We know, because we have delivered digital products and transformation across Citi, Standard Chartered, DBS and Arab Bank.
One workflow. Your systems. A number. Three specifics you can verify, and hold us to.
Only 21% of companies using generative AI have fundamentally redesigned any workflow around it. The other 79% have bought capability and changed nothing about how the work is done.
That is the whole problem, and it is not a technology problem.
One point for each line that was true. Tap the ones that were.
Tap each line that was true of your pilot. The score reads itself.
Rework, waiting and re-keying are three things any COO can point to on a whiteboard within thirty seconds. We find where the work actually stalls.
Not a new tab, not a new login. The screen your staff touch sits where they already work, wired to the systems that hold the truth.
The specific points where a person signs, what happens when they do not, and where an exception stops and finds a human instead of guessing.
The baseline, its calculation method, its data source and its owner. If we cannot agree a baseline before we build, we do not build.
Written into the engagement, not offered as a courtesy. Alongside it, the guardrails that must not get worse, at least one named by compliance.
That last step is the difference. It is the risk you have otherwise been carrying alone.
The new sequence of steps, written down, with the removed steps struck through.
Inside the tool they already have open. Not a new tab, not a new login.
To your core system, your CRM and your document store, under your access controls.
The decisions where a named human signs, and what happens when they do not.
Adoption, task success, cost, exceptions and overrides. One screen, your data, your definitions.
Personalised engagement, service that answers at once, recommendations that fit. Designed with real customers, measured after launch.
That is the whole of it. Everything between a model that can do the thing, and the thing getting done on Tuesday morning.
You end with the questions your pilot is missing answered, a baseline everyone has signed, and a ranked shortlist of what to build first. If the honest answer is that AI does not belong in this workflow, that is what the report says, and you have saved a build.
The redesigned workflow, how the human and the AI interact, and the case for building it.
Production implementation on Ergon and Skopos, integrated with your systems and your approved models, tested with your people.
The number, published monthly, including when it is bad. Prompts and workflows tuned, exceptions reviewed, next use cases identified.
Revenue is earned at the top line, not only saved in operations: campaigns that know your stock, personalised engagement that converts, next-best-action in front of the RM, shopping and sales assistants that close, service agents that resolve at first contact. Your customers feel it: served faster, answered accurately, followed up on time. And it compounds: the next use case takes weeks, not quarters, because the connections, controls and reporting already exist.
The team that maps your workflow in week one is the team that defends it in your risk review, and the team on the call when adoption dips in month four. No pitch team that vanishes after signing, no handover, no re-explaining your business to a delivery unit you did not meet.
If the number does not move, we say so in the monthly report, we tell you why, and we agree whether to fix it or stop. We would rather lose the retainer than run a workflow nobody can defend.
The two products behind every engagement. They are what stops your project starting from zero.
Turns business processes into coordinated, measurable human and AI workflows.
Makes AI performance, risk and business value visible.
Here is what has to be true before we put one in front of your customers.
That list is the product. The chat window is the least interesting part of it.
Customer service agents · employee knowledge assistants · relationship manager copilots · product and sales assistants · retail shopping assistants · operations and compliance assistants · voice-enabled service agents
Explore agentic assistants · coming soon→On 14 March this went to a customer. Who authorised it, what did it read, did anyone check it, and had it gone wrong before?
Today that question starts a three-week investigation across four teams. We make it a four-minute answer on one screen.
Every AI action leaves the same eight-field Transparency Record, on every system we build for you. Your risk team learns it once, and it is in place before go-live, not assembled after an incident.
We do not certify your compliance. Your regulator and your advisers do that, and any vendor who tells you otherwise is selling you a liability. What we build is the evidence they will ask for.
Before every client review, an RM assembles a pack: holdings, recent activity, product fit, what was promised last time, and what compliance will allow them to say. It happens the night before, and its quality depends on which RM you get.
Praxis rebuilds it as a draft that arrives before the meeting, sourced, with the compliance-sensitive lines flagged. The RM edits instead of assembles. Every version is logged, so the pack that was actually used is recoverable a year later.
Most retailers run marketing and inventory as separate systems with a meeting between them. So the campaign that works sells out in four days, and the one that fails leaves stock nobody planned for.
We connect the two. The campaign knows what you are holding. The buying plan knows what demand is doing this week. The number we move is picked with you in the diagnostic: stockouts on promoted lines, markdown on slow stock, or margin per campaign.
Document and data handling, internal approvals, exception routing, quality assurance, knowledge retrieval, reporting and reconciliation. Rebuilt as human-in-the-loop workflows with the same controls and the same dashboard as everything else we ship.
Every offer connects a defined user, a workflow, a business outcome, a control model and a measurement plan. We do not enter a sector with a generic AI feature.
Humancraft AI exists to build it alongside.
We are headquartered in Singapore, working with banks and ambitious enterprises across Asia, Europe and the Middle East. Outcomes that compound. Built with the humans who use them.
Models create possibility. Humancraft turns that possibility into a way of working.
Outcomes you can see · Controls you can trust