Tenders, technical customer enquiries and proposals involve many sources and stakeholders. AI can structure requirements, surface relevant knowledge and prepare drafts for professional review.
How much time does your sales team lose gathering requirements, technical answers and proposal content?
USE CASE EXAMPLES
How AI could help in everyday work.
Illustrative scenarios. Requirements and value are assessed for your specific process.
01
AI-ASSISTED REQUIREMENTS ANALYSIS
Analyse tenders and technical specifications
THE STARTING POINT
Large tenders spread requirements, deadlines, supporting evidence and technical specifications across many documents. Contradictions and open questions can easily be missed.
A POSSIBLE APPROACH
AI can extract requirements with source references and prepare a requirements matrix. Comparing these with approved capability descriptions highlights possible matches, gaps and points to clarify. Mandatory requirements and evaluation criteria are listed separately.
WHAT YOU NEED
Complete tender documentation, including attachments and amendments, approved capability descriptions, clear criteria and assigned review responsibilities.
UNDERSTAND THE AI
What happens behind the scenes.
TECHNICAL INSIGHT
POSSIBLE SOLUTION DESIGN
Document AI · Requirements extraction · RAG matching
What does the AI actually do?
AI identifies requirements in prose and attachments and maps them to approved capability descriptions.
How the solution works
01
Structure requirements
An LLM extracts statements, mandatory criteria and dates with sources, including amendments.
02
Find capabilities
Semantic search retrieves candidates from approved product and service records.
03
Prepare the matrix
Show rationale and evidence, flagging missing support and contradictions.
What value can it deliver?
Less reading and compilation effort; earlier visibility of gaps supports an informed bid decision.
A concrete example
An interface requirement finds a related description but no evidence for the requested version; the matrix exposes the gap.
Technology, setup & operation+
Combine a pretrained LLM, index and requirements schema. Measure completeness against an expert reference. Textual similarity does not establish compliance.
Illustrative solution design. The final architecture depends on data, systems and pilot evaluation.
Pilot, value assessment & professional responsibility +
HOW A PILOT COULD START
Select a closed tender and evaluate the AI analysis against the original professional review.
HOW TO ASSESS VALUE
Compare requirement coverage, correct source attribution and initial analysis time with a manually verified reference.
Relevant specialists assess technical compliance, deadlines, contractual terms and the bid decision. Missing information remains explicitly unresolved.
02
AI IN THE PROPOSAL PROCESS
Prepare proposals from approved knowledge
THE STARTING POINT
Tailored proposals require sales, presales and technical teams to bring together product information, scope and suitable content. Version differences and unintended commitments make alignment harder.
A POSSIBLE APPROACH
AI supports a proposal draft based on confirmed customer requirements, current templates and approved content. It suggests suitable sections and flags missing information, assumptions and deviations for review.
WHAT YOU NEED
Current proposal templates, approved product and service information, confirmed requirements and a defined review and approval process.
UNDERSTAND THE AI
What happens behind the scenes.
TECHNICAL INSIGHT
POSSIBLE SOLUTION DESIGN
Generative AI · RAG · Template-based proposal draft
What does the AI actually do?
AI selects approved content for confirmed requirements and turns it into a coherent draft.
How the solution works
01
Define context
Confirmed requirements and the current template determine scope and structure.
02
Assemble content
RAG supplies approved service sections; an LLM connects them and marks assumptions.
03
Check figures and commitments
Authoritative systems supply prices and dates; defined reviewers approve the draft.
What value can it deliver?
Less first-draft effort and less searching for current content can shorten proposal preparation.
A concrete example
An additional service receives an approved description; an unavailable price remains an open field.
Technology, setup & operation+
The LLM supports wording and structure. Pricing and approvals remain in their designated systems, with source and revision links retained.
Illustrative solution design. The final architecture depends on data, systems and pilot evaluation.
Pilot, value assessment & professional responsibility +
HOW A PILOT COULD START
Trial one recurring proposal type with a clearly defined scope and a small sales and presales team.
HOW TO ASSESS VALUE
Compare preparation time, correction effort, completeness and the number of review cycles with the existing workflow.
Prices, discounts, technical commitments, delivery dates and contractual clauses come from the responsible sources and are approved before sending. Missing values are not invented.
03
AI IN CUSTOMER MANAGEMENT
Analyse customer enquiries and change requests
THE STARTING POINT
Technical questions and change requests are spread across emails, meeting notes, CRM records and proposal versions. Reconstructing current needs and existing agreements takes time.
A POSSIBLE APPROACH
AI can consolidate authorised information for an enquiry, extract requirements and open points, and flag changes against the previous position. This supports an action list and a draft response with source references.
WHAT YOU NEED
Linked customer and case information, approved meeting notes and correspondence, current proposal versions and defined access rights.
UNDERSTAND THE AI
What happens behind the scenes.
TECHNICAL INSIGHT
POSSIBLE SOLUTION DESIGN
NLP · Context summarisation · Change comparison
What does the AI actually do?
AI extracts new requirements from correspondence and identifies differences from a confirmed baseline.
How the solution works
01
Limit the case
CRM link, customer and permissions determine accessible messages and proposal revisions.
02
Compare statements
An LLM extracts requirements and compares them with the confirmed baseline.
03
Prepare clarification
Source-linked changes, questions and possible actions are presented for confirmation.
What value can it deliver?
Less effort reconstructing agreements and a lower risk of missing change requests in long threads.
A concrete example
An email requests an additional acceptance test; the assistant flags it as a new request for effort assessment.
Technology, setup & operation+
A pretrained LLM can extract and summarise. Baseline, date and status must be explicit; a new request does not become an agreed commitment.
Illustrative solution design. The final architecture depends on data, systems and pilot evaluation.
Pilot, value assessment & professional responsibility +
HOW A PILOT COULD START
Investigate a defined customer workflow using selected closed enquiries and manually verified results.
HOW TO ASSESS VALUE
Evaluate summary completeness, correctly identified changes and time spent on typical customer enquiries.
Customer information remains within the authorised group. Responsible staff review tasks, CRM changes and responses before adoption or sending.
MY CONTRIBUTION
Work out together what is worth pursuing.
I help you turn an interesting use case into a clear, assessable business task.
Structure needs and objectives with business functions and IT
Clarify value, requirements and open questions
Prepare a basis for decisions and define a possible pilot scope
Support alignment with suitable implementation partners
FREQUENTLY ASKED QUESTIONS
Before taking the first step.
Can AI send binding proposals on its own? +
The approach described here prepares drafts and review steps. Pricing, technical commitments, contractual terms and sending remain within the company’s defined approval process.
Do we need a new CRM first? +
No. A pilot can begin with a curated set of tenders, approved proposal content and selected case data. Later integration with CRM, document management or ERP is assessed against value and operating requirements.
FROM A USE CASE TO THE NEXT STEP
Which idea would you like to make concrete?
Together, we clarify the starting point, possible use cases and expected value, creating a sound basis for your next decisions.