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Applied AI

AI that removes
a specific piece of work

Not a strategy deck about transformation. A tool that reads the invoices, answers the repeat question, drafts the first version or flags the account before it churns — running on your data, inside the systems your team already opens every morning.

Document processingRetrieval assistantsClassification ForecastingInternal copilots
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OPS
We get about 400 supplier invoices a month. Two people key them into the ERP by hand.
AI
Extraction plus validation against your PO data. Roughly 90% posted without a human touch, the rest queued for review with the reason attached.
OPS
How long to know if it actually works on our documents?
AI
Three weeks. We test on your last 500 invoices and publish the accuracy before anyone commits to a build.

Most AI projects fail on scope, not on technology

01Pick a bounded taskOne workflow, one measurable output. A tool that does a single job well beats a platform that does nine things approximately.
02Prove it on real dataYour documents, your tickets, your history. Accuracy on a vendor demo tells you nothing about accuracy on your mess.
03Put it where work happensInside the CRM, the helpdesk, the ERP. Anything that needs a separate login gets used twice and abandoned.
Document processing screen

01 — Read and extract

Documents into structured data

Invoices, contracts, claims, KYC packets, delivery notes. We extract the fields, validate them against systems you already trust, and route only the exceptions to a person — with the reason for the flag attached so review takes seconds.

  • Field extraction with confidence scores
  • Validation against ERP or PO data
  • Human-in-the-loop queue for exceptions
Knowledge assistant screen

02 — Find and answer

Assistants over your own knowledge

Policies, product documentation, past tickets, contracts. Retrieval grounded in your material, with citations back to the source paragraph, so an answer can be checked rather than trusted blindly. Access follows your existing permissions.

  • Answers cited to the source document
  • Permission-aware retrieval
  • Logged questions that reveal content gaps
Forecasting dashboard

03 — Predict and prioritise

Scoring, forecasting and routing

Which leads deserve a call today, which accounts are drifting, what demand looks like next quarter. Often the least fashionable models are the right ones, and we will use them when they beat something larger on your numbers.

  • Lead and churn scoring
  • Demand and inventory forecasting
  • Ticket triage and routing

Our honest filter

Worth building

  • High volume, repetitive, rule-adjacent work
  • A task where 90% accuracy plus review still saves hours
  • Data you already hold and are allowed to use
  • An owner inside the business who wants it to work
  • A number that improves if it succeeds

Not worth building

  • Anything a rule or a report already handles
  • Judgement calls where a wrong answer is expensive
  • Processes nobody has written down yet
  • Projects whose goal is to be seen doing AI
  • Data that is scattered, stale or off-limits

Where it has paid off

Drag to scroll →

OperationsInvoice postingExtraction and validation ahead of ERP entry, with exceptions queued for a single reviewer.
SupportFirst-response draftingReplies drafted from past resolved tickets, edited by an agent before sending.
SalesLead scoringEnquiries ranked on likelihood to close so the team calls in a sensible order.
LegalContract reviewClause extraction and deviation flagging against a standard template.
MarketingContent operationsFirst drafts and variant generation inside an approval workflow, never auto-published.
MediaArchive searchTranscription and semantic search across footage and audio libraries.

Common questions

Rarely, and only when there is a clear reason. Most business problems are solved better and far more cheaply by retrieval, a fine-tuned small model, or classical machine learning on data you already hold.

Wherever your policy allows. We can run entirely inside your cloud tenancy, use enterprise API tiers that do not train on your data, or deploy open models on your own infrastructure.

We agree an accuracy threshold and a time-saved figure before the build, measure both against a held-out set, then keep measuring against real usage after launch.

We will say so. A large share of the requests we assess are handled better by a rule, a report or a fixed integration, and that recommendation costs you nothing beyond the assessment.

Bring us the task, not the technology.

Describe the work that eats your team's week. We will tell you whether AI is the right tool, and what a first version would take.

Assess a use case →