Services
Sales and pipeline enrichment
Score the pipeline you have; surface the deals you cannot see. Sales and pipeline enrichment scores every lead and opportunity on behaviour and fit, so your revenue team spends its time on the ones most likely to close, and it surfaces the deals across your wider market that never reached the pipeline on their own. Both live inside the CRM your revenue team already runs, not in a separate report. This is scoring and deal-sourcing intelligence wired into the workflow, not a data-append service that bolts extra fields onto a contact record. A score only earns its keep when a salesperson acts on it, so the integration is the deliverable.
The process we optimise
The process we optimise: how revenue teams decide which leads to chase, and which deals exist to chase at all
Underneath most sales work sit two commercial decisions: which of the leads in front of the team are worth the effort, and which deals exist in the wider market to pursue in the first place. Made well, attention goes to the leads most likely to close and the team can see opportunities that never walked through the front door. Made badly, everyone gets the same outreach, prioritisation runs on gut feel, and the market is judged as it looked last quarter. We optimise both decisions: we score leads and opportunities on how they actually behave and how well they fit, we surface the deals across your addressable market that the pipeline cannot see, and we make sure both land in the CRM your revenue team already sells from. This is scoring and deal-sourcing intelligence, not a data-append service that bolts extra fields onto a contact record.
Before and after
What changes when the system is rebuilt
Before: blanket outreach and gut-feel prioritisation
- Outreach goes out blanket, with the same effort spent on a lead ready to buy and one who will never respond
- Leads are prioritised on gut feel and whoever shouted loudest, not on how they behave or how well they fit
- The market is seen as it moved last quarter, so deals that formed since go unnoticed until a competitor has them
- Whatever scoring exists sits in a spreadsheet, disconnected from the CRM the team actually sells from
After: every lead scored, the whole market visible, in the CRM
- Every lead is scored on behaviour and fit, so the revenue team spends its time on the ones most likely to close
- The whole addressable market is visible, including the deals that never reached the pipeline on their own
- Scores and sourced deals live in the CRM the team already runs, at the point they decide who to call
- Prioritisation is argued from evidence, with a clear reason each lead and deal sits where it does
How we think about it
We start from the objective, then work outwards
The same discipline runs through every engagement: understand what the process is for, then design the system to serve it and measure against it.
Agree the commercial decision the scores serve
We begin with the objective: what the revenue team is actually trying to decide, which leads to chase now and which deals to pursue across the market, and how they intend to act on the answer. We agree the activation up front, which CRM the scores feed and where in the sales motion they surface, so we are building intelligence someone will act on rather than an interesting but inert analysis.
Map how leads and deals are prioritised today, honestly
We look at how prioritisation is currently done, whether that is blanket outreach or a gut-feel ranking nobody has tested. We trace where the behavioural and firmographic signal lives, how clean it is, how much of the addressable market the team can actually see, and how they move from a lead to a call today. This shows the real gap between the prioritisation you do now and the prioritisation the data would support.
Build and validate the scoring with the revenue team who will act on it
We build the behavioural and fit features first, then model the lead and opportunity scoring and the deal sourcing that surfaces the wider market, and we validate them with the salespeople who will act on them. A score has to be recognisable and trusted by the person picking up the phone, not just statistically tidy, so their judgement shapes the model rather than receiving it at the end.
Activate into the CRM and measure against the decision
We wire the scores and sourced deals into the CRM the team already runs, at the point they decide who to call, and hand it over. Then we measure against the objective from step one: is the revenue team now prioritising on evidence, and is the commercial decision the scores were built to serve being made better than it was? A score nobody acts on changes nothing, so the integration is the deliverable, not an afterthought.
An engagement, step by step
The following is a representative six to ten week arc, not an account of a specific client. It shows the order sales and pipeline enrichment work typically runs in, activation planning first and activation delivery last.
- Weeks 1 to 3
Objective and activation planning
We agree the commercial decision the scores have to serve, which leads to chase and which deals to pursue, and confirm the CRM and the point in the sales motion the scores will feed. Planning activation first keeps the whole build pointed at something the revenue team can act on.
- Weeks 3 to 5
Signal and market mapping
We assemble the behavioural and fit signals the scoring will rest on from your data, and we map the addressable market the deal sourcing will draw from. This is where blanket outreach and a partial view of the market are replaced with signal about how leads behave and where the unseen deals are.
- Weeks 5 to 8
Scoring, sourcing and validation with the team
We model the lead and opportunity scoring, surface the sourced deals across the wider market, and validate both with the salespeople who will use them, checking each score is recognisable and each deal is worth a call. Their feedback reshapes the model, so the intelligence is trusted rather than argued with.
- Weeks 8 to 10
Activation into the CRM and measurement
We wire the scores and sourced deals into the CRM the team already runs, hand it over, and set up measurement so uplift is visible. The revenue team ends the engagement prioritising on evidence, in the tool they already use, not reading a report on the side.
The engagement is judged on the objective set in week one: whether the revenue team now prioritises on evidence, in the CRM they already use. QuantSpark has documented the pattern in anonymised work: generative AI lead scoring wired into a revenue team's workflow, and a deal-sourcing platform that gave a private-equity fund a view across its whole market for the first time.
What you get
- Lead and opportunity scoring on behaviour and fit, wired into your CRM
- Deal sourcing that surfaces opportunities across your wider market, not just the pipeline you already see
- Scores and sourced deals living in the CRM your revenue team runs, not a separate report
- Validation with the revenue team who will act on the scores, and uplift measurement after go-live
How it typically runs
Typical engagement: 6 to 10 weeks, 1 to 2 engineers, fixed-scope pilot.
Diagnose
Weeks 1 to 3
Prototype
Weeks 3 to 5
Build and score
Weeks 5 to 8
Activate into the CRM and hand over
Weeks 8 to 10
Indicative timeline. Every project is scoped individually: book a discovery call and we will provide a detailed proposal within 48 hours.
Where it fits
This practice is delivered through both QuantSpark engines, depending on whether the answer is rollout or build.
Model-neutral by default
We evaluate Claude, GPT and open-weights models head to head for every problem, and recommend whichever wins on cost, latency and accuracy.
Proof
Sales and pipeline enrichment in the field
We are still tagging the library by practice. In the meantime, the full body of documented work is one click away.
Explore all our work