AI project management in plain English

Plan, track and bill projects by chatting with AI. Traditional interface always available.

The limits of traditional tools

Classic tools for project management 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 project management 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.

The signals that a project is drifting, before the numbers move

A project rarely goes wrong on the date it is declared to be in trouble. The evidence is there weeks earlier, in signals that no status report records: hours drifting toward tasks that were not in the plan, the same milestone re-baselined twice, a client contact who has stopped answering, a team member who has been at ninety percent on one deliverable for three weeks.

Surfacing those automatically does not replace the project manager's reading of the situation; it gives them the situation earlier. The value is not the alert, it is the four weeks of room the alert buys to renegotiate scope while renegotiation is still a normal conversation.

Hours booked to tasks outside the agreed plan
Milestones re-baselined more than once
Deliverables stuck at near-completion for weeks
Client-side responsiveness declining on a project

What to automate in status reporting, and what not to

Status reports consume an astonishing amount of senior time and communicate less than anyone would like, mostly because the effort goes into assembling numbers rather than into interpreting them. The assembly is entirely automatable: hours, spend against budget, milestone dates, open risks.

The interpretation is not, and should not be. A report that generates its own commentary trains everyone to skim it. Keeping the numbers automatic and the two-paragraph assessment human is what makes a client actually read the update, and it is also what forces the project manager to form an opinion every week.

Figures assembled automatically from the live data
Assessment and risk commentary written by the manager
Same structure every week so changes stand out
Open decisions listed with the date they were raised

Estimation that gets better instead of repeating itself

Most firms estimate from experience and never close the loop, so the same optimism repeats project after project. The correction is unglamorous: record the estimate at the moment it is made, record the actual at close, and look at the ratio by project type and by whoever estimated.

The pattern that appears is usually not random. Certain work types are consistently underestimated, certain people are consistently optimistic by a stable factor, and both are correctable once visible. A firm that applies its own historical ratio to new estimates improves its margin without changing anything about how it delivers.

Original estimate frozen and kept for comparison
Estimate-to-actual ratio by work type and estimator
Historical correction factor applied to new proposals
Post-project review focused on the estimate, not blame

Consulting firm, 80 consultants

An 80-consultant firm managed project management 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 project management 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