Built for commercial finance, factoring, and asset-based lenders
The CRM that treats underwriting as the pipeline, not a step after it.
Commercial finance runs on broker intake, underwriting throughput, funding workflow, and portfolio monitoring. Generic sales CRMs fit the broker relationship but not the underwriting pipeline. Strkr ships a CRM shaped around commercial lending with deal workflow, underwriting queues, broker scoring, and AI drafting for term sheets and declines.
Commercial finance operations run on a different motion than B2B SaaS or professional services. The pipeline is deal flow from brokers and direct submissions. The time cost is underwriting throughput. The success metric is funded dollars at target risk-adjusted yield. The pain points below are what Strkr onboarding hears from factoring companies, asset-based lenders, equipment finance shops, and bridge lenders.
Deal intake is scattered
Email, broker portals, PDF applications, LinkedIn messages.
A commercial finance intake inbox pulls from broker email, broker portal uploads, direct website submissions, LinkedIn messages, and old-fashioned PDF applications. The intake coordinator reconciles the stream into a single pipeline, chases missing documents, and routes to the right underwriter. Strkr ships a Deal Intake surface that normalizes multi-channel inbound with document extraction and broker attribution.
Underwriting is the pipeline
The deal does not progress until the underwriter blesses it.
In B2B SaaS the pipeline is sales stages. In commercial finance the pipeline is underwriting states (intake, pre-screen, package complete, underwriting review, committee, approved, declined). Each stage has a target cycle time and a person responsible. Strkr ships a Deal Workflow with underwriting-shaped stages, cycle-time tracking, and SLA alerts.
Broker scoring is intuition
The managing partner knows which brokers are gold and which are noise.
A commercial finance shop takes submissions from 180 to 420 brokers. Some brokers send 40 submissions per year with a 24 percent fund rate. Others send 400 submissions per year with a 2 percent fund rate (and consume underwriting capacity). The managing partner knows which is which in their head but the data lives nowhere. Strkr ships a Broker Scoring object with submission volume, fund rate, average deal size, cycle-time contribution, and overall score.
Term sheet drafting is slow
A term sheet takes 90 minutes to draft and 30 minutes to review.
An underwriter drafts a term sheet from scratch for each approved deal with the pricing, structure, covenants, and conditions tuned to the deal. The drafting takes 90 minutes. Strkr AI drafts the term sheet from the deal structure, prior similar deals, and the underwriting file. The underwriter tunes two or three provisions before sending.
Decline letters feel rote
A thoughtful decline keeps the broker relationship warm.
A commercial finance shop declines 60 to 80 percent of submissions. The quality of the decline communication affects whether the broker sends the next deal. A fast, specific, warm decline keeps the broker engaged. A slow or generic decline burns the relationship. Strkr AI drafts the decline letter with specific decline reason, decision rationale, and warm language that invites future submissions.
Portfolio monitoring is sidecar
The funded book lives in a different system from the sales pipeline.
Once a deal funds the loan servicing lives in a different system (RapidAdvance, Equitable Lender, Validis, custom back office). The sales team cannot see borrower performance which is relevant for cross-sell and renewal. Strkr ships a Funded Loan object with servicing-side integration through the webhook spec that pulls payment history, aging, and modification status.
The commercial finance schema
Objects shaped like commercial lending.
The default CRM schema does not reflect commercial finance work. Strkr ships a tunable schema with Deal, Borrower, Broker, Underwriter Assignment, Credit Decision, Term Sheet, Funded Loan, and Covenant as first-class objects.
Deal object
Product, structure, request, status.
A Deal in Strkr carries product type (factoring, ABL, equipment finance, SBA, bridge, MCA), request amount, structure (invoice factoring, revolving line, term loan, equipment lease), broker attribution, submission date, and current workflow stage. The schema drives the underwriting queue and the cycle-time tracking.
Borrower object
Business, industry, tenure, financials.
A Borrower captures business name, industry (SIC/NAICS), tenure, revenue, EBITDA, prior-year financials, debt schedule, management team, ownership structure, and credit history. The schema feeds the underwriting workflow and the funded-loan servicing tie.
Broker object
Submission volume, fund rate, score.
A Broker captures submission volume by period, fund rate, average deal size, cycle-time contribution, commission structure, and overall broker score. The scoring runs across trailing-12-month data with configurable weights. Top-tier brokers get expedited intake. Bottom-tier brokers get a reduced-touch workflow.
Underwriter assignment object
Workload, product focus, pipeline.
An Underwriter Assignment captures the underwriter identity, product focus (factoring, ABL, equipment), current pipeline, workload capacity, and cycle-time performance. The schema drives the deal routing and the capacity planning. Managing partners see where underwriting capacity is tight and where there is slack.
Credit decision object
Decision, rationale, conditions.
A Credit Decision captures the decision (approve, approve with conditions, decline, counter-offer), rationale, specific conditions, approval authority (underwriter, senior, committee), decision date, and conditions-fulfillment status. The schema feeds the term sheet drafting and the decline letter workflow.
Term sheet object
Pricing, structure, covenants, conditions.
A Term Sheet captures the final pricing, structure, covenants, closing conditions, broker commission, and signed-date status. The schema integrates with the e-signature workflow and feeds the funded-loan creation on close.
Funded loan object
Servicing status, payment, modification.
A Funded Loan captures loan reference, original terms, current outstanding, payment history, aging status, modification history, and renewal date. The schema pulls servicing data through the webhook spec from the loan servicing system. The sales team sees borrower performance for cross-sell and renewal conversations.
How Strkr AI drafts for commercial finance
The AI jobs that fit lending work.
Commercial finance communication is specific, structural, and compliance-aware. Strkr AI drafts in the voice an underwriter or portfolio manager would use with retrieval scope strictly scoped to the deal file.
Term sheet draft
The pricing, structure, covenants in writing.
Strkr AI drafts the term sheet from the deal structure, pricing decision, covenant package, and prior similar deals. The underwriter tunes two or three provisions before sending. The 90-minute term sheet drafting drops to 20 minutes of effective work.
Decline letter draft
The warm, specific decline that keeps the broker.
Strkr AI drafts the decline letter with the decline reason, decision rationale, and warm language that invites future submissions. The decline cycle time drops from several days to same-day. Brokers continue submitting because the relationship stays warm.
Request-for-info chase
The missing document ask.
When an underwriting package is incomplete Strkr AI drafts the missing-document request with the deal reference, the items outstanding, and the completion target date. The intake coordinator approves and sends. Package completion cycle time drops because the ask is specific.
Broker scorecard
The quarterly performance narrative.
Strkr AI drafts the quarterly broker scorecard with submission volume, fund rate, average deal size, cycle-time contribution, and overall ranking. The managing partner reviews and sends to the top tier brokers as a relationship-building touch and to the bottom tier as a performance-expectation reset.
Underwriting memo
The internal credit memo.
Strkr AI drafts the internal credit memo with the deal summary, borrower analysis, credit history, financial analysis, structure recommendation, and risk assessment. The underwriter tunes and signs. The committee walks into the credit committee meeting with a consistent memo format grounded in the actual deal file.
Borrower status update
The post-funding communication.
Strkr AI drafts the monthly or quarterly borrower status update summarizing utilization, covenant compliance, upcoming renewal, and expansion opportunities. The portfolio manager approves and sends. Borrower relationships stay warm which drives renewal and expansion.
The deal flow cockpit
The view the chief credit officer opens each morning.
Commercial finance leaders run on daily deal-flow situational awareness. Strkr ships a deal flow cockpit that pulls intake volume, underwriting pipeline, pending decisions, and funded-loan portfolio onto a single view.
Intake snapshot
New submissions in the past 24 and 72 hours.
The intake section shows new submissions in the past 24 hours and the past 72 hours with broker attribution, product type, and request size. The intake coordinator works the queue to ensure every submission gets a pre-screen response inside 24 hours.
Underwriting pipeline
Deals by workflow stage.
The underwriting pipeline shows deals in each workflow stage (pre-screen, package complete, underwriting review, committee, pending close) with aging and underwriter assignment. The chief credit officer sees which deals are stuck and need escalation.
Committee queue
Deals awaiting committee sign-off.
The committee section shows deals pending credit committee with the underwriter memo, decision recommendation, and committee date. The committee works the queue each week with consistent format.
Funded portfolio
Portfolio status with aging.
The funded portfolio section shows the funded book with outstanding balance, aging distribution, covenant compliance, and modification status. The portfolio manager sees borrowers approaching renewal, borrowers in modification, and borrowers in covenant breach.
Broker scorecard
Top and bottom brokers by score.
The broker section shows the top 20 brokers by score and the bottom 20 by score with trailing-12-month submission volume and fund rate. The managing partner uses the view to deploy relationship-management time and to deprecate low-performing broker relationships.
Decline queue
Declines awaiting broker communication.
The decline queue shows deals declined and awaiting broker communication with the Strkr AI drafted decline letter ready for review. The intake coordinator works the queue same-day because the drafting cost is minimal.
Head-to-head
Strkr vs ClickUp, Monday, Salesforce Financial Services Cloud for commercial lending.
The comparison below is Strkr's commercial finance posture versus a typical generic CRM or workflow tool applied to the same shop.
Feature
Strkr
Generic CRM or workflow tool
Underwriting workflow stages
Lending-specific with SLA alerts.
Generic sales stages.
Broker scoring object
Volume, fund rate, cycle time.
Contact tag.
Multi-channel intake
Email, portal, PDF normalization.
Email only.
Term sheet workflow
AI draft from deal structure.
Word template.
Credit committee queue
First-class with memo workflow.
Manual.
Portfolio monitoring tie
Servicing webhook integration.
Separate system.
Decline letter workflow
AI draft with warm language.
Form letter.
Underwriter capacity planning
Workload and cycle-time view.
None.
Pricing
Flat per seat.
Per-deal or per-user with tier gates.
How teams use Strkr
Four commercial finance playbooks.
The playbooks below are patterns Strkr onboarding sees across commercial finance deployments.
Pattern A
The 24-person factoring company.
A 24-person factoring company serving mid-market trucking and distribution clients ran its deal intake in Gmail, underwriting in Excel, and funded portfolio in a legacy servicing system. The managing partner wanted the broker intake, underwriting, and portfolio on one surface. Strkr onboarding ran a 16-business-day migration with servicing stayed as the system of record. Deal cycle time dropped from 11 to 6 business days average and funded volume grew 32 percent in the first full year at flat headcount.
Pattern B
The 68-person asset-based lender.
A 68-person asset-based lender served mid-market borrowers with revolving credit facilities between $5M and $50M. The team ran Salesforce for broker relationships but had to work the underwriting in a separate Excel-based workflow. Strkr onboarding ran a 24-business-day migration consolidating broker relationships, underwriting workflow, and portfolio monitoring onto one surface. Underwriter productivity lifted 24 percent as context-switching between tools dropped.
Pattern C
The 42-person equipment finance shop.
A 42-person equipment finance shop served construction, transportation, and medical equipment buyers. Deal intake came from 240 equipment dealers and vendor finance programs. Strkr onboarding configured the Broker Scoring object specifically for equipment vendor finance programs and the Deal Workflow with equipment-specific documentation requirements. Dealer satisfaction scores lifted 18 points as decision cycle time dropped.
Pattern D
The 18-person bridge lender.
An 18-person bridge lender served real estate investors with short-duration bridge loans. Deal cycle time was the primary competitive advantage with target 48-hour decisions on well-packaged deals. Strkr onboarding configured the Deal Workflow with 48-hour SLA alerts and the AI drafting for term sheets and declines. The 48-hour commitment held clean. Deal volume grew 68 percent in the first full year.
Run commercial finance on a CRM shaped for lending work.
Book a 30 minute call. We will walk through the commercial finance schema, the underwriting workflow, and the broker scoring with your actual product lines and deal flow.
Does Strkr integrate with LeasePilot, Linedata, Finastra, or custom servicing systems?
Strkr integrates with every major loan servicing and leasing platform through its REST and GraphQL API, outbound webhooks with HMAC signing, and the published OpenAPI spec. For LeasePilot, Linedata, Finastra Loan IQ, Odessa, and custom back-office systems the integration pulls payment events, aging status, modification events, and covenant compliance on a webhook-driven cadence. The integration lands in two to four engineering sprints depending on complexity. The servicing system stays as the system of record for the funded loan.
How does Strkr handle credit committee workflow and sign-off?
The Credit Decision object captures the decision authority (underwriter, senior, committee), decision date, rationale, conditions, and sign-off trail. For committee-tier decisions the committee queue surfaces deals with the AI-drafted underwriting memo, the underwriter recommendation, and the committee date. Committee members review and vote through the Strkr interface with the full voting trail logged. The sign-off trail provides the audit record that regulatory examiners expect.
Can Strkr handle the broker commission tracking and payment workflow?
Yes. The Broker object captures commission structure (point split, flat fee, tiered) and the Deal object captures the broker commission on closed deals. The commission payment workflow runs through the accounting integration (QuickBooks, Sage Intacct, NetSuite) with payment events logged to the broker scorecard. The managing partner sees per-broker commission expense versus per-broker revenue contribution for relationship profitability analysis.
What does Strkr cost compared to Salesforce Financial Services Cloud, nCino, or BankWorks?
Strkr prices flat per seat at $29 Starter, $59 Pro, and $119 Scale per user per month with a 14-day trial. For a 42-person commercial finance shop on Scale the monthly spend runs $4,998 for seats plus $49 for AI Enhancement. Salesforce Financial Services Cloud typically runs $150 to $300 per user per month plus implementation partner retainer. nCino runs similar enterprise pricing. BankWorks runs enterprise pricing with heavy customization engagement. Strkr sits dramatically lower on cost while adding the AI drafting for term sheets and declines.
How does Strkr handle compliance for commercial finance regulators?
Commercial finance regulatory posture varies by product. For state-licensed products (factoring in some states, consumer-side MCA, bridge lending in some jurisdictions) Strkr's audit log captures every record read, record write, and AI call with retention tunable to state requirements. For bank-regulated products the SOC 2 Type II posture (target 2026-Q4) supports the vendor-management expectations of regulated bank buyers. Integration tokens live in AWS KMS. The admin configures retention policies per product line to match the applicable regulatory requirement.
Can Strkr handle multiple product lines (factoring, ABL, equipment, SBA) in one tenant?
Yes. The admin configures product lines (factoring, ABL, equipment finance, SBA, bridge, MCA) with product-specific deal workflow stages, documentation requirements, pricing templates, and term sheet templates. An underwriter working across product lines sees product-specific pipelines with cross-product reporting rolling up to the chief credit officer. The flexibility supports shops that run multiple product lines under one roof.
What is the full compliance and security posture for commercial finance?
Strkr is HIPAA compliant with BAA on request (relevant for medical-practice-serving commercial finance), GDPR and CCPA compliant today, and SOC 2 Type II is in progress with a target of 2026-Q4. Integration tokens live in AWS KMS, platform credentials live in AWS Secrets Manager. The audit log captures every record read, record write, and AI call with 90-day default retention tunable per tenant to the applicable state licensing authority retention ceiling. SSO (SAML and OIDC) is live on Scale tier through Clerk, MFA with TOTP plus text-based verification is live across all tiers, and Verified Domains are live on Scale tier.
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