AI presales assistant for faster proposal preparation

• Client type: Presales and solution teams handling proposals, CV requests, case-study matching, and availability checks
• Scope: AI-assisted candidate validation, case-study assessment, proposal drafting, and availability support using Microsoft tools, OpenAI models, and Power Apps
• Core result: Less repetitive information gathering in presales, with the source deck modeling a 40 to 50 percent reduction in analysis effort

Growing customer interest and project demand created a presales bottleneck.

Proposal preparation was taking too much senior time, but the real drag was not limited to writing the final document. Teams were also spending hours each day reviewing CVs, checking availability, confirming domain and technical fit, and deciding which case studies were relevant enough to support a bid.

That created several practical problems:

• proposal preparation stayed slow because routine validation still depended on manual review
• candidate and case-study matching required repeated expert input instead of using a reusable decision flow
• senior architects and consultants were pulled into repetitive qualification work instead of focusing on solution design
• response times suffered when clients asked for quick confirmation on availability, skill set, or relevant proof points

The result was a presales process that struggled to keep up with demand and put too much valuable time into repeatable information-gathering tasks.

iQberry framed this as a presales knowledge and workflow problem rather than a standalone writing tool.

The goal was to support the early qualification and drafting steps that consume time before a proposal is ready for expert review. That meant combining existing collaboration and document sources with an AI layer that could interpret candidate profiles, compare them with project needs, surface relevant case studies, and assist with proposal drafting.

The source material positions the solution around:

• context-aware analysis instead of keyword-only filtering
• reuse of existing Microsoft-based information sources
• low-code workflow support through Power Apps
• faster first-pass qualification so senior people can spend more time on architecture and solution choices

This keeps AI focused on accelerating repeatable presales work rather than replacing technical judgement.

iQberry designed an AI-assisted presales solution that connects routine qualification tasks into one more structured workflow.

The source deck describes a Microsoft-based operating context using Outlook, Teams, and SharePoint, with OpenAI models providing the AI layer and Power Apps supporting workflow automation.

The solution covers:

• automated CV analysis for relevance, availability, and initial fit
• smart matching between candidate profiles and project or architecture requirements
• case-study assessment to help identify the most relevant supporting examples
• proposal drafting support to reduce manual document assembly

In practical terms, the assistant is intended to reduce the time spent collecting and checking presales information so teams can respond faster and with more consistent supporting data.

The strongest outcome in the source material is a modeled productivity case for presales rather than a published client KPI set.

According to the deck, the assistant is expected to reduce analysis and information-gathering work by 40 to 50 percent and create an illustrative annual productivity gain of GBP 25,000 to GBP 40,000. The same material also points to faster proposal preparation, lower manual workload, improved information quality, and quicker response to client questions about availability, skills, and relevant case studies.

Operationally, the value comes from making repeatable presales work more structured:

• less manual review of CVs and supporting material
• faster first-pass matching between requirements and available candidates
• quicker selection of relevant case studies
• more consistent proposal drafting inputs
• stronger knowledge retention through a reusable assistant rather than ad hoc individual memory

Because the source is a business-case deck, these outcomes are presented as expected operational gains, not reported post-implementation customer metrics.

In presales, speed matters, but speed without credible supporting information creates its own risk.

This case matters because it shows a pragmatic use of AI automation in a place where service organisations often lose time quietly: repeated validation of CVs, availability, case studies, and proposal inputs. Those tasks are important, but they do not always need to consume senior architecture time.

By structuring that work into an AI-assisted workflow, iQberry shows how teams can respond faster, protect expert capacity for higher-value decisions, and make proposal preparation more consistent without turning the process into a black box.