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(●)(01) Service

We design and build practical AI systems: assistants that answer from your own documents, automations that take repetitive work off your team, and models that help you predict what happens next. Every system is grounded in your data and built to fit the tools you already use.

Discuss a ProjectSee capabilities
(01)Problems we solve

  • Problem 01

    Answers are buried in documents

    Policies, contracts, product information and past tickets live across drives and inboxes, so staff spend time searching instead of helping customers.

  • Problem 02

    Repetitive work eats the week

    Data entry, document handling, routing enquiries and drafting routine replies are rule-based tasks that still take up skilled people's time.

  • Problem 03

    Support queues grow faster than the team

    Customers expect quick, accurate responses at any hour, and the same questions arrive again and again.

  • Problem 04

    AI pilots that never reach production

    Experiments built in isolation stall because they aren't connected to real data, real users or real workflows.

(02)Capabilities

Four focused capabilities. Use one on its own or combine them into a complete system.

01.1

Generative AI

Content and automation that speed up everyday work.

Who it's for

Teams that spend hours each week writing, reading or sorting documents, emails and requests by hand.

Overview

We use large language models to draft, summarise, classify and extract, wired into the workflows where that work actually happens. Outputs follow your tone, templates and rules, with review steps where accuracy matters.

What we deliver

  • Document summarisation and extraction
  • Drafting tools for emails, proposals and reports
  • Classification and routing of incoming requests
  • Prompt design, guardrails and evaluation

What changes for you

  • Routine drafting and summarising takes minutes instead of hours
  • Incoming requests are sorted and routed without someone triaging them
  • Staff spend their time checking and deciding, not retyping
01.2

Applied Machine Learning

Predictions and personalisation that improve results.

Who it's for

Businesses with a few years of sales, customer or operational history and a decision they make repeatedly.

Overview

When a business question can be answered with historical data, we build models that forecast, score or recommend, and we put their output where decisions are made: inside a dashboard, an internal tool or an automated workflow.

What we deliver

  • Demand and volume forecasting
  • Lead, risk and churn scoring
  • Recommendations and personalisation
  • Model monitoring and retraining pipelines

What changes for you

  • Plans and stock levels are based on forecasts, not guesswork
  • Sales and support focus on the leads and customers most likely to matter
  • Customers see offers and content that fit what they actually buy
01.3

NLP & AI Assistants

Chat tools that answer customers and staff quickly.

Who it's for

Teams answering the same customer or staff questions every day across chat, email and the phone.

Overview

We build assistants for websites, help desks and internal teams that understand natural language, follow your policies and hand over to a person when they should. Conversations are logged so you can see what people ask and improve answers over time.

What we deliver

  • Customer-facing website and support assistants
  • Internal helpdesk and operations assistants
  • Human handover and escalation rules
  • Conversation analytics and feedback loops

What changes for you

  • Common questions are answered instantly, day or night
  • People only step in for the conversations that need them
  • You learn what customers ask most and where answers fall short
(03)In practice

A retrieval-augmented assistant finds the relevant passages first, then answers from them and shows where each fact came from.

Try a question

Illustrative demo with example documents. Real assistants answer only from the sources you approve.

What's the returns window for custom orders?

Found 2 relevant passages

  • returns-policy.pdf · §4
  • terms-of-sale.pdf · §11

Grounded answer

(04)How we deliver

  1. Step 01

    Pick one valuable workflow

    We start with a single process where AI clearly saves time or improves quality, and agree how success will be judged.

  2. Step 02

    Prepare the data

    We locate the documents and records the system needs, clean them up and decide what the AI may and may not access.

  3. Step 03

    Build, evaluate, refine

    We build a working version, test it against real examples and tune prompts, retrieval and guardrails until the answers are dependable.

  4. Step 04

    Integrate and monitor

    The AI goes live inside the tools your team already uses, with logging and review so quality stays visible after launch.

(05)Related

Case study · Removals & logistics · UK

Fastiya Logistics

Admin software, a website and an AI lead generation platform for a removals company

SolutionsReading
(06)FAQ

Questions about AI Solutions

Ask us directly
Will the AI make things up?

We reduce that risk by grounding answers in your own sources with retrieval, showing citations, limiting the AI to approved content and adding human review where mistakes would be costly.

Is our data used to train public models?

We design systems so your data stays within the services and accounts you control, and we choose providers and settings that match your data-handling requirements.

Do we need lots of data to start?

Not usually. Assistants and knowledge search work with the documents you already have. Predictive models need historical records, and we will tell you honestly whether yours are enough.

Can AI connect to our existing systems?

Yes. We integrate with CRMs, help desks, databases and internal tools through their APIs so AI works inside your current workflow.