DD Sherpa
A 500-document data room read in 3 hours, with exact piece citation for every finding. Produces a structured red-flag report, ranked by severity and category, for M&A firms, investment banks and PE/LBO funds.
On a typical DD, 2 weeks of junior-associate time recovered (~€80k billed at M&A rates of €800-1,200/hour). And — more importantly — reduced risk of a missed red flag that ends up in the SPA and activates the liability warranty 18 months later.
The modern data room kills the junior associate who tries to read everything.
DD manuelle
Deux juniors, deux semaines, 500-2 000 documents
- · Intralinks / Datasite : statuts, comptes, contrats, contentieux
- · Kira / Luminance extraient des clauses, n'orchestrent pas la DD
- · Aucun outil ne refuse de halluciner sur une pièce manquante
- · Risque résiduel coûte des millions sur la garantie de passif
DD automatisée + traçable
Citation pièce + page + extrait mot pour mot, ou rien
-
›
Alertes triées
critical→lowavec citation obligatoire -
›
Chiffre non sourcé → marqué
[à confirmer] -
›
Pièce non lue → ajoutée à
documents_unread, jamais inventée - › Légifrance vérifié en direct sur les questions de droit
Four steps, from incoming document to human decision.
-
1
Scope bounding
The agent calls `list_documents` to get the complete index: document_id, name, category, page_count, size. Without this call: no analysis — it refuses to fabricate findings on a data room it hasn't seen.
-
2
Prioritized multi-category reading
Articles + shareholder agreement, 3-year audited accounts, top 10 customer contracts by revenue, litigation, IP, HR, compliance, tax. Per piece: 0 to N red flags + 0 to M informational findings, with textual citation (no paraphrase) + exact page.
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3
Cross-cutting search + jurisprudence check
`search_data_room` sweeps themes (change of control, penalties, class action, sanctions, tax assessment). `check_external_jurisprudence` sources Légifrance rulings to back red flags that hinge on a legal question. No invented ruling — verification is live against Légifrance.
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4
Structured JSON report + audit chain
Red flags sorted `critical` → `low`, each with estimated financial impact + supporting pieces + recommended next steps (side letter, earn-out, specific warranty). `documents_unread` exhaustively lists what was NOT read (timeout, format, oversize). `human_review_recommended` flags arbitration points for the senior partner.
Tools, connectors, deployment.
Tools (function-calling)
4-
list_documents -
read_document -
search_data_room -
check_external_jurisprudence
Optional connectors
3- data-room
- legifrance
- doctrine
Each connector activates based on the customer's subscription.
$ lmbox agent deploy ./dd-sherpa \
--box BOX-XXX \
--token "$LMBOX_BOX_API_KEY" \
--api https://api.lmbox.eu
LMbox guarantees across the catalogue
Data stays with you
Model and data stay on the customer's LMbox appliance. No patient, contract or invoice data is ever sent to an external cloud.
Audit chain
Every tool call, every agent output is timestamped, hashed and admissible before the regulator (ACPR, ANSM, CNIL, EBA).
Human decision
The agent recommends, the human decides. No auto-signature, no auto-payment: final responsibility stays with the business.
Other catalogue agents
NDA Reviewer
Reads each incoming NDA, identifies non-standard clauses against the firm's internal template library, drafts an amendment memo for the responsible partner.
Meeting Summarizer
Turns a raw meeting transcript (Teams, Zoom, locally-transcribed audio) into a structured CR: decisions made, action items with owner and deadline, open questions.
Try DD Sherpa on the public demo.
One-click sign-in. You see the agent installed on a real LMbox, with its system prompt loaded and audit chain live.