Built for RevOps leaders and systems architects designing the sales stack
The AI sales system where the architecture choices are documented and the API is first-class.
Buyers framing AI sales systems at the architecture level ask different questions than feature buyers. API shape, webhook posture, extensibility, data residency, model-routing documentation, migration portability. Strkr is built with those questions as first-class design decisions rather than retrofit answers.
A systems architect or RevOps lead evaluating an AI sales system has a different checklist than a feature buyer. The questions are usually about the shape of the API (REST versus GraphQL, event-driven versus polling), the webhook posture (which events fire, with what payload, with what retry semantics), the data residency (where prompts and responses live), the model-routing documentation (which model vendor processes which prompt), and the migration portability (what happens to the data on exit). First-wave AI CRM products answered these questions as marketing claims rather than documented architecture decisions. Strkr documents the architecture and treats the API as a first-class interface, which is why systems-focused buyers typically find the architectural answers cleaner.
Opaque API posture
Rate limits and payload shapes documented in the sales call.
Most AI CRM products treat the API as a secondary surface, documented thinly, with rate limits and payload shapes that change without versioning. Strkr publishes a full OpenAPI schema with versioned endpoints, documented rate limits per tier, and a changelog that gets updated per release. The RevOps lead building against the API has a reliable interface rather than a moving target.
Webhook retry mystery
Nobody says what happens when the webhook fails.
A webhook that fails because the downstream endpoint was down for 30 seconds should retry. Most AI CRM products either do not retry at all or retry on an opaque schedule the admin cannot inspect. Strkr documents the exponential backoff schedule (initial retry at 30 seconds, max retries 10, max window 24 hours) and surfaces the retry status in the admin console so the systems team can see what fired and what did not.
Parallel data stores
The AI product builds its own silo outside the CRM.
Several AI CRM products quietly build parallel data stores (contacts, companies, activity history, call recordings) outside the main CRM schema. Teams migrating off hit a data-portability wall. Strkr keeps every record in the shared tenant database, so there is one record of truth and the migration surface at exit is a documented export rather than a vendor-controlled extraction.
Undocumented model routing
The vendor will not say which model handles which prompt.
Systems architects building against an AI CRM need to know which model vendor processes which prompt type for data-sovereignty and model-governance reasons. Several AI CRM vendors treat the routing policy as a trade secret. Strkr publishes the three-tier routing policy on request: WASM-local for simple transforms, light tier for mid complexity, the complex tier for deep reasoning, with the vendor named per tier.
No event-driven primitives
Polling the API for state changes is the only option.
A systems team building a reactive workflow against the CRM needs event-driven primitives: webhook on opportunity stage change, on contact create, on activity log, on AI call completion. Several products ship a REST API but no webhook surface, so the systems team polls, which is wasteful and lossy. Strkr ships event-driven webhooks on every major entity transition with documented payloads.
Extensibility ceiling
Custom-field support stops at 20 fields per object.
Several AI CRM products cap custom fields per object at 20 or 50, which is a hard ceiling for teams with complex sales motions requiring 100-plus custom fields. Strkr's custom-field layer is designed for the taxonomy breadth real sales teams run, with field-level permissions, validation rules, and conditional visibility.
What the system architecture ships
The architectural primitives that make the AI sales system real.
A systems-focused buyer evaluates the primitives rather than the demo. The cards below are the architectural decisions behind Strkr's AI sales system, each documented and testable in the trial tenant.
OpenAPI-documented REST
Versioned endpoints with full schema publication.
Full OpenAPI 3.1 schema for every REST endpoint, versioned at the URL (v1, v2) with a documented deprecation window on breaking changes. Rate limits per tier published in the API docs rather than discovered empirically. The systems team builds against a stable interface.
Event-driven webhooks
Subscribe to every major entity transition.
Webhooks fire on opportunity stage change, contact create, activity log, call complete, AI call complete, methodology scorecard update, approval request state change. Each webhook ships a documented payload. Exponential backoff retry schedule with status visible in the admin console.
Three-tier model routing policy
Published routing policy per task complexity.
WASM-local for Tier 1 simple transforms (no external model vendor). light tier for Tier 2 mid complexity. the complex tier class for Tier 3 complex reasoning. The policy document names the vendor per tier and the data-handling posture. Procurement committees get the full document on request.
Native data model
One tenant database; no parallel stores.
Every record (contact, account, opportunity, activity, call transcript, AI-generated summary) lives in the tenant database. No parallel data store controlled by the vendor outside the CRM schema. Migration at exit is a documented export rather than a vendor-controlled extraction.
Custom-field taxonomy
Hundreds of custom fields per object with field-level perms.
Custom fields on every object with 14 supported field types, field-level permissions, validation rules, and conditional visibility. The taxonomy breadth real sales teams need (segments, verticals, methodology data) is first-class rather than capped at an opinionated ceiling.
Per-AI-call audit log
Replay prompt, response, model, user per call.
Every AI call captured with prompt, response, model routed to, latency, token count, user ID. Admin console surfaces each call with filter and replay. SIEM export on request against standard formats. The audit log is a first-class product surface rather than a billing report.
Claims-propagating AI
AI runs under user claims across every call.
Every AI call runs under the asking user's own claims and the same row-level security that gates the application. A user who cannot read an account directly gets the same denial through the AI. The architectural choice means visibility rules propagate automatically rather than per-feature configuration.
AIMDS defense layer
Admin-configurable prompt and output scanning.
Incoming prompts scanned for jailbreak and injection patterns. Outgoing drafts scanned for PII, banned terms, and compliance-sensitive content. Admin configures block, warn, or pass per category. The layer is documented, versioned, and testable in the trial tenant with procurement-supplied adversarial prompts.
Flow and automation builder
Trigger-condition-action primitives with AI steps.
Flow builder with trigger-condition-action primitives. Triggers fire on entity events. AI drafting steps native in the flow definition. Approval primitives at any step. The automation layer is composable rather than a canned set of pre-built sequences; the systems team builds the workflows their motion needs.
How the API posture compares
The interface differences systems teams run into.
The architectural choices surface quickly when a systems team starts integrating. The cards below are the differences between Strkr's API posture and the first-wave AI CRM products, on the dimensions systems teams actually hit in practice.
Rate limits
Per-tier documented limits with headers.
Strkr publishes rate limits per tier in the OpenAPI schema. Response headers include remaining budget. The systems team builds backoff logic against documented numbers. First-wave vendors frequently return 429s with no header; the team discovers the limit empirically and the integration breaks on the next pricing-tier change.
Webhook retry
Exponential backoff with inspectable status.
Strkr's webhook retry runs exponential backoff from 30 seconds with max 10 retries over 24 hours. The admin console shows each attempt. First-wave vendors frequently either do not retry at all or retry on an opaque schedule, which the systems team learns about during the next outage postmortem.
Payload stability
Versioned payloads with deprecation window.
Strkr versions webhook payloads at the subscription level. Breaking changes ship a new version with a documented deprecation window (minimum 180 days). First-wave vendors frequently ship payload changes without notice. The systems team discovers the change on the next broken parse.
Idempotency
Idempotency keys on every mutating endpoint.
Every mutating REST endpoint supports an idempotency key header. A retry with the same key does not double-create. First-wave vendors frequently require the systems team to implement idempotency at the integration layer, which is a predictable source of duplicate-record incidents.
Pagination
Cursor-based with stable ordering.
Strkr paginates with cursors and stable ordering, so a resumed pagination after a client crash does not miss or duplicate records. First-wave vendors frequently use offset-based pagination that becomes unreliable under concurrent writes.
Bulk API
Native bulk endpoints with status polling.
Strkr ships bulk endpoints for create, update, and delete with asynchronous status polling. The systems team moves 100,000 records as a single operation rather than a 100,000-request loop. First-wave vendors frequently require the per-record loop, which hits the rate limit and burns the integration day on a batch load.
Where the system stops
What Strkr's architecture does not do, and why.
A systems-focused buyer wants to know the architectural ceilings as clearly as the capabilities. The cards below are where Strkr's system stops at architecture scale, with the reason rather than a roadmap commitment.
No GraphQL endpoint
REST is the API surface today.
Strkr ships REST (and webhooks) as the API surface. GraphQL is not shipped and we do not have an immediate plan to ship it, because the common use cases our customers run against the API fit REST cleanly and adding GraphQL as a second surface would double the maintenance load without a proportional capability lift. Teams whose integration patterns require GraphQL should factor this before signing.
No self-hosted deployment
Multi-tenant cloud only.
Strkr runs multi-tenant cloud with tenant-isolated data and per-tenant encryption keys. We do not ship a self-hosted or single-tenant deployment. For buyers whose compliance framework requires self-hosted, we do not fit. For teams whose framework accepts multi-tenant with the documented residency and audit posture, we clear the review cleanly.
No full code-level extensibility
Automation lives in the flow builder, not in server-side code.
Automation extensibility runs in the flow builder with trigger-condition-action primitives. The flow builder covers the vast majority of real automation needs. For teams who want to run arbitrary server-side code against entity events in-tenant (Salesforce Apex style), Strkr is not that surface today. The webhook plus external-worker pattern covers most of those use cases.
Non-English quality lags
English drafting strongest; others vary.
AI drafting quality is strongest in English, serviceable in Spanish and German, thinner in other languages. Teams running non-English pipelines should pilot on a sample before standardizing. We publish quality notes per language on request so the pilot has a defensible baseline.
Regional data-sovereignty roadmap
Specific regional certs on roadmap.
SOC 2 Type II in progress through 2026-Q4. HIPAA with BAA. GDPR and CCPA. BSI C5 in Germany, SecNumCloud in France, IRAP in Australia, FedRAMP for federal are on roadmap. Enterprise buyers with specific regional requirements should ask for the current certification timeline so procurement can plan.
Vendor-neutral model labeling
UI says Strkr AI; routing policy on request.
User-facing surfaces say Strkr AI. The underlying three-tier model routing is an operational detail. Procurement committees that need the policy for a vendor memo get the full document on request. The capability is tested against the Strkr brand rather than the model vendor whose reputation may shift.
Head-to-head
Strkr system architecture vs HubSpot and Salesforce for systems-focused buyers.
The two generalist AI CRMs that systems architects evaluate most. The comparison below focuses on the architectural primitives that systems teams hit in practice rather than the feature headlines.
Feature
Strkr
HubSpot Enterprise or Salesforce Enterprise
API surface
OpenAPI 3.1 schema; versioned endpoints
REST + GraphQL (Salesforce); REST (HubSpot)
Webhook retry policy
Exponential backoff; inspectable in console
Varies; frequently opaque
Idempotency support
Idempotency key on every mutation
Partial; often external implementation
Bulk API
Native bulk with async status
Available (Salesforce Bulk API); limited (HubSpot)
Model routing documentation
Published three-tier policy
Model-vendor details limited
Data residency
Tenant-resident; no parallel stores
Varies; some AI features cache externally
Custom field count
Hundreds per object with field perms
Capped per tier (HubSpot); generous (Salesforce)
Rate limits
Per-tier documented; headers include budget
Varies; Salesforce documented, HubSpot thinner
Audit log
Per-AI-call replay with SIEM export
Partial; AI-call replay varies
Compliance posture
SOC 2 Type II in progress through 2026-Q4; HIPAA compliant with BAA available
Varies by product tier
How teams use Strkr
How systems teams actually build on Strkr.
Real integration patterns from systems-focused customers. The common thread: the API posture holds up under production load and the audit layer answers the compliance review without engineering retrofits. Patterns below.
Series B SaaS, 48 reps, RevOps-led
Built custom territory assignment on top of webhooks.
A 48-rep SaaS team's RevOps lead built a custom territory-assignment service against Strkr's webhook surface. On opportunity-create the webhook fires, the service applies the custom territory logic (which Salesforce had been doing in Apex), and writes the assigned rep back via the REST API. The service runs on Lambda, costs roughly $8 per month at the team's volume, and replaced a $40K Salesforce Apex budget line for the use case.
Mid-market B2B, 60 reps, data-warehouse team
Native bulk API moved 240K records in under 20 minutes.
A 60-rep B2B team's data team migrated 240K historical records from the previous CRM to Strkr using the native bulk API. The migration ran in roughly 18 minutes with async status polling. The team had budgeted 8 hours for the operation based on prior experience with per-record REST loops on specialist vendors. The time saved went to validating the data landed cleanly rather than fighting the loader.
A 22-AM agency had been seeing roughly 1-2 duplicate-contact incidents per month from retried API calls after transient network failures. After moving the integration to Strkr with idempotency keys on every create, duplicate-record incidents dropped to zero over the following six months. The integration lead credited the architectural posture rather than any additional defensive code.
FinServ, 14 wealth advisors, compliance-driven
SIEM export answered the 90-page compliance review.
A 14-advisor wealth firm's compliance review required SIEM export of every AI call for retention under the firm's records policy. Strkr's audit log export against standard SIEM formats answered the compliance question cleanly. The firm had been running an incumbent product that could not produce the export without a separate services engagement.
Pre-IPO growth team, 110 reps, systems architect
Three-tier model routing policy satisfied the AI governance memo.
A 110-rep growth team at pre-IPO scale had a formal AI governance review that required documented per-vendor data flow for every AI capability in the stack. Strkr's published three-tier routing policy named the vendor per tier and the data-handling posture at each. The governance memo cited the Strkr document as the clearest vendor-side answer in the review and approved the capability within the standard 4-week window rather than the 10-to-12-week extension the systems architect had budgeted.
The AI sales system with the architecture documented first-class.
Start a 14-day trial. OpenAPI 3.1 REST, event-driven webhooks, published model routing policy, native bulk API, idempotency keys, SIEM export. Architecture tested in your tenant rather than claimed on a slide.
OpenAPI 3.1 REST with full schema publication, versioned endpoints (v1, v2) at the URL, documented rate limits per tier in the schema, response headers that include remaining rate budget, idempotency keys on every mutating endpoint, cursor-based pagination with stable ordering, and native bulk endpoints with async status polling. Webhooks fire on every major entity transition with versioned payloads and exponential backoff retry visible in the admin console. GraphQL is not shipped today because our customer use cases fit REST cleanly; teams whose integration patterns require GraphQL should factor this before signing.
How does the webhook retry work in practice?
Exponential backoff from 30 seconds with max 10 retries over 24 hours. The admin console surfaces each attempt with timestamp, response code, and payload. Failed webhooks after the final retry land in a dead-letter queue that the admin reviews. Payload stability is versioned at the subscription level with a 180-day deprecation window on breaking changes. The systems team builds against a stable interface rather than discovering changes in the next broken parse.
Where does the AI data actually live?
Prompts live transiently at the model vendor during the AI call and persist only in the Strkr tenant audit log. Responses persist only in the Strkr tenant and the admin audit. No training-set contribution from tenant data. Every contact, account, opportunity, activity, call transcript, and AI-generated summary lives in the tenant database rather than a vendor-controlled parallel store. The migration surface at exit is a documented export rather than a vendor-controlled extraction. Retention is configured per customer with the default at 90 days for the audit log.
What is the three-tier model routing policy?
Tier 1 simple transforms (var-to-const, field conversion, basic type changes) route to a WASM-local path with no external model vendor involved. Tier 2 mid-complexity tasks (reply drafts, short summaries, straightforward extractions) route to light tier models. Tier 3 complex reasoning (forecast narrative, deal-risk analysis, cross-record retrieval, methodology scoring) routes to the complex tier class. The policy document names the model vendor per tier and the data-handling posture at each. Procurement committees get the full document on request. The economics work because every call routes to the cheapest model that can do the job rather than every call hitting a frontier model.
Can we build custom automation against Strkr?
Yes, two patterns. First, the native flow builder with trigger-condition-action primitives covers the vast majority of real automation needs: trigger on entity events, apply conditions, run AI drafting steps or approval primitives or external HTTP calls. Second, the webhook plus external-worker pattern for cases that need arbitrary server-side logic: webhook fires, external service runs the custom logic (on Lambda, Cloudflare Workers, or any infra), writes back via REST API with idempotency keys. The two patterns together cover roughly 95 percent of what customer systems teams build. For teams needing in-tenant Apex-style code execution we are not that surface today.
How does the system architecture answer enterprise procurement?
Every AI call is logged with prompt, response, model routed to, latency, token count, and user ID, retained in the admin audit log and exportable to a SIEM pipeline on request. Every call runs under the user's own claims so visibility rules propagate. The AIMDS defense layer scans incoming prompts and outgoing drafts. The three-tier model routing policy is documented. SOC 2 Type II controls are in progress through 2026-Q4; HIPAA compliant with BAA available. The architectural posture answers the standard enterprise AI governance questionnaire in the 90-plus percent clean-answer range, with the remaining gaps typically being specific regional certifications we have on roadmap with a committed timeline.
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