AI automation services with human control built in

We use AI inside a bounded workflow when it can prepare, classify, extract or draft useful material, and when a person can still inspect, correct and approve the consequential action.

Discuss an AI workflow See the delivery method

AI automation services place a bounded AI task inside a wider business workflow. Durward does not sell an autonomous agent or an AI model: we design the input, task, checks, human approval and recovery route around a specific business use case, then test it before any wider release.

What an AI automation service includes

What is included

AI-assisted extraction, categorisation, source-linked research support or drafting from material the client has approved for the task.
Deterministic checks and review queues that keep the workflow legible when the AI output is incomplete, unclear or unsuitable.
A controlled test with acceptance criteria, a named owner and a route back to manual handling.
Design and implementation of the surrounding workflow, rather than an isolated prompt or generic chatbot demonstration.

What is not included

An autonomous decision-maker for financial, professional, safety, clinical, legal or customer-consequence decisions.
A claim to be an AI model provider, an agent marketplace or a substitute for the accountable member of the client's team.
A broad import of business data simply because it may be useful later; access starts with the minimum material required for the defined test.
An unsupported accuracy, compliance, time-saving or ROI promise.

The three boundaries that make AI useful in an operating process

The technology is only one part of the service. The surrounding decisions determine whether it can be used responsibly.

A bounded task

“Extract these fields from approved documents” or “draft a reply from this approved knowledge” is testable. “Handle the customer” or “decide the case” is not a sufficiently bounded task.

A check that fits the consequence

The workflow can compare required fields, preserve source links, apply a known rule or route low-confidence material for review. The check is designed before the output is relied on.

A named human hand-off

A person owns approval, ambiguous outputs, changed instructions and any action with a customer or professional consequence. Human involvement is part of the operating design.

How we move from AI idea to controlled test

We test the workflow around the AI component, not just whether a model can produce a plausible-looking answer.

Define the job and source

We specify the business trigger, authorised source material, AI task, required output and the person who owns the result.

Buyer receives: A constrained use-case brief and a clear exclusion boundary.
Assess readiness

We check source quality, permissions, system access, exception volume, change sensitivity and whether non-AI automation would be simpler.

Buyer receives: A proceed, amend or stop decision before a prototype is relied on.
Prototype the route

The AI task is combined with input shaping, deterministic checks, source retention and a human review queue.

Buyer receives: A testable workflow path with defined inputs, checks and review.
Test acceptance and failure

We use agreed examples, edge cases and failure conditions to establish what the output must contain, when it is rejected and who sees it.

Buyer receives: Acceptance criteria and an exception process that the owner can inspect.
Release with an owner

A workflow is only released after the owner can work with the records, exceptions and manual recovery route; it is reviewed as source material or requirements change.

Buyer receives: An owned operating routine and a defined review point.

Three bounded AI workflow patterns

These patterns reflect the areas already represented in the Durward portfolio. They show the controls a working design needs; they do not represent deployed client outcomes.

Public-source account research for a B2B team

A B2B supplier wants a more consistent starting point for researching an account from authorised public market sources.

Trigger: A scheduled research run or an approved source event added to a commercial review queue.
Systems or sources: Approved public sources, a research workspace and the client's sales process.
AI task and checks: The AI prepares a source-linked account summary or draft research note; required source fields and claims are checked before it reaches a person.
Human checkpoint: A commercial owner reviews relevance, commercial interpretation, contact route and any message before it is approved.
Exception route: Missing, conflicting or weak source material stays visible as a research gap and never becomes an asserted buying signal.
Success measure: A reviewer can trace the source, correct the summary and decide whether there is an appropriate next action.

First-party follow-up draft and reply classification

A team holds client-authorised historic quote or service records and needs help preparing a follow-up queue without handing decisions to a model.

Trigger: A record reaches an agreed review condition after duplicate, ownership and suppression checks.
Systems or sources: Client-authorised CRM, quote, service or account records and the approved communication process.
AI task and checks: The AI drafts within approved message boundaries and classifies an incoming reply into a defined review or hand-off category.
Human checkpoint: The account or service owner approves live communication rules and handles any reply that needs judgement, negotiation or clarification.
Exception route: A missing record, unclear reply, opt-out or sensitive issue routes to a named person rather than receiving a model-generated continuation.
Success measure: Draft quality against the approved brief, correct routing and visible exception handling.

Source-linked preparation of an internal review pack

An operational team needs to prepare recurring documents and messages for a responsible person to assess.

Trigger: A new case, scheduled review date or defined document batch.
Systems or sources: Client-authorised documents, messages and internal records, with source location retained.
AI task and checks: The AI extracts requested fields and drafts a structured summary; required fields, source links and conflicts are checked before review.
Human checkpoint: The operations lead or responsible professional validates the material and decides the next action.
Exception route: Low-confidence, conflicting or incomplete material is marked for review; it is not silently filled or decided.
Success measure: A review pack that is traceable, correctable and acceptable to its accountable user.

The operating controls are part of the product

A useful AI workflow has to be inspectable when it works, when it changes and when it fails.

The workflow prepares

A defined task, approved source boundary and output format rather than an open-ended AI request.
Source links, required-field checks, review status and an exception queue appropriate to the process.
A record of the test condition and acceptance criteria so the owner can decide whether to continue or correct the route.

The accountable team retains

Approval of the use case, information access, prompts or instructions, release conditions and change decisions.
Judgement on ambiguous material, customer-facing actions and every consequential decision outside the bounded task.
The manual route needed to continue the work when the automated path is not suitable.

When an AI-assisted route is worth assessing

AI is one option in the implementation route. It should earn its place by handling an information task that deterministic automation alone cannot reasonably handle.

A good fit

The task involves repeated unstructured material, but the desired output and review standard can be described clearly.
The client can identify authorised source material, a named owner and a practical exception route.
A smaller controlled test can establish whether the output is accurate and useful enough for the actual operating process.

Non-AI automation or manual work is better when

A known rule, approved template or native platform feature can complete the task more directly and predictably.
The process requires an unreviewed high-consequence decision or an owner cannot explain how exceptions should be handled.
The source material is too inconsistent, unavailable or sensitive for a bounded test to be defined.

Go to the level of detail you need

These pages distinguish the business process, the implementation service and the AI-specific decision rather than treating them as one offer.

Supporting information: B2B Lead Generation: an approved public-source research and nurture route · Research & Evidence Workflows: source-linked internal preparation for B2B teams · Security and data: how data and access boundaries are approached · Discuss one AI-assisted workflow

Bring one task where AI might genuinely help

Describe the trigger, source material, output and person who would approve it. We will assess the non-AI route as well as the AI-assisted one.

Discuss an AI workflow See how we work