Consultant staffing in 10 seconds

AI reads the client request, searches the database, proposes the top 3 candidates ranked by match. You confirm, AI prepares the proposal.

Manual staffing is the bottleneck

Every client RFP requires hours: read JD, filter database, contact candidates, verify availability, prepare proposal. Time that becomes lost opportunities to faster competitors.

RFPs queued for days
Database with thousands of CVs hard to search
Consultant availability always to verify by hand
Proposals built in Word from scratch every time
Bench unmonitored: idle consultants while compatible requests come in

AI-driven staffing, end-to-end

hice.ai automates the whole flow: from JD reading to signed proposal. Manager stays in control, AI handles the repetitive work.

"Client Enel asks for 2 senior AWS Cloud Architects, 12 months, Rome"
"Notify Anna and Luca they've been proposed for the BNL project"
"Generate a commercial proposal for Generali with the data engineering team"
"Update Marco's availability from February 1"

Try it in chat

Examples you can ask:

Static demo of hice's AI chat.

What you get

AI parsing of JDs and CVs

Automatic extraction of skills, seniority, location, duration, rate.

Match scoring

Compatibility score candidate-project with explanation.

Calendar integration

Real consultant availability based on existing allocations.

Proposal generator

Commercial PDF documents generated with one command.

Bench radar

Auto alerts when JDs match benched consultants.

Visual pipeline

Status of each proposal: sent, in evaluation, won, lost.

The data without which no staffing proposal is reliable

No intelligence layer compensates for an incomplete inventory. For an assignment proposal to be useful you need five datasets: profiles with dated skills, a real availability calendar with leave and contracted hours, current assignments with allocation percentage, demand with roles and time windows, and role economics.

If one is missing, the system degrades predictably. Without leave, it proposes people who will be away. Without allocation percentage, it confuses someone partly booked with someone free. Without skill dates, it proposes expired experience. Fixing the data is worth more than widening the feature set.

Skills with level, evidence and verification date
Availability with leave, working hours and internal commitments
Assignments with allocation percentage and end date
Sell rate and cost by role with separate permissions

Scenarios instead of a single recommendation

A ranked list hides the alternatives. In practice a good answer to a staffing request takes four shapes: the ideal option that is probably unavailable, the available option with an identified gap and a plan to close it, the one that delays the start by two weeks to free the right profile, and the mixed option pairing a part-time senior with someone developing.

Presented that way, the client conversation changes in nature. Instead of negotiating over a single proposal, you negotiate over an explicit trade-off between date, profile and price, which is a far stronger position and usually ends better for both sides.

Several alternatives with impact on margin, risk and date
Skill gaps flagged with their coverage plan
Resequencing options when the ideal profile is busy
Effect of each scenario on team development

Watching the bench before it appears

The bench is almost never a surprise: it is an assignment ending on a known date with nothing behind it. What is missing is somebody looking at that date early enough to act commercially.

A fifteen-minute weekly routine focused only on exceptions handles most of it. It should show who finishes an assignment in the next six weeks, which tentative bookings have expired, which probable demand could absorb that capacity and which skills are going unused. Every line needs an owner and a date, not just an observation.

Assignment end visible weeks in advance
Tentative bookings with a confirm-or-release date
Cross-check between capacity freeing up and probable demand
Actions with an owner and a date, not just alerts

150-consultant body rental firm

Staff manager processed 25 RFPs/week, 20 minutes each = 8+ hours. With hice.ai: 25 × 2 minutes = under one hour. 7 hours/week freed for higher-value commercial activities.

hice.ai vs manual staffing or classic ATS

Feature	hice.ai	Classic ATS
Time to process an RFP	2 minutes	20-30 minutes
AI matching	Native	Manual filters
Real-time availability	Yes	Verify by hand
Auto commercial proposal	Yes	Manual Word
Bench alerts	Yes	No
License cost	Included in PSA	Separate tool