4
Contract Management and Administration
Overview
Effective contract management and administration are
pivotal for the mining sector, where contracts often repre-
sent significant financial commitments and complex legal
arrangements. Site teams, while expert in their operational
roles, may lack the time or legal expertise to manage these
contracts meticulously, potentially leading to costly con-
tractual disputes or oversights.
An AI-powered solution can transform contract man-
agement by providing an intuitive way for team members
to interact with contract documents, understand their
obligations, and identify out-of-scope requests that require
additional billing.
Furthermore, LLMs can continuously monitor project
communications and reports, intelligently identifying and
flagging potential contractual issues before they escalate,
such as the need to notify clients of claimable events.
How the Solution Could Work
An LLM-based contract management system could employ
Retrieval Augmented Generation (RAG) to serve both
primary functions: interactive document search and auto-
mated report and email scanning. For document search,
when users pose questions or seek clarifications, the LLM
would instantly retrieve and synthesize information from
the relevant contracts and legal documents, providing pre-
cise answers and guidance.
In terms of automated scanning, the LLM would con-
tinuously analyze incoming reports and emails, using RAG
to cross-reference the contents with contractual obligations
and terms. By understanding the significance of the text
within the context of the contract, the LLM could iden-
tify potential issues or actions that need to be taken, such
as notifying the client of claimable events. This dual func-
tionality ensures that contractual obligations are proactively
managed and that compliance is maintained across all com-
munication channels.
Value Provided
The introduction of an LLM into contract management
provides substantial value by equipping site teams with the
tools to interpret and adhere to complex contracts accu-
rately. This capability ensures that all contractual obliga-
tions are met, variations are correctly billed, and potential
disputes are minimized. The LLM’s proactive monitoring
can alert teams to actionable items, ensuring compliance
with contract terms and safeguarding against financial
losses due to unclaimed entitlements.
Implementation Feasibility
The feasibility of deploying an LLM for contract manage-
ment using Retrieval Augmented Generation (RAG) is
quite promising, given that RAG circumvents the need for
extensive pre-training on specific documents. This technol-
ogy allows the LLM to pull the most relevant and up-to-
date information from a document repository in real-time,
which is crucial for providing accurate responses to con-
tract-related queries and for monitoring communications
effectively. However, the success of this implementation
largely depends on the seamless integration of the LLM
with the company’s existing contract management systems
and communication channels. This integration is critical to
ensure that the LLM has comprehensive access to all neces-
sary documents and data flows.
The design of the system must prioritize user-friend-
liness to encourage adoption by site teams who may not
be technically inclined. Additionally, the document reposi-
tory that the LLM relies on must be meticulously main-
tained and regularly updated to reflect the latest contractual
changes and legal updates, ensuring the system’s ongoing
accuracy and relevance.
Bidding and Tendering
Overview
In the competitive landscape of bidding and tendering for
mining contracts, the ability to rapidly sift through exten-
sive tender documents and evaluate opportunities against
specific criteria is crucial. Large Language Models can auto-
mate this process, enabling contractors to efficiently assess
the viability of a tender and ensure compliance with their
policies.
By using LLMs to automatically draft departures from
standard terms, contractors can align tender responses with
their internal risk profiles and operational capabilities. This
application of AI not only streamlines the tender review
process but also enhances the quality and consistency of
bid submissions.
How the Solution Could Work
An LLM-based system for bidding and tendering would
allow users to upload tender documents, which the AI
would then analyze to extract key information. The LLM
would be equipped to understand and interpret the com-
plex language often found in these documents, including
technical specifications, contractual obligations, and com-
pliance requirements.
Users could input a set of criteria reflecting the con-
tractor’s policies and risk thresholds, and the LLM would
Contract Management and Administration
Overview
Effective contract management and administration are
pivotal for the mining sector, where contracts often repre-
sent significant financial commitments and complex legal
arrangements. Site teams, while expert in their operational
roles, may lack the time or legal expertise to manage these
contracts meticulously, potentially leading to costly con-
tractual disputes or oversights.
An AI-powered solution can transform contract man-
agement by providing an intuitive way for team members
to interact with contract documents, understand their
obligations, and identify out-of-scope requests that require
additional billing.
Furthermore, LLMs can continuously monitor project
communications and reports, intelligently identifying and
flagging potential contractual issues before they escalate,
such as the need to notify clients of claimable events.
How the Solution Could Work
An LLM-based contract management system could employ
Retrieval Augmented Generation (RAG) to serve both
primary functions: interactive document search and auto-
mated report and email scanning. For document search,
when users pose questions or seek clarifications, the LLM
would instantly retrieve and synthesize information from
the relevant contracts and legal documents, providing pre-
cise answers and guidance.
In terms of automated scanning, the LLM would con-
tinuously analyze incoming reports and emails, using RAG
to cross-reference the contents with contractual obligations
and terms. By understanding the significance of the text
within the context of the contract, the LLM could iden-
tify potential issues or actions that need to be taken, such
as notifying the client of claimable events. This dual func-
tionality ensures that contractual obligations are proactively
managed and that compliance is maintained across all com-
munication channels.
Value Provided
The introduction of an LLM into contract management
provides substantial value by equipping site teams with the
tools to interpret and adhere to complex contracts accu-
rately. This capability ensures that all contractual obliga-
tions are met, variations are correctly billed, and potential
disputes are minimized. The LLM’s proactive monitoring
can alert teams to actionable items, ensuring compliance
with contract terms and safeguarding against financial
losses due to unclaimed entitlements.
Implementation Feasibility
The feasibility of deploying an LLM for contract manage-
ment using Retrieval Augmented Generation (RAG) is
quite promising, given that RAG circumvents the need for
extensive pre-training on specific documents. This technol-
ogy allows the LLM to pull the most relevant and up-to-
date information from a document repository in real-time,
which is crucial for providing accurate responses to con-
tract-related queries and for monitoring communications
effectively. However, the success of this implementation
largely depends on the seamless integration of the LLM
with the company’s existing contract management systems
and communication channels. This integration is critical to
ensure that the LLM has comprehensive access to all neces-
sary documents and data flows.
The design of the system must prioritize user-friend-
liness to encourage adoption by site teams who may not
be technically inclined. Additionally, the document reposi-
tory that the LLM relies on must be meticulously main-
tained and regularly updated to reflect the latest contractual
changes and legal updates, ensuring the system’s ongoing
accuracy and relevance.
Bidding and Tendering
Overview
In the competitive landscape of bidding and tendering for
mining contracts, the ability to rapidly sift through exten-
sive tender documents and evaluate opportunities against
specific criteria is crucial. Large Language Models can auto-
mate this process, enabling contractors to efficiently assess
the viability of a tender and ensure compliance with their
policies.
By using LLMs to automatically draft departures from
standard terms, contractors can align tender responses with
their internal risk profiles and operational capabilities. This
application of AI not only streamlines the tender review
process but also enhances the quality and consistency of
bid submissions.
How the Solution Could Work
An LLM-based system for bidding and tendering would
allow users to upload tender documents, which the AI
would then analyze to extract key information. The LLM
would be equipped to understand and interpret the com-
plex language often found in these documents, including
technical specifications, contractual obligations, and com-
pliance requirements.
Users could input a set of criteria reflecting the con-
tractor’s policies and risk thresholds, and the LLM would