KT Sparks

About KT Sparks

Emerging technology, put to work on processes that already exist. An engineering-led consultancy across AI, automation, software and process improvement.

Why we do this work

Every organisation already runs on processes that were designed for a different decade. Invoices arrive as PDFs and are typed into a ledger. A statement is downloaded, reformatted and reconciled by hand. A case file moves between four systems because none of them was bought to talk to the other three. None of this is anyone's fault; it is the sediment of twenty years of sensible individual decisions.

The cost is not the hours, though the hours are real. The cost is that the organisation cannot change shape. Every new product, market or regulation lands on top of manual work that has to be re-learned, re-staffed and re-checked. Capacity gets spent on keeping yesterday running.

Emerging technology is the only lever that moves that without a rebuild. You cannot replace the ERP to fix a reconciliation. You can put a document model, an orchestration layer and a well-tested agent step between the systems you already own, and give the work back. That is a months-long project rather than a multi-year programme, and it is reversible if it turns out to be wrong.

The difficulty is that the field is loud. Anything genuinely new arrives wrapped in claims that are two years ahead of what it can actually do, and the gap between a compelling demonstration and something that survives a quarter-end close is where most automation budgets die. Reading that gap correctly is a skill, and it is most of what we sell.

So we do three things, in this order. We measure the process as it actually runs, using its own event data, because a backlog built from opinions ranks the loudest request first. We say which candidates are not worth automating - usually a third of them - before anyone has paid for a build. Then we build the remainder to a standard that assumes we will not be there in year two: monitoring, runbooks, an evaluation set for every model step, and a named owner on your side who has operated it before we step back.

What you get out of it is not a robot. It is capacity you can point at something else, and an organisation that can absorb the next change without hiring for it.

What we hold to

  • 01

    Measure first

    No automation is proposed without a baseline it can be held against.

  • 02

    Say no

    We will tell you which candidates are not worth automating.

  • 03

    Hand it over

    Documented, maintainable, and yours - whether or not we stay on.

How an engagement actually runs

Five steps, in this order, every time. The early ones are cheap on purpose - they are where a bad candidate gets caught.

  1. 01

    1-2 weeks

    Baseline

    We measure the process as it runs today from its own event data: volume, handling time, rework and failure rate. No proposal is written before this exists.

  2. 02

    1 week

    Shortlist and refuse

    Candidates get ranked by measured cost. We tell you which ones we would not automate and why - usually about a third, and that conversation is free.

  3. 03

    4-8 weeks

    Pilot one process

    One candidate, end to end, against real data in a sandbox. Scope is fixed and the exit criteria are written down before the build starts.

  4. 04

    2-4 weeks

    Production

    Monitoring, alerting, exception routing, cost control and the audit trail. The unhappy path is designed first, because that is where automations die.

  5. 05

    2 weeks

    Handover

    Runbooks, documentation and a named owner on your side who has operated it live. We stay on retainer only if you want us to.