Aitenders vs generic AI

Generic AI answers.
Aitenders proves.

An LLM is a tool for answering one question. Aitenders is the intelligence operating system for construction — every requirement, version, and validation linked to its source clause.

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Generic AI writes text from what you paste in. Aitenders is a vertical AI OS for construction tenders and contracts . It reads the full document set, traces every requirement to its clause, and remembers every bid. Not a better chatbot. A different category.

The two machines

One runs a conversation.
The other runs the tender.

Both are real machines — built for different jobs. Drawn honestly, side by side.

GENERAL ASSISTANT · ONE THREAD you "Is this section compliant?" model "It appears compliant…" ∅ no source clause ∅ no version ∅ no record session ends ↺ starts over

Great for drafting.

One question, one fluent answer. It doesn't hold your ontology, doesn't remember your past bids, doesn't scale to a team.

AITENDERS ·A VERTICAL AI OS tenders · annexes · past bids YOUR ONTOLOGY read draft verify 100+ specialised agents REQ-0142 · covered §4.2.1 · v3 ✓ validated feeds the next bid ↺

Built for governing.

Ingests your archive, structures it on your ontology, runs 100+ agents that verify each other, links every conclusion to its source clause.

Different categories, not competing tools.

 

Generic AI

Aitenders

Sees

What you paste in, one session

Your entire tender and contract archive, any format

Holds

No ontology of your business

Your construction ontology — clauses, costs, risks

Remembers

Not your past bids

Institutional memory across projects

The AI

One general model

100+ construction-specific agents that verify each other

Traceability

A chat history

Every output linked to its source clause · § · v · ✓

COVERS

One question at a time

The whole project — win the tender, deliver the contract

Scales to

One user at a time

Teams , and consortium partners on one matrix

Runs on

The vendor's public cloud

5 deployment options, incl. sovereign & air-gapped

You can't defend an enterprise decision on a chatbot answer. That's the whole difference — and why this isn't a comparison; those are different categories.

At project scale

How would you track 20,000 deliverables across 15 companies for five years in a chat window?

You wouldn't — and you shouldn't have to. This is the scale Aitenders runs at today, on Europe's hardest projects:

0+
deliverables tracked on one register
Grand Paris Express L15 · €2B
15
contracting companies, one intelligent matrix
Grand Paris Express L15
€10B
largest programme governed on the platform
Lyon-Turin base tunnel
0+
users — shipping since 2019, not a demo
30 enterprise clients
What Aitenders actually is

Chaos in. Proof out.

The same tender, run two ways — every line below is a requirement finding its record.

A tender across chat threads — untraceable
The same tender through Aitenders
01

Ingest anything

02

Give it meaning

03

Agents at work

04

Provable output

Six differentiators — a stack, not a silver bullet

Why Aitenders, and not someone else.

01

Built for construction, and shaped to your process.

Not a general AI adapted, not a rigid SaaS. The Foundry adds features on your ontology in weeks — the platform bends to your process.

02

Deploys anywhere,
locks in nowhere.

Cloud, sovereign, your cloud, on-prem, or air-gapped. LLM-agnostic. Native SharePoint, Procore, ERP integrations — live today.

03

Traceable, not a black box.

Every risk flag, conformity status, and drafted answer links back to its source clause. The answer always comes with its evidence.

04

We run the change, not just the software.

A dedicated Business Transformation Manager embedded in your teams from kick-off to full scale.

05

Proven where it is hardest.

Lyon-Turin (€10B). Grand Paris L15 (€2B). Flamanville & Hinkley Point C. If it holds there, it holds anywhere.

06

Built for teamwork — even across consortium partners.

Multi-organisation collaboration on one intelligent matrix. Fine-grained rights per partner, commercial autonomy intact.

For your IT team

Auditable, not asserted.

Four commitments your CIO can verify — not marketing claims.

Data

Never trains models. Microsoft ZDR certified. Per-project isolation by architecture, not policy.

Compute

You choose where it runs. No CLOUD Act exposure on EU-sovereign or on-prem.

Model

LLM-agnostic — you choose the model. Air-gap runs local models only.

Audit

Every AI action traceable to author, agent, and source clause. Exportable. EU AI Act aligned.

Deployments — Aitenders EU cloud · Sovereign EU cloud · Your cloud (AWS · Azure · GCP) · On-premises · Air-gapped. Every option live today, at production scale.

One answer at a time — or your team's institutional memory.
Draft wherever your team already drafts. Generic AI is good at it, and regulators permit it.
Govern the tender in Aitenders — requirements, addenda, validations, knowledge.
On the Microsoft stack? Aitenders deploys on top of it, not instead of it.

See both jobs done properly — on your own tender.

Bring a real document set. In 25 minutes you'll see it as a requirement register, with the audit trail a buyer could ask for.

Book a 25-minute walkthrough
FAQ

Quick answers.

Is Aitenders a wrapper around a generic model?

No. It's LLM-agnostic — you choose the model, and the platform runs 100+ construction-specific agents, your ontology, and a full audit trail on top. The model is a component; the operating system is the product.

Can't I just use a chat tool for my tenders?

For drafting, yes. For the work that decides compliance — full-set requirement coverage, addenda impact, validation records — an LLM answers one question at a time, doesn't remember your past bids, and doesn't scale to a team.

Does it replace Microsoft Copilot?

No — it deploys on top of your Microsoft stack, not instead of it. Copilot assists individuals inside documents; Aitenders governs the tender across the team. They coexist at our clients.

Where does our data live?

Your choice of five deployments, from EU cloud to fully air-gapped — all live today. Data is never used to train models, and isolation is per-project by architecture.