AI Automation Consulting
Turn AI Experiments Into Working Business Automation
Move beyond ChatGPT pilots and disconnected tools. We identify where AI belongs inside your workflows, build the system, and measure the outcome.
Most organisations we meet have already bought AI and run experiments. The gap is operational, not technological.
- Fixed scope
- Measurable ROI
- Human-in-the-loop
- Works with your existing stack
Trusted on complex technology delivery
Does any of this look familiar?
These are the symptoms we see before the numbers get measured.
- Licences are bought and pilots have run, but nothing is embedded in a real process
- AI output still needs a person to copy it somewhere for it to matter
- Nobody can quantify what the experiments have returned
- Different teams are trialling different tools with no shared architecture
- Enthusiasm is high and production deployments are zero
What this process costs you today
Before anyone talks about technology, it is worth knowing the number you are trying to beat.
A worked example
Modelled estimate3 employees × 7 hours/week of repetitive processing = 1,092 hours/year.
At $55/hour loaded cost
$60,060
annual process cost
If automation removes
60%
of the manual effort
Recoverable capacity
$36,036
per year
A knowledge-work process where judgement is real but most of the handling is routine.
Now run it on your own volumes
Your current process
Assumptions
- Fully loaded hourly cost of the staff handling these cases today
- Rework estimated at 1.5x the original handling time per failed case
- Automatable share of 50–70%, reflecting that judgement steps stay with people
- Payback modelled against a typical single-process implementation investment
Your estimated opportunity
Modelled estimate- Current estimated annual cost
- $179,850≈ 2.0 FTE of manual effort
- Potential automatable effort
- 50–70%
- Indicative payback
- 2 months
- Potential annual opportunity
- $89,925 – $125,895
Directional planning estimate based on your inputs — not a quote or a substitute for finance sign-off.
How we work
- 1
Map
Sit with the people doing the work and document the process as it actually runs — including the workarounds nobody wrote down.
- 2
Measure
Establish a baseline: volume, handling time, error and rework rate, and what the process costs you today.
- 3
Build
Redesign the workflow and build it against your real systems and data — not a mockup or a slide.
- 4
Validate
Test against edge cases, confirm exception handling and approval gates, and measure the result against the baseline.
- 5
Deploy
Move into production with monitoring, runbooks and training — or recommend against it if the numbers do not hold up.
Before and after
AI where judgement is needed. Automation where rules are enough.
Today
- Rules: route an invoice based on supplier — but done by hand
- AI: understand what a document actually says — but done by hand
- Rules: escalate anything above a threshold — but remembered, not enforced
- Human: approve genuine exceptions — buried among routine ones
Automated
- Rules engine routes by supplier, deterministically
- AI reads and classifies the document content
- Thresholds enforced automatically, every time
- People see only the exceptions that need judgement
What we automate
- Document understanding and classification
- Case summarisation and triage
- Data extraction from unstructured sources
- Drafting and review assistance
- Research and enrichment
- Exception explanation for reviewers
What you receive
- A map of where AI genuinely adds value versus where rules are cheaper and safer
- Baseline metrics for the target workflow
- Working AI-assisted automation on your systems
- Confidence thresholds and human review points
- Evaluation against real cases, not demo inputs
- Measured result and a production recommendation
What the numbers say
Every figure is labelled by where it comes from — measured customer results, industry benchmarks, modelled estimates, or commitments we make to you.
50–70%
Of case handling typically automatable
70%
Faster document review
Human
Sign-off retained on every judgement call
Measured
Against a baseline recorded before we start
Technology we build on
We are not tied to one platform. The right tool depends on the workflow, your existing stack, and what you can maintain.
How we keep it safe
Human approval gates
You decide which decisions an automation may make alone and which require a person. Thresholds are configured to your policy.
Exception handling
Anything ambiguous, incomplete or out of policy routes to a named owner with the reason attached — it does not fail silently.
Audit trail
Every action is logged: what ran, on what input, what it decided, and who approved it. Built for audit and incident review.
Least-privilege access
Automations connect through credentialed, scoped integrations you grant and can revoke. Your systems stay the system of record.
How engagements run
Each stage is a decision point. You only continue if the previous stage justified it.
Process Assessment
Map one process, quantify what it costs today, and get a fixed implementation recommendation.
Fixed fee, credited against a SprintStage 2Automation Sprint
Redesign and build the workflow, then prove whether the economics justify scaling it.
Fixed scope, fixed feeStage 3Production Implementation
Harden a validated workflow for real users, real data, security, monitoring and scale.
Scoped from the Sprint findingsStage 4Managed Automation
Keep automations reliable as systems, rules and volumes change.
Monthly retainerAI Automation Consulting FAQs
Find your highest-ROI AI workflow
We assess your candidate workflows and tell you which one justifies building — and which are better served by rules.