI and custom models.

AI is worth adding where it removes a specific, repetitive judgement — reading documents, routing requests, answering from your own content. It is not worth adding because it is available. We start from the task you want removed and work back, and we are explicit about where the technology is unreliable: anything that must be exactly right still needs a person in the loop, and we design for that rather than hoping. You own the source code that comes out of it, so nothing here creates a dependency on us that you did not choose.

Who this is for: Teams with a high volume of document handling, classification or first-line questions that follow patterns — and who need the result auditable.

What We Deliver

Document Processing (OCR + AI)
Smart Search & Recommendations
AI Chatbot Integration
Content Generation
Automated Data Extraction
Predictive Analytics

How It Works

01

Tell Us the Challenge

What manual task wastes your team's time? We identify AI opportunities.

02

AI Proof of Concept

Working AI demo on your actual data. See the accuracy before you invest.

03

Evaluate & Decide

Test the AI results yourself. Adjust parameters until you're satisfied.

04

Integrate & Train

Connect AI into your existing system. Fine-tune for your specific use case.

05

Monitor & Improve

Performance tracking, model updates, and warranty of up to 3 years.

What you get

A defined task, with success and failure both measurable
Integration into the system your team already uses
Human review where the cost of being wrong is high
Your data staying on infrastructure you control
Evaluation of output quality before rollout, not after
Documentation of what the model does and does not decide

How the work runs

The first job is deciding what AI should and should not be doing. Our published framework is explicit that AI output is not bug-free and that the person leading the work owns turning it into what the project needs — so the engagement is built around review, not around generation.

  1. Scoping which parts of the workflow AI genuinely improves, and which it should not touch
  2. Writing the standards and context the model works inside, so output heads the right way
  3. Breaking the work into pieces small and clear enough to be checked one at a time
  4. Every output reviewed and iterated until it meets the requirement
  5. Handover including the standards, so your team can keep running it

We have written this up in full — the process when you come to us directly, the sprint cycle when you are an agency partner, and the framework underneath both.

Questions we get asked

Where does our data go?

Wherever you decide. This is built on infrastructure you control, and the choice between a hosted model and a self-hosted one is made explicitly, with the trade-offs on the table.

How do we know the output is reliable?

You measure it before rollout on your own material, not on a vendor demo. Where accuracy cannot be guaranteed, the design keeps a person in the decision.

Is this worth it for us?

Sometimes the honest answer is no, and a simpler rule or a better form removes the same work more reliably. Discovery is where that gets decided.

Can it work with our existing platform?

Yes — this is normally added to a system you already run rather than delivered as a separate product.

Delivered work using this service

Technologies Used

OpenAI GPT Python Drupal 11 Tesseract OCR Vector DB

Let's Get Started

Ready to transform your business with ai solutions?

Start Your Project