The Presales Velocity Framework 🌟 Why Speed in Writing is the Wrong Metric to Track

The Metrics Presales Teams Get Wrong

We see presales teams obsess over their RFP response velocity all the time. But the presales teams that are more obsessed with pipeline and revenue generation are the ones measuring whether an RFP response moves a deal to Closed Won, not how fast they wrote a first draft.

Presales velocity is the rate at which a presales or proposal team turns a qualified opportunity into a complete, reviewed, win-ready response that moves the deal forward. It is measured across response speed, accuracy, reviewer load, and pipeline impact. A team can score high on speed and still have low velocity if reviewer load or accuracy drags the deal.

This is not just a stargazy analyst's contrarian pose, either. It is also the view of the people building the speed tools. Manisha Raisinghani, co-founder and CEO of SiftHub, said, "The teams worth copying are the ones who track win rate and pipeline progression as their AI metrics, not response speed."

When the founder of a platform built to produce sub-10-minute drafts tells you the draft time is not the scoreboard, the claim is hard to wave away. She reached this conclusion, partly by interviewing more than 200 sales and presales leaders before building an AI-native product.

So if you're a presales team that is optimizing for fast drafting, you'll have great productivity metrics, but what does that matter when your real focus should be about pipeline velocity and Closed Won revenue?

What is Presales Velocity?

Velocity has four, measurable dimensions:

Response speed

Response speed is the time from a qualified opportunity to a first complete, reviewable draft. This is the dimension every revenue software, even though it's the least useful in isolation; speed only matters if you're doing well on the other dimensions.

Accuracy

Accuracy is how much of a draft survives review without rework. A response generated in ten minutes that needs forty hours of correction hasn't saved you a lot of time. It simply moved the work downstream into reviewing and editing instead of initial drafting.

Accuracy is also about trust, not just correctness. In Raisinghani's interviews, the fear that came up consistently about using an AI-native tool to draft a proposal was not job loss, like you'd assume. The consistent fear was being the person blamed when an AI-generated answer hallucinated something false in front of a Fortune 500 evaluation committee. A reviewer who does not trust the draft will re-check every line, which erases the speed gain entirely. Accuracy, properly measured, is the share of content a reviewer is willing to ship without having to do multiple verification checks and rewrites.

Reviewer load

Reviewer load is when subject-matter experts and bid reviewers have a fixed capacity - which we all do. Double the volume of drafts flowing toward them and you have not increased velocity. Reviewer load measures the human review time each response consumes, and on a high-volume desk, you will run out of time.

Pipeline impact

Pipeline impact is whether faster, more accurate responses help you with shorter sales cycles, higher progression/shortlist rates, and higher win rates (aka. higher revenue!). This is the only dimension the board cares about, and so it should be how you measure your proposal software tools. A team can be writing faster and far more proposals and still show no pipeline impact if it is responding faster to deals it was never going to win.Why Orchestration in Presales and Proposal Teams is a New Winner

It's easy to fall into the trap of believing that automation means faster responses!

Raisinghani describes AI as not removing the subject-matter expert from the process, but it does, in many ways, change their job from author to reviewer. The expert who used to write answers now checks them. So if the first draft response needs a total re-verification and re-write, you've just moved where their time is spent on a proposal, not reduced it.

So what separates a velocity gain from a faster queue is how the relocated work gets managed. SiftHub's approach is a useful illustration to understand this new way of working and thinking about proposal orchestration. Rather than handing a reviewer 500 machine-generated answers and a percentage confidence score, the system flags answers red and yellow, directing attention to the specific answers on a 500-question RFP that need more urgent and deeper human decision and support.

A team with disciplined ownership and a clean review process will get more out of mediocre software than a chaotic team will get out of the best platform on the market.

Not all Sub-10-minute Drafts are Created Equal

The sub-10-minute first draft has become the headline number for proposal and deal orchestration software. SiftHub cites it; others cite versions of it. It is a real benchmark and a useful one, as long as you read it with its conditions attached, and not all 10-minute drafts are created equal!

A sub-10-minute draft is a complete first pass of a structured response, produced against a content library that is already populated, curated, and trusted. It measures how fast a well-built system assembles a first draft. It does not measure final quality, reviewer sign-off, or win probability. A team starting with a thin or stale content library will not see this number on day one, and that gap is 100% a content-readiness problem.

Velocity in Practice

Two customer signals show what happens when orchestration accompanies speed rather than replacing it.

Allego, the sales enablement company, reported completing RFPs roughly 8x faster after adopting an AI-native response platform, filling about 90% of questions automatically.

Sirion reported handling around 1.5x the volume with the same team, moving to roughly 40 to 45 RFPs a month while cutting 48 hours from its response SLAs.

The Sirion result matters because the volume rose and turnaround fell at the same time, which is what all four dimensions moving together look like.

In each case, the software gain came from pairing that with clear ownership and a defined review process.

The pattern holds across the customer evidence in stargazy's 2026 Proposal & Bid Software Report, too, where teams that automated drafting without fixing review tended to draft faster and ship at the same pace, because the constraint moved to review rather than disappearing. The multiple a team captures depends less on which platform it buys than on whether it fixed its weakest dimension first.

Where reference implementations fit

Tools matter once you know which dimension to fix. A platform that addresses response speed and reduces reviewer load, the way SiftHub's AI RFP software is designed, is solving two of the four dimensions at once, which is why it shows up in this analysis. The test for any platform is the same: score it against the four dimensions, not against its fastest headline number. A vendor that improves response speed, accuracy, reviewer load, and pipeline impact together is making a stronger claim than one that can only point to a quick first draft.

That is the question worth taking into any evaluation. Not how fast does it draft, but which of my four dimensions does it actually move, and is that the one capping my velocity today.

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Frequently asked questions

What is presales velocity?

Presales velocity is the rate at which a presales or proposal team turns a qualified opportunity into a complete, reviewed, win-ready response that moves the deal forward. It is measured across four dimensions: response speed, accuracy, reviewer load, and pipeline impact.

How is presales velocity different from RFP turnaround time?

RFP turnaround time measures only how fast a response gets produced. Presales velocity includes turnaround but adds accuracy, reviewer load, and whether the response actually moves the deal. A team can have fast turnaround and low velocity if reviewers are overloaded or the wins do not follow.

Why doesn't automation improve presales velocity on its own?

Automation speeds up drafting, which is one of four dimensions. If review, ownership, and process do not keep pace, the constraint relocates to review rather than disappearing. The team drafts faster and ships at the same rate.

How do you measure presales velocity?

Score the team one to five on each of the four dimensions: response speed, accuracy, reviewer load, and pipeline impact. The lowest score is the velocity ceiling and shows where the next investment should go.

What is a realistic first-draft benchmark for an RFP?

Leading AI-native platforms cite a sub-10-minute first complete draft, but only when the content library is already populated, curated, and trusted. Teams with thin or stale libraries should expect longer until the library is in order, because the constraint there is content readiness, not software.

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Sources

• stargazy, 2026 Proposal & Bid Software Report, Section 6 (implementation benchmarks)

• The stargazy Brief, episode with Manisha Raisinghani, co-founder and CEO, SiftHub.

• SiftHub, Allego customer story (8x faster RFP completion, ~90% autofill). https://www.sifthub.io/customer-stories/allego

• SiftHub, Sirion customer story (1.5x RFP volume, 48-hour SLA reduction)