AI Workflow Engineering

Practical AI in the places it actually pays back

AI Workflow Engineering

Document extraction, classification, drafting, and support triage built into your existing workflows - with evaluation and human review, not a demo that fails in production.

Signs This Applies To You

If more than one of these sounds familiar, this is the right service to talk about.

A team reads the same kind of document hundreds of times a week

An AI pilot demonstrated well and never reached production

Support agents rewrite the same answer in slightly different words all day

Nobody can tell you how accurate your current AI feature actually is

Model spend is climbing and no one can attribute it to a workflow

The Problem

AI pilots stall because they are evaluated on impressive demos instead of on your messiest real inputs. The result is a tool nobody in operations trusts enough to rely on.

Our Approach

We pick narrow, high-volume tasks with a clear right answer, benchmark against your historical data, and ship with a human review queue that shrinks as measured accuracy earns it.

What The Work Covers

Document extraction

Invoices, contracts, forms, and scanned PDFs turned into structured fields with per-field confidence and a review queue for anything below threshold.

Classification and routing

Inbound email, tickets, and applications categorised and routed, with the ambiguous minority escalated to a person instead of guessed at.

Drafting with review

Replies, summaries, and reports drafted for a human to approve, which keeps a person accountable while removing most of the typing.

Retrieval over your own content

Answers grounded in your documentation and records, returned with citations, so a wrong answer can be traced back to the source that caused it.

Evaluation and cost control

A labelled test set, accuracy tracked per release, token budgets per workflow, and alerting when either number moves the wrong way.

How It Runs

01.Use-case assessment

We rank candidate tasks by volume, tolerance for error, and how cleanly success can be measured. Some get rejected here - that is the point.

02.Evaluation harness

Before any build, we assemble a labelled set from your own history so accuracy claims are grounded in your data.

03.Production build

The workflow ships with review queues, confidence thresholds, cost controls, and full audit logging of every model call.

04.Continuous evaluation

Accuracy is tracked over time, so model or prompt changes are a measured decision rather than a hopeful one.

Outcomes We Aim For

Repetitive reading and drafting work handled automatically

Measured accuracy before anything reaches a customer

Human review kept exactly where judgement matters

When This Is Not The Right Fit

We would rather say so early than take the work and disappoint you later.

Tasks with no agreed right answer to measure against

Regulated decisions that require a named human to make them

Training a model from scratch when a hosted model plus retrieval will do

Chat interfaces added because a competitor added one

What You Get

Use-case assessment with expected accuracy and cost

Prototype evaluated against your real historical data

Production build with review queues and audit logging

Ongoing evaluation harness and monitoring


Engagement: Assessment first, then scoped build

Timeline: Assessment in 1 week, pilot in 4-6

Discuss This Service

Typical Stack

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1 wk

To a written assessment with expected accuracy and cost

4-6 wks

From assessment to a measured pilot in production

Every call

Logged with inputs, outputs, cost, and reviewer decision

In Practice

Where AI Workflows Earns Its Keep

01.Invoice and PO matching

Line-level extraction matched against purchase orders, so only genuine exceptions reach the finance team.

02.Claims triage

Incoming claims classified by complexity and completeness, so straightforward cases move quickly and the rest reach a specialist with a summary attached.

03.Support deflection

Draft replies grounded in the knowledge base and reviewed by an agent, measured on first-response time rather than on how clever the demo looked.

04.Tender and RFP response

Requirements extracted into a matrix and matched against previous answers, turning a week of reading into a day of editing.

Related Work

Where We Have Done This

Financial services

Document review cut from three days to under an hour

A lending team read every application pack by hand. We built an extraction and classification workflow with a human review queue, benchmarked on two years of their own files.


Read Case Study

Questions

AI Workflows Questions We Get Asked

We build a labelled evaluation set from your own historical records before writing the workflow, then report accuracy against it. If the number is not good enough for the task, we say so and stop there.

To whichever provider you approve, under a zero-retention or enterprise agreement where one is available, or to a model running inside your own environment if the data cannot leave. That decision is made in the assessment.

Grounding answers in retrieved source documents, returning citations, enforcing structured output, and pushing low-confidence results into a review queue rather than passing them downstream.

Per-workflow token budgets, caching of repeated context, cheaper models for the easy majority of cases, and a dashboard that attributes spend to the workflow that caused it.

No, but you need one at the start. Review coverage is reduced deliberately as measured accuracy earns it, class by class, rather than switched off on launch day.

Get In Touch

Have a AI Workflows Problem?

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Tell us what is not working. The more concrete you can be about the process or the system, the more useful our first reply will be. We reply to every enquiry within one business day.

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