The same mess a solar or home-services operator lives every day: leads, financing, install status, commissions, and reporting sitting in systems that don't agree, and no number you'd bet a decision on. This is what it looks like when one system finally ties them together.
Here, that system ran CSL Capital's entire lead-to-decision pipeline: automated ingestion, scoring, ML risk assessment, a proprietary loan-status system, automated reporting, and a live dashboard. Nektar built it as one connected system, end to end, on a governed warehouse that fails its own build if a number stops tying out.
CSL Capital, a private-credit fund making merchant-cash-advance and specialty-finance investments, was running its entire operation on data that couldn't keep up with the decisions riding on it.
None of this was a judgment problem. The team knew the business. What they didn't have was one connected system that captured a deal, graded it, tracked it, and reported on it the same way every time.
Not six tools stitched together after the fact. Six stages designed to hand cleanly to the next. A deal that enters at Stage 1 flows through scoring, risk, status tracking, and reporting without a single manual re-entry point.
Deal emails from funding partners are read, extracted, and written into a structured record automatically. No one re-keys a deal by hand. Every incoming deal is captured the same way, every time.
A hard-fail gate plus a multi-point scoring rubric grades every deal that lands. The same rules apply whether the deal arrived at 9am or midnight. No exceptions made on memory or gut feel.
Deals that clear scoring feed an ML-based risk/underwriting model, retrained on a regular cadence. This is what turns "this looks like a good deal" into a modeled, auditable signal.
Not a repurposed CRM stage field. A purpose-built status taxonomy and determination logic designed for how this specific loan book behaves, with every status change audited and reversible only through a governed override path.
Executive, portfolio, and fund-level reports are generated and delivered on a schedule. No manual assembly step, no version where a number drifts from what's in the warehouse.
The governed decision surface leadership actually runs the fund on, pulled live from the same warehouse every other stage in this pipeline reads from and writes to.
Stage 4 is the piece that couldn't have been bought off a shelf. Nektar designed the status taxonomy and its determination logic specifically for how this loan book behaves (which statuses are terminal, which are watchlist-worthy, which require escalation) and made every change to it auditable. It's the single source of truth for "where does this deal actually stand," and it's differentiated IP, not a checkbox in someone else's CRM.
All six stages sit on top of the same governed warehouse. The standard here wasn't "does the dashboard look good." It was "would this number survive an audit."
Every number that reaches any of the six stages (a deal score, a risk signal, a status change, a report, a dashboard figure) first passes through this layer. That's not a marketing number. It's the literal count of transformation steps and automated checks standing between raw source data and anything a decision-maker reads.
| Layer | Before | After |
|---|---|---|
| Lead ingestion | Analyst hand-keys deal terms from email into the CRM | Automated extraction pipeline, no manual re-entry |
| Deal scoring | Judgment-based, no consistent rubric | Hard-fail gate + scoring rubric, applied identically every time |
| Risk assessment | Sized on institutional memory of past deals | ML risk model, retrained on a cadence |
| Loan status | Whatever field was closest, no audited definition | Proprietary taxonomy, single source of truth, audited overrides |
| Collections accounting | Reversed/voided payments never ingested, overstated collections | Dedicated reversal tracking nets every reversal before a number is reported |
| Partner reconciliation | Each portal reconciled by hand, ad hoc cadence | Automated reconciler, shared identifier, scheduled tie-out |
| Reporting layer | Hand-written queries, no test coverage | 76 tested dbt models, 109 tie-out tests |
| Recurring reports | Manually rebuilt weekly/monthly | Automated, scheduled, no manual rebuild |
| Decision surface | Periodically rebuilt spreadsheet | Live dashboard, same governed warehouse |
Every model is tested. Every partner tie-out is automated. Every deal that enters the pipeline is captured, scored, risk-assessed, status-tracked, reported, and surfaced on a dashboard, through one connected system, not six separate tools someone has to manually stitch together.
That's the same rigor available to any operator running a sales, install, or lending business across systems that don't agree with each other. The stakes are different, but the discipline is identical: capture once, score consistently, track status honestly, report automatically, and never make a decision-maker guess which number is right.
This is the same discipline behind the Single Source of Truth Build. I build the reconciliation layer underneath your CRM, sales tools, and financing systems, so leadership runs on one governed set of numbers instead of four disagreeing ones. Start with a 2-week paid diagnostic that shows you exactly where your numbers disagree.
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