Consultancies

AI agents for firms that sell
senior people's hours.

The partner sells, delivers and is still the only one who knows what was agreed. Labs designs the agent on your proposal, project and reporting flow, connected to the systems the firm already uses.

What jams

The firm's knowledge walks out of the door every day at seven.

What was agreed with the client, what worked on the similar project and what was promised in the last meeting live in the head of whoever was there. Writing that down is the first thing a busy week drops.

  • 01What was agreed with the client lives in the head of whoever was in the meeting.
  • 02The status report eats the evening of whoever should be delivering the project.
  • 03Logged hours and billed hours diverge, and the project's margin shows up late.
  • 04Renewal arrives with no record of what was delivered, and the conversation turns into a discount.
Use cases

AI agents for every area
of your consultancy.

Hendu connects the systems and channels your company already uses and keeps the operation's context: what was agreed, with whom, and what is still missing. The agents work on top of that layer, each one as the right arm of an area. You step in to approve.

01Marketing

The firm gets found by the problem it solves.

Consulting is not bought from an ad. It is bought on reputation and proof. The agent answers whoever arrives through content, events and referrals, understands the problem before the job title, and shows which channel brought a signed project.

  • Answers content, event and referral contacts at once, on any channel.
  • Qualification by the problem described, with the company already enriched.
  • The loop closed: channel, meeting, proposal, contract.
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What changes

The same team,
operating at another level.

This is not about your team working harder. Chasing, checking, handing over, remembering: that work, the one that eats their day today, starts happening on its own, following your rule and under your eye.

Efficiency

What took a day starts taking an afternoon. Not because someone ran faster, but because the step that jammed everything stopped existing.

Productivity

People go back to the work that needs people: deciding, negotiating, solving the hard case. The rest arrives finished, waiting to be reviewed.

Cost

Growing stops meaning hiring in the same proportion. The operation absorbs more volume on the structure it already has.

Margin

Less rework, fewer forgotten commitments, fewer missed deadlines. That is margin that was already yours and was leaking on the way.

Scale

What works in one team is repeated in the others the following week, because the rule is written down, not held in one person’s head.

How it works

Seven days.
One process, live.

Not a six-month project. One process at a time. And you see the first one working before you decide on the second.

Days 1 and 2

We map the rule

We sit with the people who do the work: the exceptions, the limits, who decides what.

Days 3 to 5

We build

The agent is built in your vocabulary, on the systems the company already uses.

Days 6 and 7

It goes live

It runs on real work, supervised, and is adjusted in use before it leaves our hands.

After

It expands

With the first one running, the second costs less: context and governance already exist.

Why the Labs exists

AI is already in your company.
The work has not changed.

Someone on your team opened ChatGPT today. It helped write an email, summarise a document. But the payment nobody chased, the deadline nobody checked and Friday's report are still done by hand, exactly as before. It is worth understanding why. That is what the Labs fixes.

Ask an off-the-shelf AI tool for a payment reminder and it writes a flawless email in ten seconds. But it does not know how much that client owes, what was agreed with them last week, whether your finance team already chased them yesterday, or that your company never chases before five days overdue. You are the one who knows all that. So you type in the context, read what comes back, fix what is wrong. And you end up spending nearly as long as writing it yourself. The real work was never writing the email. It is knowing what has to be in it.

The screen

This is where the agents live.
You approve, they do the rest.

The agent the Labs builds does not live in a separate window. It comes into this screen, alongside the rest of your team’s work: this is where it shows what it did, what it proposes and what is waiting on your go-ahead.

MONDAY, AUGUST 30Full day
Good morning, Laura.
SINCE YESTERDAY AFTERNOON3meetings transcribed9new commitments4no reply
How the day breaks down
8am7pm
8am – 12pmThree meetings back to back. Most of today’s commitments come from here.
12pm – 4pmThe clearest block of the week. Good for clearing what is stuck.
4pm onwardsTwo deliveries due at end of day. After that Friday closes clean.
Moving as expected14 commitments on trackVitrine: kickoff bookedtomorrowLima: proposal acceptedyesterday
Today's briefing
Priority actions
Agent flow3 activeCustomer follow-up3/5Chasing open items3/5Contract renewal2/6
Decisions with traction14Moving4Late2Stuck

Sector not listed?
The conversation starts from your process.

Half an hour to find where it hurts in your operation, and a picture of what AI changes in it. If it makes sense, the first agent is live in seven days.