Go
m9m
Use it to automate workflows from one Go binary.
Solution · Workflow automation
Make a demanding workflow shorter and dependable
We redesign one workflow end to end, connect it to the systems it already depends on, automate what should be automated — with AI only where it earns its place — and measure the difference.
Usually owned by: COO, operations leader or the manager whose team runs the process every week.
The situation
Recognise where you are
Many of the most valuable automation opportunities are not glamorous. They are the recurring processes where people gather inputs from several systems, check them against rules that live in someone’s head, chase missing information, re-key data and hand work from one stage to the next. The work is skilled, but much of the elapsed time is waiting and rework.
These workflows rarely need a large AI programme. They need someone to map where the time actually goes, connect the systems that should talk to each other, encode the checks people already perform, and reserve human attention for the exceptions that genuinely need judgement. AI is useful for some steps — reading unstructured inputs, classifying exceptions, drafting — and unnecessary for many others.
We have done this for payroll preparation and for animation production handoffs. In both cases the gain came from collection, validation, tracking and integration rather than from a model doing the skilled work.
How we approach it
From the system you have to the result you need
- 1
Map where time goes
Trace one real cycle end to end, separating active work from waiting, chasing and rework, and agree which measure matters most — usually elapsed turnaround.
- 2
Connect and validate
Collect inputs automatically from the systems that hold them and apply the checks people already perform, so problems surface at the start rather than the end.
- 3
Automate the handoffs
Replace status chasing with tracked states and notifications, so each stage knows when work is ready and what is missing.
- 4
Keep people on exceptions
Route the cases that need judgement to the right person with the context attached, and measure the new cycle against the baseline.
Scope
What we need, what you receive
- What we need from you
- One named workflow and the people who run it.
- Access to the systems that hold its inputs and outputs, or exports where direct access is not possible.
- The rules and checks the team applies today, even if they are informal.
- Two or three recent cycles to use as the baseline.
- What you receive
- A measured baseline of the current workflow and where its time goes.
- Automated collection, validation and handoff for the agreed steps.
- An exception queue that gives people the context they need to decide.
- Before-and-after measurement of the agreed metric, with its definition.
- Not included
- Replacing core systems of record such as payroll, ERP or production tracking platforms.
- Automating decisions that require accountable human judgement.
- Organisation-wide process redesign beyond the agreed workflow.
- How success is measured
- Elapsed turnaround for the workflow, measured the same way before and after.
- Errors caught at input rather than discovered downstream.
- Human time concentrated on exceptions rather than collection and chasing.
How to buy it
Start with a bounded first step
Decision guide
The questions buyers ask, in the order they ask them
01
Is this a software problem or a process problem?
Usually both. Mapping one real cycle shows how much time is spent waiting, chasing and re-keying — the software-shaped parts — and how much is genuine judgement that should stay with people.
02
Do we need AI, or ordinary automation?
Often ordinary automation does most of the work: integration, validation rules and tracked handoffs. AI is worth adding for unstructured inputs, classification and drafting, and only where it measurably helps.
03
Should we buy a workflow product instead?
Sometimes, and we will say so. If an existing platform covers the workflow with configuration, that is usually cheaper to own. Custom work is justified when the integrations, rules or volume fall outside what products handle well.
04
What does a first engagement look like?
A well-understood workflow can go straight to a fixed-scope sprint. A less clear one starts with a short readiness review to map the cycle and agree the metric. Indicative prices are on the engagement pages.
Evidence
What you can check before trusting us
Client results and public engineering are kept apart; the evidence method explains why.
Client delivery
A UK employer with 250–1,000 staff
Payroll preparation
48 hours → 40 minutes
Elapsed turnaround per payroll cycle.
Read the case study →An independent animation studio with under 50 staff
Production tracking and stage handoffs
20 days → 1 week
Elapsed turnaround for the production workflow in scope.
Read the case study →Neul Labs public engineering
Neul-owned work you can inspect — not client case studies or warranties, and never a requirement.
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Further reading
A good fit
- The workflow recurs often enough that improvement compounds.
- Its inputs live in systems that can be read through APIs, exports or integrations.
- The team running it wants the change and will help define the rules.
Not a good fit
- The process runs a few times a year and is dominated by one-off judgement.
- The real constraint is a policy or approval decision rather than the workflow itself.
- The expectation is to remove people from accountable decisions entirely.
Questions people ask
How do you measure the improvement?
We agree one primary measure before starting — usually elapsed turnaround from inputs arriving to output ready — and measure it the same way before and after. The definition is published alongside any figure we quote.
Will this replace our existing systems?
No. The work connects and automates around your systems of record. Replacing a payroll, ERP or production-tracking platform is a different project, and rarely the one that shortens the workflow.
Can you automate a workflow that involves sensitive data?
Yes, with appropriate controls. We start with synthetic data and least-privilege access, agree how sensitive data is handled before touching it, and keep it out of tools and services that do not need it.
What happens after the first workflow?
If it works, adjacent workflows often share the same systems and rules, so the second is usually faster. Each is scoped and measured as its own decision.
Other situations we help with: launch production ai · rescue an ai project · make ai useful for a team · get an ai system approved.
Make a demanding workflow shorter and dependable
Each cycle of a slow workflow costs skilled time, delays the decisions that depend on it and makes growth require proportional hiring.
Scope this with us