Built for Salesforce-shaped stacks

AI lead score value extraction from Salesforce without the Agentforce credit math.

Teams on a Salesforce base often have Einstein scoring enabled but cannot extract clear value from it. Credits meter the ask, scoring opacity hides the weights, and the AI reads a slice of the data. Strkr pairs with or replaces the Salesforce scoring layer so lead scoring produces explainable, cited value on a flat line.

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What this audience is actually dealing with

The pains that bring buyers here.

Teams running Salesforce with Einstein Lead Scoring enabled share a set of frustrations in 2026. The scoring works technically; the value extraction is harder. Six symptoms we hear every week from Salesforce admins trying to extract real value from the Einstein layer:

Credit math on every ask

Agentforce credits ration the questions the team can ask.

Salesforce Agentforce is priced per Flex credit at roughly 10 cents per conversation unit. A sales team asking natural-language questions about the scored pipeline burns credits fast. Strkr includes the ask surface on a flat seat line so the team queries freely without an invoice overrun.

Score opacity

Einstein scored this lead 82; nobody can tell why.

Einstein Lead Scoring returns a number, a stage, and a confidence. The admin can view the top contributing features but the signal weights behind the score are not published in a way the SDR can interpret in-flow. Strkr publishes signal weights and hover-explains the score on every chip.

Data slice

Einstein reads what you fed it; the signals outside are invisible.

Einstein scoring reads the fields the admin configured. Signals outside the Salesforce schema (GitHub public activity, hiring postings, conference-speaker rosters, Backstage mentions, intent providers) require manual enrichment or additional product licenses. Strkr pulls these externally and fuses with Salesforce data in one score.

Pardot-handoff drift

Pardot score and Einstein score disagree on the same lead.

A Pardot score for marketing and an Einstein score for sales produce two numbers for the same lead. The handoff conversation becomes an argument. Strkr fuses marketing fit, intent, and sales qualification into one Strkr Score so the handoff stops being a resolution exercise.

Edition gating

The scoring feature you want is on a higher edition.

Einstein Lead Scoring is on Enterprise and above; Einstein Opportunity Insights is on higher editions; Agentforce credits are a separate commercial line. The feature set you need often requires an edition upgrade. Strkr ships the full scoring stack on every paid tier.

Admin complexity

Tuning the model takes a certified admin and a few weeks.

Einstein model tuning requires training data preparation, feature review, and model retraining in a sequence. The admin usually asks a certified consultant. Strkr's weight-tuning console is admin-friendly: adjust weights, test on a cohort, roll back if the reply rate drops. The feedback loop is hours instead of weeks.

What Strkr AI does for Salesforce value extraction

The jobs that unlock scoring value on a Salesforce base.

Teams on Salesforce usually keep the base and augment the scoring layer, or migrate the lead layer to Strkr and sync. Both motions work. The cards below are the AI jobs that produce clear scoring value either way.

Salesforce data ingestion

Pull leads, contacts, accounts, activities from Salesforce.

Strkr integrates with Salesforce via REST + GraphQL API, outbound webhooks, HMAC signing, OpenAPI spec. Leads, contacts, accounts, activities, and opportunity history pull on a configured cadence. The data model maps cleanly; Salesforce field names flow through.

External signal fusion

GitHub, hiring, conference, intent fused with Salesforce fit.

Strkr pulls external signals (GitHub public activity, hiring postings, conference-speaker rosters, Backstage mentions, intent providers) and fuses them with Salesforce firmographic fit and sales qualification into one Strkr Score. The combined score is explainable and cited.

Explainable score

Hover-explain on every chip.

Every Strkr Score chip hovers to show which signals contributed what. The SDR sees '92 because: Salesforce company fit 95, tech stack 100 match (GitHub signal), 2 recent SRE hires, intent spike 11 days ago' instead of a black-box number.

Natural-language ask

Ask the pipeline without an Agentforce credit meter.

The top-right ask box answers natural-language questions over the fused Salesforce + Strkr data with records cited inline. 'Which Salesforce leads from Q3 with Backstage signals have no touch in 14 days?' returns a cited table. No per-question credit meter.

Weight tuning console

Admin-friendly, hours not weeks.

The weight-tuning console lets the admin adjust signal weights, test on a cohort, roll back if the reply rate drops. The feedback loop is hours instead of the weeks required for Einstein model retraining. The weights are published and versioned.

Account-level roll-up

Buying-committee readiness over Salesforce accounts.

Strkr rolls up contact-level signals (from Salesforce + external) to compute account-level buying-committee readiness. For enterprise motions this is often the single most useful score. The roll-up persists back to Salesforce via API so the Salesforce user sees it in-platform.

Pardot and marketing fusion

One score across marketing and sales.

Strkr fuses Pardot marketing-fit scores with sales qualification and external intent into one Strkr Score. Handoff disputes between marketing and sales resolve on a single number both sides can see and tune.

Audit log

Every scoring call captured for compliance.

The audit log captures every Strkr scoring call with prompt, response, model, user, latency per call. Teams running Salesforce in regulated verticals get a cleaner audit answer here than attempting the same with Einstein, which does not retain per-call replay.

Bidirectional sync

Writes back to Salesforce on configurable rules.

The admin configures which Strkr fields write back to Salesforce. The Strkr Score, the account-level roll-up, the signal-contribution breakdown, and the suggested next move can all persist back to Salesforce so users in the Salesforce UI see Strkr's value without switching tools.

What Salesforce value-extraction buyers evaluate on

The real checklist.

Teams extracting lead-score value from Salesforce evaluate specific criteria that generic scoring vendors miss.

Salesforce integration depth

API coverage, write-back, bidirectional sync.

Strkr integrates via REST + GraphQL API, outbound webhooks, HMAC signing, OpenAPI spec. Bidirectional sync is first-class. The buyer should ask every vendor what fields and objects they read and write back.

External signal breadth

GitHub, hiring, conference, intent indexed.

Strkr indexes external signal sources that Einstein does not. The buyer whose ICP depends on signals outside the Salesforce schema cares a lot about this.

Score explainability

Published weights or black box.

Strkr publishes signal weights and hover-explains the score. Einstein shows top features but the full weight model is not surfaced in-flow.

Pricing structure

Flat seat or Agentforce credit meter.

Strkr includes the ask surface on the flat seat line. Agentforce credits meter per conversation unit. For a team querying the pipeline daily, the pricing math usually favors Strkr by several thousand dollars a month at scale.

Admin feedback loop

Hours or weeks to tune weights.

Strkr: adjust, test, roll back in hours. Einstein: training data prep, feature review, model retrain in weeks. The feedback loop difference compounds.

Audit depth

Per-call scoring audit or platform-level.

Strkr retains per-call scoring audit. Einstein offers model-level governance but not per-call replay. For regulated verticals this gap matters.

What Strkr AI will not do here

the limits of Salesforce value extraction.

Strkr augments or replaces the Salesforce scoring layer. The limits buyers should know:

Not a Salesforce replacement at Enterprise scale

Strkr can replace or augment; the choice depends on scale.

For mid-market teams running Salesforce with Einstein, Strkr often replaces the whole CRM and scoring layer. For large Enterprise teams with heavy Apex, custom objects, and 400+ user seats, Strkr usually pairs with Salesforce and augments the scoring layer rather than replacing the base. The decision is a sizing exercise during pilot.

No Salesforce Apex execution

Apex and Flow stay in Salesforce.

Strkr integrates with Salesforce; it does not execute Apex code or Salesforce Flow logic. Teams that have heavy Apex business logic keep the logic in Salesforce and let Strkr handle the scoring and AI augmentation layer.

No autonomous outbound

The AI drafts; a human sends.

First-contact outbound requires a human click. Nurture steps pre-approved during setup can send without a per-message click.

Non-English quality lag

English, Spanish, German, French strong; others lag.

Strkr AI drafts and parses signals well in English, Spanish, German, French. Other languages work but quality lags for industry vocabulary.

Model-vendor neutrality

The router picks per task; the mix shifts.

Buyers who need to pin a model for a procurement memo can request the current routing policy on request.

No private intelligence

Public signals plus licensed providers only.

Strkr reads public signals and licensed provider data. We do not source non-public contract or budget information.

Head-to-head

Strkr vs Salesforce Einstein for scoring value extraction.

Einstein Lead Scoring plus Agentforce is the comparison teams on Salesforce run first when evaluating scoring augmentation. Head-to-head:

Feature Strkr Salesforce (Einstein Lead Scoring + Agentforce)
External signal fusion GitHub, hiring, conference, intent fused with Salesforce data Fields configured by admin; external via Data Cloud
Score explainability Published weights; hover-explain on every chip Top features surfaced; full weights not in-flow
Pricing basis Flat seat; ask surface included below plan cap Agentforce Flex credits per conversation unit
Admin feedback loop Adjust, test, roll back in hours Training data prep plus model retrain in weeks
Account-level roll-up Buying-committee readiness score Einstein Opportunity Insights on higher edition
Pardot fusion One Strkr Score across marketing + sales Pardot score + Einstein lead score separate
Natural-language ask Included; records cited Agentforce; credit-metered
Edition gating Full stack on every paid tier Features gated by edition
Audit depth per call Prompt, response, model, user, latency captured Model-level governance; no per-call replay
Compliance posture HIPAA with BAA available, SOC 2 Type II in progress through 2026-Q4 HIPAA via Shield on higher edition
How teams use Strkr

How Salesforce teams extract scoring value with Strkr today.

Five live customer motions where Strkr augmented or replaced the Salesforce scoring layer:

Mid-market SaaS, 40 reps on Salesforce Enterprise

Replaced Einstein scoring; kept Salesforce as base.

A 40-rep mid-market SaaS team on Salesforce Enterprise kept Salesforce as the base and replaced Einstein scoring with Strkr's scoring layer. External signals (GitHub, hiring, intent) fused with Salesforce firmographic data into one explainable score. Agentforce was turned off and the Agentforce credit line disappeared from the Salesforce bill. Monthly savings roughly 1,800 dollars; reply rate on scored outreach rose 28 percent on the same lead pool.

Enterprise, 180 reps on Salesforce Unlimited

Paired Strkr for signal augmentation; kept Salesforce for base.

A 180-rep Enterprise team on Salesforce Unlimited kept the whole Salesforce stack but augmented with Strkr for signal fusion. External signals pulled in Strkr, scoring computed, result written back to Salesforce via API. Users saw augmented scores in their native Salesforce UI; admins tuned weights in Strkr's console. The scoring layer became explainable and the Agentforce credit line stayed capped.

B2B SaaS, 24 reps on Salesforce Professional + Pardot

Fused Pardot score and Salesforce qualification into one number.

A 24-rep SaaS on Salesforce Professional + Pardot had chronic handoff disputes between marketing (Pardot score) and sales (Salesforce qualification). Strkr fused both into one Strkr Score. The handoff conversation stopped being an argument. Marketing-sourced pipeline conversion rose 22 percent in the next quarter.

FinServ, 60 reps on Salesforce Financial Services Cloud

Audit depth cleared Shield-equivalent compliance review.

A 60-rep financial services firm on Salesforce FSC needed per-call AI audit depth that Einstein did not provide without Shield at a higher edition. Strkr's per-call audit log cleared the compliance review in one cycle. The team saved roughly 40K a year on the Shield upgrade cost by using Strkr for the AI audit layer.

Devtools vendor, 18 reps on Salesforce Enterprise

GitHub signal fusion caught 14 whale accounts Einstein missed.

An 18-rep devtools vendor on Salesforce Enterprise had been scoring on firmographic fit alone. Strkr fused GitHub public activity, hiring, and conference-speaker signals with the Salesforce data. 14 whale accounts that Einstein had scored below the ICP threshold rose to top-tier Strkr Scores. 6 of 14 booked discovery; 3 closed in two quarters.

Extract lead-score value from Salesforce on a flat seat line.

Start a 14-day trial. Pull Salesforce leads, contacts, accounts; fuse with external signals; score explainably; ask natural-language questions; write back on configurable rules. One flat per-seat line, no Agentforce credit meter.

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Common questions

What buyers in this bucket ask most.

Can Strkr replace Einstein Lead Scoring on a Salesforce base?

Yes. The pattern is: keep Salesforce as the base system of record; integrate Strkr via REST + GraphQL API, outbound webhooks, HMAC signing, OpenAPI spec; pull leads, contacts, accounts, activities from Salesforce; fuse with external signals (GitHub, hiring, conference, intent) in Strkr; compute Strkr Score; write back to Salesforce fields on configurable rules. Users in the Salesforce UI see the Strkr Score natively; admins tune weights in Strkr's console. The Agentforce credit line usually drops significantly, which pays for Strkr several times over.

Does Strkr replace all of Salesforce or just the scoring layer?

Depends on scale. For mid-market teams under 150 Salesforce seats, Strkr often replaces the whole CRM and scoring layer in a planned migration. For large Enterprise teams with 400+ seats, heavy Apex customization, and multi-year contract commitments, Strkr usually pairs with Salesforce and augments the scoring layer rather than replacing the base. We publish a sizing framework and run pilot in both modes; the right call surfaces during the pilot.

How does Strkr handle Pardot marketing scoring alongside Salesforce sales qualification?

Strkr fuses marketing fit (from Pardot and other marketing signals), third-party intent (from Bombora, 6sense, G2 if licensed), and sales qualification (from Salesforce BANT or MEDDIC fields) into one Strkr Score 0 to 100. The weights are admin-tunable and the signal contribution is visible on hover. Handoff conversations between marketing and sales teams stop being disputes about whose score is right and become discussions about which signal is telling the team to act. Pardot sync is bidirectional via API.

Does Salesforce data stay in Salesforce or move to Strkr?

Both options supported. The default pattern pulls Salesforce data into Strkr for scoring, enrichment, and signal fusion, then writes computed fields back to Salesforce. Teams can configure read-only mode where no data writes back to Salesforce; they can also configure Strkr-only mode where data moves fully to Strkr. For regulated-industry motions that need Salesforce as the system of record, the default pull-and-write-back pattern keeps Salesforce authoritative while letting Strkr handle the AI augmentation.

What is the Agentforce credit math on a typical Salesforce deployment?

Agentforce is priced per Flex credit at roughly 10 cents per conversation unit. A 40-rep sales team asking natural-language questions daily tends to burn 30 to 60 dollars per rep per month, which lands at 1,200 to 2,400 dollars monthly on the Flex line. Strkr includes the ask surface on the flat seat line with no per-conversation credit meter below the plan cap, so the ask-surface line disappears from the invoice. The economics work because the three-tier model router keeps per-call unit cost low enough to offer this sustainably.

Is Strkr safe for a regulated-industry Salesforce augmentation?

Yes. HIPAA compliant with BAA available for healthcare and healthtech Salesforce FSC motions. SOC 2 Type II in progress through 2026-Q4. GDPR and CCPA posture clean. Every AI scoring call runs under user claims so RLS propagates. The per-call audit log satisfies compliance reviews that Einstein without Shield cannot. Procurement at regulated firms often clears Strkr for AI augmentation faster than clearing Shield for similar audit depth.

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