One ladder: find the opportunity, build it, then make it last
Most clients start with the assessment because it is the cheapest way to find out where the money is. Some already know and go straight to a build. Others would rather buy an operated capability than staff a platform team. All of it is the same practice applied at different depths — and the capabilities that surround the build are the ones that decide whether the savings survive contact with the organisation.
Assess
1 capabilityAutomation consulting
Know which automations will pay before you build any of them.
Most automation budgets are spent on the wrong processes. We measure your operation, rank every candidate by value and feasibility, build the financial model for each, and hand you a sequenced roadmap you can execute with us or without us.
- 2–4 weeks
- from kickoff to a costed, ranked roadmap
- 5–20×
- typical spread in return between the best and worst candidate
- 100%
- of shortlisted initiatives with a defensible payback figure
- Zero
- build spend committed before the case is proven
Fits when
- An automation budget is approved but nobody agrees what to spend it on
- A previous automation pilot produced no measurable return
- Multiple business units are independently pursuing overlapping initiatives
Build
5 capabilitiesWorkflow automation (AI/RPA)
Remove the manual steps, not just the keystrokes.
We take the processes your teams run by hand every day — invoice processing, order entry, document handling, approvals, reconciliation, reporting — and rebuild them as monitored automation with humans only where judgement is genuinely required.
- 70–92%
- of routine transactions automated end to end
- 3–6×
- faster average cycle time
- 60–80%
- reduction in processing cost per transaction
- 85%+
- fewer data-entry errors reaching downstream systems
Fits when
- More than four people spend most of their week on the same repeatable process
- Volume is growing but headcount cannot keep growing with it
- Cycle time is measured in days when the actual work takes minutes
Custom internal software & agent tools
Software shaped around your process, not the other way round.
When the process is specific to how your business actually works, off-the-shelf tools force your teams into someone else's workflow. We build the internal applications, portals and AI agents that fit the process as it is — and integrate them into the systems of record you already have.
- 1–2 quarters
- from kickoff to first production release
- 40–70%
- reduction in time-on-task for the core workflow
- 100%
- of the process covered, not the vendor's 70%
- Zero
- vendor change-request backlog between you and a fix
Fits when
- A process critical to revenue runs on a spreadsheet someone personally maintains
- Your teams have built shadow systems next to the official one
- You are paying for a vendor product that covers part of the process only
Data foundation & integration
Automation inherits every flaw in the data beneath it.
Before a process can be automated or handed to a model, its data has to be findable, consistent and trustworthy. We build the integration and data layer that connects the systems you already own, resolves the conflicts between them, and gives everything downstream something reliable to operate on.
- 1–3 weeks
- to a defensible picture of the data estate
- 70%+
- of manual reconciliation and report assembly removed
- 100%
- of published metrics carrying end-to-end lineage
- One
- agreed definition per metric, replacing departmental variants
Fits when
- Two departments produce different numbers for the same metric
- Reporting is assembled by hand from multiple exports
- An automation or AI project stalled because the inputs were unreliable
Customer operations automation
Answer faster, resolve more, and stop paying for the same conversation twice.
Contact centres and service desks are almost never short of people. They are short of a process that gets a customer to the right answer without a human copying data between four systems. We automate the queue: what arrives, what can be resolved without a person, what a person needs to see, and what happens after the call.
- 20–40%
- of tier-one contacts resolved without an agent
- 30–50%
- reduction in after-contact administration
- 100%
- of interactions quality-assured, replacing a 2% sample
- Same team
- absorbing volume growth that previously meant hiring
Fits when
- Contact volume is growing faster than customer numbers
- Agents rekey the same data across three or more systems per interaction
- After-contact administration is a recognised problem
Testing & release automation
Ship the change without finding out what it broke in production.
Regression testing is usually the reason change feels dangerous and slow. We make the test suite real, owned and fast — versioned, automated, running in the pipeline against production-shaped data — so releases stop being an event and upgrades stop being a quarter-long project.
- 60–80%
- of regression effort automated and run per change
- Weeks → days
- for a typical upgrade rehearsal window
- Hours
- from merge to a verified, deployable build
- Fewer
- defects escaping to production, with a test on each fix
Fits when
- An upgrade cycle consumes a quarter of manual regression effort
- The automated suite exists but is not trusted or is routinely bypassed
- Staging environments no longer resemble production
Scale & operate
2 capabilitiesPlatform builds
When the automation becomes a product, not a project.
Some automations stop being internal tools and start being products — a service you sell, a platform your partners operate on, or a capability that has to run across dozens of business units with tenant isolation, metering and SLAs. We engineer for that from the start.
- 99.9%+
- availability target with a defined error budget
- N tenants
- on one codebase instead of N forks
- < 15 min
- mean time to detect against the SLO
- Per-unit
- cost attribution across business units
Fits when
- More than one business unit or an external partner needs the capability
- The automation is on a path to being sold, bundled or resold
- You need per-unit cost attribution or usage-based billing
Managed automation operations
We run what we build, against a number you can hold us to.
Automation is software, and software needs an owner. Rather than handing over a runbook and wishing you luck, we operate the estate: monitoring, incident response, exception handling, continuous change and quarterly reporting of the return against the case that was approved.
- 99.5%+
- automation availability across the managed estate
- < 30 min
- median time to detect a broken run
- Quarterly
- benefit reporting against the original business case
- Fixed
- monthly cost, so the savings case cannot erode as scope grows
Fits when
- Automations are in production with no named operational owner
- There is no on-call cover for systems the business now depends on
- Exception queues are growing and nobody is accountable for them
Assure & enable
3 capabilitiesAI governance & assurance
Deploy AI on evidence, not on enthusiasm.
Regulators, auditors and boards now ask the same questions about every AI system: what does it decide, on whose authority, with what oversight, and can you prove it. We build the inventory, testing and documentation that lets you answer, so good ideas are not blocked by unanswerable risk.
- Days
- to produce audit evidence, instead of a quarter of archaeology
- 100%
- of AI systems inventoried, owned and risk-classified
- Proportional
- controls: light-touch for low risk, rigorous for high
- Continuous
- fairness and drift monitoring rather than a one-off test
Fits when
- You cannot list every AI system your organisation uses
- A model influences decisions about customers, employees or credit
- Legal and engineering disagree about what the regulation requires in practice
Automation change & enablement
Adoption is the difference between the savings and the model.
Automation initiatives fail on the human side far more often than the technical one. We handle the operating model, the role changes, the training and the communication that decide whether the system is actually used — and whether the people whose work changes support it or quietly work around it.
- 90 days
- of measured adoption tracking after go-live
- Zero
- surprises: every affected role receives a written change statement
- Measured
- redeployment, so freed capacity appears in the benefit report
- Earlier
- union or works council engagement, before positions harden
Fits when
- A previous automation went live and was quietly worked around
- Affected teams have not been told what happens to their roles
- Nobody has decided what the freed capacity will be used for
AI literacy & capability
Capability that stays after the consultants leave.
The organisations that get real value from AI are not the ones with the best tools. They are the ones whose people know what to use it for, what never to put into it, and how to get a good idea from the front line into production. We build that capability deliberately and measure whether it improved.
- Measured
- capability baseline and uplift, rather than a satisfaction survey
- Every role
- receives concrete rules on what must never be entered into a tool
- Quarterly
- cadence on the front-line idea intake, with a stated response
- Falling
- reliance on external support as internal capability matures
Fits when
- You have bought AI tooling and cannot demonstrate what it returned
- You suspect staff are using public AI tools with data they should not
- Training has been delivered and behaviour has not changed
Not sure which of these you need?
How it works
Measure, model, build, operate — in that order
Audit
1–3 weeks
We measure the process as it actually runs, not as it is described in the documentation.
You get
A measured baseline you can hold us to.
What happens
- Structured interviews with process owners and the people doing the work
- Volumes, touch counts, cycle times and exception rates captured at transaction level
- Fully loaded cost per transaction reconciled against finance data
- Systems, data quality and integration constraints mapped
- Confidence level stated for every figure we report
Model the return
1–2 weeks
Before anyone writes code, you see the number — investment, savings, payback and what happens if adoption is lower than planned.
You get
A defensible business case per initiative, and a sequenced roadmap.
What happens
- Target coverage agreed per process, with the reasoning stated
- Implementation effort estimated as a range with assumptions exposed
- Gross and net annual savings, plus ongoing run cost
- Payback period and three-year return, with sensitivity analysis
- Ranked shortlist so you can stop at any point and still have value
Build & integrate
6–14 weeks per process
We build against your systems, not a demo environment, and put the first process into production while the rest are still in progress.
You get
Working automation in production, with tests and documentation.
What happens
- Integration against ERP, CRM, ITSM and document stores through supported interfaces
- Deterministic rules wherever the logic is stable; models only for genuinely unstructured input
- Confidence thresholds with a human review queue for exceptions
- Access control inherited from your identity provider
- First process live before the engagement closes out
Operate & expand
Ongoing
Automation that is not monitored is automation that will fail quietly. We run what we build and expand it as coverage proves itself.
You get
A running capability with a measured, reported return.
What happens
- Throughput, accuracy and cost-per-transaction dashboards
- Alerting on failure, drift and volume anomalies; failed runs replayable after fix
- A named support path with agreed response times
- Quarterly review of coverage and the next highest-value candidate
- Handover to your team whenever you want to take it in-house
Bring us the process you already know is costing too much
Thirty minutes with an engineer is usually enough to tell whether it is worth automating, roughly what it would save, and whether the payback is inside a window your finance team will accept. If the answer is no, we will say so on the call.