Automate every consulting workflow with AI

Onboarding, approvals, billing, reporting: all in chat. Manual flow available too.

The limits of traditional tools

Classic tools for workflow automation are full of forms, screens and configurations. Consultants lose hours on admin instead of working with clients.

Dozens of screens and tabs to learn
Initial setup that takes weeks
No AI that understands consulting context
Data scattered across many tools
Reports always days behind

One AI, one conversation

hice.ai handles workflow automation like a chat: type what you want, AI acts on your real data. For those who prefer the classic approach, the traditional manual interface is always available.

«Show me the status of all open projects»
«Update X with what I'm telling you»
«Generate the monthly client report»
«Alert me if anything goes over budget»

Try it in chat

Examples you can ask:

Static demo of hice's AI chat.

What you get

AI chat in plain English

Talk naturally, AI understands jargon, abbreviations and context.

Real actions on data

AI doesn't just answer: it updates, creates, modifies, always with audit trail.

Total memory

AI remembers every conversation, decision and historical data point.

Native integrations

Calendar, mail, billing, databases: all connected.

Configurable automations

Triggers on events, conditions, deadlines: AI acts or alerts.

Conversational dashboards

Ask any chart in chat: no more complex report builders.

Multi-tenant

Per-client data isolation, granular permissions.

Full audit trail

Every AI action is tracked, reversible, exportable.

Smart notifications

Only notifications that matter, grouped by AI.

EU data & GDPR

EU hosting, encryption at-rest and in-transit, daily backups.

Fast onboarding

No weeks of training: a chat is a chat.

Traditional interface

For those who prefer forms, tables and classic dashboards: always available alongside AI.

Choosing which workflow to automate first

The instinct is to automate the process that annoys people most, and that is usually the wrong starting point, because the most annoying process is often the one with the most exceptions. The first automation should be boring, high-volume and stable, because its job is to prove the approach works.

A simple ranking makes the choice defensible: frequency times time per occurrence, divided by the number of genuine exceptions. Processes that score high are the ones where automation pays back in weeks. The annoying-but-rare process can wait until the team trusts the tooling.

Frequency and time per occurrence measured, not estimated
Number of genuine exceptions counted before starting
First automation chosen for reliability, not for drama
Payback estimated in weeks, and checked afterwards

Designing for the exception, not only the happy path

Automations fail in production for a predictable reason: they were designed around the case that happens eighty percent of the time, and the remaining twenty percent has nowhere to go. The work then reappears as a person quietly handling exceptions in a spreadsheet, and the reported saving never materialises.

The discipline is to decide, before building, what happens to everything the automation cannot handle: who receives it, how quickly, and how it gets back into the flow once resolved. An automation with an explicit exception path at seventy percent coverage beats one with no path at ninety-five.

Named owner for every exception queue
Exception volume monitored as a first-class metric
Resolved exceptions re-entering the normal flow
Recurring exceptions reviewed as design feedback

Keeping automations from rotting

An automation is code, and code that nobody owns decays: a form changes, an API version is retired, a rule that made sense last year silently produces wrong results. The failure is rarely loud, which is what makes it expensive.

The maintenance that works is small and scheduled. Each automation has an owner, a written statement of what it assumes, and a quarterly check that those assumptions still hold. Automations that no longer earn their keep get switched off deliberately, which is far cheaper than discovering in eight months that they have been wrong all along.

A named owner per automation, reviewed when people move
Written assumptions, checked on a fixed cadence
Alert when an automation stops running or errors silently
Deliberate retirement of automations no longer worth it

Consulting firm, 80 consultants

An 80-consultant firm managed workflow automation with legacy tools: hours lost on admin every week. With hice.ai AI absorbs most repetitive work. Result: -50% admin time, +30% delivery capacity, managers more client-focused.

hice.ai vs traditional workflow automation tools

Feature	hice.ai	Traditional tools
Interface	AI chat + classic	Only forms and menus
Native AI	Yes, acting agent	No / add-on
Languages	Multi (IT/EN/FR/ES)	Often EN only
Setup	Days	Weeks / months
Real-time data	Yes	Delayed reports
Real-time KPIs	Yes: utilization, margin, bench	Delayed reports
Learning curve	One chat	Long training