A client’s technical annex asks for a Java application architect, and the bid team needs to know who inside the group can genuinely fill the role. Building that consultant shortlist by hand means searching profiles one at a time, checking years of experience, chasing CVs and reformatting each one for the submission.
For a consulting group with hundreds or thousands of consultants on file, that search is where bid time disappears. This post walks through a workflow that turns one job description into a ranked list, weighted by what the client cares about, with every selected CV exported in the bid template and language in a single step.
- A consultant shortlist can start from a job description sent by email or pasted into the platform, which creates a profile match in seconds.
- Summarising the requirement before sending it gives better results than forwarding a long PDF annex.
- AI reads the job description and pre-fills the requirements. The bid team adds any skills, certifications, industry experience or languages it missed.
- The match score comes from weights the team can edit, from nice-to-have to essential, and recalculates with one click.
- Results arrive as a spreadsheet showing what each consultant has, what is missing, and their years of experience per skill.
- Every profile match stays visible to other managers, so the same search does not have to be run twice.
- Selected CVs are exported together in the bid template and language, and can be tagged for reuse in the next bid.
Why a Consultant Shortlist Takes So Long to Build
A technical annex usually lists precise requirements: a role, a minimum number of years, a set of technologies, sometimes a certification, an industry background and the languages the consultant must speak. Matching that list against a large consultant database by hand is slow, and it is easy to miss someone strong.
The manual route tends to look the same every time. Someone searches for a technology, opens CV after CV, estimates years of experience from project dates and builds a spreadsheet. Then the selected CVs still have to be converted into the format the bid requires.
Each of those steps repeats for every requirement and every bid, often by different people in the same company who cannot see each other’s work. The result is a consultant shortlist that took days to assemble and still leaves the team unsure whether it missed someone.
What slows down building a consultant shortlist for a bid?
Checking each consultant’s real experience against every requirement, then reformatting the selected CVs, all by hand.
Finding people who mention a technology is quick. Confirming who has the years, the certification, the industry background and the languages the client asked for takes far longer, and the work starts again with the next requirement. Converting the final selection into the bid template adds another round on top.
Starting a Consultant Shortlist From the Job Description
The workflow starts with the requirement itself. A bid manager can send the job description by email, or paste it into the platform and create the profile match there. Both routes lead to the same result, so each person can use whichever is quicker for them.
Within seconds, a profile match is created, listing what the role requires: total years of experience, years in the core technology, and the frameworks, databases and tools mentioned in the annex. AI reads the job description and pre-fills these requirements, which saves typing them in field by field.
The quality of the input matters. A ten-page PDF contains plenty of context that does not change who qualifies. Summarising the requirement first, and sending only what the role genuinely needs, gives the system a cleaner brief and produces a cleaner consultant shortlist.
Should we send the full tender annex or a summary of the role?
A summary. Pasting the specific requirement works better than forwarding a long PDF.
A little preparation before sending pays off. Cutting the annex down to the role, the years, the technologies, the certifications and the languages that decide selection makes the match easier to run and the results easier to read. A requirements table copied from a client document can be cleaned up into plain text first.
Setting the Requirements and Weights That Matter to the Client
Once the requirements are listed, the bid team decides what matters most. Skills the AI did not pick up can be added by hand, along with certifications, industry experience and languages. A role might call for five years in banking, an AWS certification, and both English and French.
Each requirement carries a weight on a simple points scale, and the score is recalculated with one click after any change.
| Points | How the bid team can use it |
|---|---|
| 0 | Requirement listed in the job description but irrelevant for this client, so it does not count |
| 1 | Nice to have: adds a little to the score without deciding the selection |
| 10 | Relevant requirement that should lift a consultant above others |
| 50 | Important requirement that strongly separates candidates |
| 100 | Essential requirement, such as minimum years in the core technology |
This is where the client’s priorities come in. When the client values one requirement more than the default weighting does, the weights can follow the client. The number of consultants to return, such as the top 100, and a minimum score are set before the search runs.
Reading the Results: What Each Consultant Has and What Is Missing
When the search finishes, an email notification arrives with a spreadsheet of the matching consultants. Each row shows the consultant’s category, location, nationality and contact details alongside the match score.
The score comes with its reasons. For each consultant, the results list which requirements they meet, which are missing, and how many years of experience they carry per skill. A strong technical profile based outside the required city, for example, shows the location as the missing requirement.
A report view adds statistics across the whole consultant shortlist: how many people have each skill, which certificates and languages appear, and when each profile was last updated. The shortlist can only include consultants whose CVs are in the system, which makes CV completeness across the group a bid issue as much as an HR one.
Why does the shortlist show what each consultant is missing?
So the team can see at once whether a gap rules someone out or only needs a quick check.
A missing requirement is sometimes a real gap and sometimes a CV that was never updated. Seeing the gap per consultant lets the bid team focus on the borderline cases and confirm details with the person directly, instead of discarding someone who may in fact qualify.
For those borderline cases, a short questionnaire link shared by email or Teams lets the consultant confirm whether they have the missing experience and where they used it, and their answers update the profile directly. The steps are covered in how to launch a questionnaire.
Narrowing the Consultant Shortlist to the People You Will Submit
From the full list, the team narrows down. Consultants who do not fit can be marked as not selected and filtered out, and results can be filtered by practice or service group, to see for instance whether the data or SAP practice has the right people.
Every profile match also stays visible to other managers in the company. A colleague picking up the same bid can open the existing results instead of running the request again, and the history of past requests remains available for the next similar opportunity.
The matching step itself is described in more detail in automate candidate shortlisting with Sprint CV Match, including how scores are paired with the skills that produced them.
Exporting Every Bid CV in the Right Template and Language
Once the selection is settled, every CV on the consultant shortlist can be exported at once, in the template the bid requires and in the language it asks for. The output arrives as a single zip file, ready for review.
Because the template is chosen at export time, the same consultant data serves any submission format the company has configured. A bid in Spanish and a bid in English can draw on the same profiles without anyone rewriting a CV by hand.
Companies that keep a set of CV templates for their main clients and frameworks can switch between them per bid without touching the underlying consultant profiles.
Tagging Shortlisted Consultants for the Next Bid
Shortlisted consultants can be tagged, for example with the name of the bid, and the tag becomes a reusable list. In a later profile match, everyone carrying that tag can be imported directly, so the profiles used in one process can be recycled for the next.
Tags also work with quick searches. A manager can search for something like ten years of Java, five of Angular, English and French, without creating a full profile match, tag the people found, and import them into a profile match when the bid becomes real.
Before running the next search, it is worth checking that:
- ✓The requirement is summarised to the role, years, technologies, certifications, industry and languages that decide selection
- ✓Skills the AI did not pick up have been added to the profile match by hand
- ✓Weights reflect what the client actually prioritises, from nice-to-have to essential
- ✓The number of consultants to return and the minimum score are set before the search runs
- ✓Consultants who clearly do not fit are marked as not selected
- ✓The export template and language match what the bid requires
- ✓Shortlisted consultants are tagged so the list can be reused in the next bid
This is the kind of workflow CV management for consulting companies is built around: one consultant database that bid, delivery and recruitment teams can all search, filter and reuse.
Frequently Asked Questions
How do you build a consultant shortlist from a job description?
Send the job description by email or paste it into the platform to create a profile match. AI pre-fills the requirements, the team adjusts skills and weights, and the search returns a ranked spreadsheet of matching consultants with what each one has and is missing.
Can the weights in the match score be changed?
Yes. Each requirement can be weighted at 0, 1, 10, 50 or 100 points, from irrelevant to essential, and the score is recalculated after the change.
Why does a consultant not appear in the shortlist?
Either their CV has not been uploaded to the database, they fall below the minimum score set for the search, or a requirement they meet is not recorded on their profile yet.
Can shortlisted CVs be exported in the client’s template and language?
Yes. Every selected CV can be exported at once in the chosen template and language, delivered as a single zip file.
Can a consultant shortlist be reused for a future bid?
Yes. Shortlisted consultants can be tagged, and everyone with that tag can be imported into a new profile match later. Other managers can also open existing profile matches instead of running the same search again.
Conclusion
A consultant shortlist is only as useful as the evidence behind it and the speed at which it reaches the submission. Starting from the job description, weighting what the client cares about and exporting the selection in the bid format turns a search spread across several people into one repeatable workflow.
The judgement stays with the bid team. What changes is how much of the day goes into searching and reformatting, and how much goes into deciding who to put forward.
Shortlisting consultants for your next bid?
See a job description become a weighted, ranked shortlist, with every selected CV exported in your bid template and language in one step.
About the Author
Three years of turning Sprint CV’s workflows into tutorials, videos, and guides people actually finish taught António Teixeira what separates a feature nobody adopts from one that sticks. He is part of the team that writes the guides, walkthroughs, video tutorials, and blog content that help recruitment and consulting teams get more out of the platform, translating every product update into content clients can act on immediately. Connect with António on LinkedIn
