CRM

Migrate your Synerise data

AI-first behavioral data platform for enterprise marketing teams that want real-time personalization and automation built on proprietary infrastructure rather than third-party components.

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In its favor

Why people choose Synerise

The signal that keeps Synerise on the shortlist. Sourced from G2, Capterra, and customer scoping calls.

Customers choose Synerise for its real-time behavioral AI engine — sub-50ms latency from event capture to profile enrichment to AI decision, not batch-processed.

The platform's in-house AI foundation models (BaseModel.ai for behavioral prediction, Cleora.ai for embedding generation) avoid third-party AI vendor lock-in, appealing to enterprises with strict data sovereignty requirements.

Synerise's multi-channel marketing automation — email, SMS, push, WhatsApp, and webhooks — runs on a single workflow canvas, reducing tool sprawl for mid-market marketing teams.

The schema builder (Brickworks) lets teams define arbitrary custom data structures, enabling vertical-specific use cases without waiting for platform roadmaps.

Customers cite exceptional customer segmentation capabilities as a top differentiator, with AI-driven personalization that adapts to individual behavioral patterns rather than static rules.

Building dashboards and reporting views requires starting from scratch every time — the flexibility that enables creative reporting also creates significant time investment for common visualization needs.

Custom attribute names cannot be renamed or deleted after creation, which creates technical debt for organizations that evolve their data model over time.

Pricing is entirely custom and opaque — no public per-seat or per-feature tiers, requiring lengthy sales cycles and making cost predictability difficult for growing teams.

Reasons to switch

Why people leave Synerise

The recurring reasons buyers give for replacing Synerise. Presented as facts, not knocks.

Platform scorecard

Strengths, weaknesses, and where Synerise fits

Grades across six dimensions, plus a SWOT-style view of where the platform shines and where it falls short.

SWOT — strengths, weaknesses, and use-case fit

Strengths

Proprietary AI stack — TerrariumDB, BaseModel.ai, Cleora.ai — built entirely in-house with no third-party AI vendor dependencies.Real-time event processing with sub-50ms latency from capture to profile enrichment to automated action.Massive API surface — 900+ endpoints across 15 API domains — covering every major data object with batch support on key endpoints.Flexible schema builder (Brickworks) enables arbitrary custom data structures without platform limitations.Behavioral Data Hub consolidates catalogs, schemas, item feeds, and profile data in one central repository.

Weaknesses

Custom attribute names are immutable after creation — a design constraint that causes technical debt and migration complexity.Dashboard and reporting views must be built from scratch each time — no pre-built templates for common marketing metrics.Pricing is fully opaque and custom-quote-only with no public tier structure, making competitive evaluation difficult.Workflows operate on a fire-and-forget model — action completion does not gate workflow progression, which can cause race conditions in complex automation chains.

Where it works

Large enterprise marketing teams processing billions of behavioral events monthly, where sub-50ms real-time personalization latency is a competitive requirement.Organizations with strict data sovereignty requirements needing in-house AI infrastructure without third-party vendor dependencies or cloud lock-in.Mid-to-large B2C businesses operating multi-channel marketing (email, SMS, push, WhatsApp) that want to consolidate automation on a single workflow canvas.Teams with dedicated technical resources capable of building dashboards and reporting views from scratch, trading setup time for maximum flexibility.Retail or e-commerce companies managing large item catalogs (up to 1M items) needing AI-driven visual similarity and behavioral recommendations.

Where it struggles

Small marketing teams or startups without dedicated technical resources, since all dashboards and reporting views must be built from scratch without templates.Organizations with evolving data models, because custom attribute names become immutable after creation, creating accumulated technical debt over time.Companies requiring transparent pricing and predictable costs, as Synerise uses fully custom-quote-only pricing with no public tier structure.Complex automation scenarios requiring conditional workflow progression, since workflows operate on a fire-and-forget model without action-completion gating.Projects with tight implementation timelines, given the lengthy custom sales cycles and configuration-heavy setup required before realizing value.

Pricing tiers

Synerise pricing overview

Synerise does not publish pricing tiers on its website. All plans are custom enterprise agreements negotiated through sales. Pricing is likely based on data volume (profile count, event volume), API call volume, and module access rather than per-seat or per-feature linear tiers.

Custom Enterprise

Tier 1 of 1

Not publicly documented — contact sales

What's included

All modules included: Behavioral Data Hub, Automation Hub, AI Recommendations, AI SearchBrickworks schema builder and custom object supportFull API access across all 15 API domainsDedicated support and onboardingVolume-based pricing negotiated per contract

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Pricing is informational. FlitStack AI does not bill on Synerise's schedule — see our quote-based pricing →

What gets migrated

Synerise object support

Object-by-object support for Synerise migrations. Per-pair details surface during scoping.

Profiles

Fully supported

The primary customer identity object in Synerise. Accessed via the Profile Management API (89 endpoints). Exportable to CSV/JSON/JSONL with a 10M record cap per job. Default attributes are email, firstName, lastName, city, birthDate, phone. Custom attributes are fully supported but names become immutable after creation.

Events

Fully supported

Behavioral signals tracked per profile — page.visit, product.view, added-to-cart, transaction, and 40+ other event types. Batch event ingestion is supported via POST /v4/events. Events are assigned to the client's clientId or UUID. Historical event export is available through the Analytics Suite API.

Companies/Accounts

Mapping required

Companies are linked to Profiles via the profile.assigned-to-company event. There is no standalone Company object — company data lives as profile attributes or schema records in Brickworks. We map these by tracking the assignment event and merging company fields into a structured schema.

Transactions

Fully supported

Transaction records are created via POST /v4/transactions or POST /v4/transactions/batch. They contain line items, totals, and timestamps. Exportable via Data Management API. Transaction events (transaction, cancelled-transaction) are automatically tied to the profile that made the purchase.

Catalogs

Fully supported

Product/item feeds managed in the Data Modeling Hub. Exports available as CSV, JSON, or JSON lines. Item feeds are used by AI recommendation models (personalized, visual similarity, top items). Catalog records can include arbitrary custom fields per catalog schema.

Segments

Mapping required

Segmentation returns true/false values per profile. Export includes segment membership flags. Custom segments built in the Segmentation builder may have complex rule logic that requires expression evaluation during import to reproduce membership on the destination platform.

Custom Attributes

Mapping required

Any profile attribute beyond the defaults can be custom-named. The critical constraint is that custom attribute names cannot be changed after creation. We audit all custom attribute names during scoping and flag immutability risks before migration begins.

Automation Workflows

Mapping required

Workflows are defined in Automation Hub with trigger nodes, conditions (Profile Filter), and actions (Send Email, Send SMS, webhook calls). Workflows are fire-and-forget — completion of an action node does not gate workflow progression. Active workflow state cannot be migrated; we export workflow definitions and re-activation is a separate step post-cutover.

AI Recommendations

Mapping required

Recommendation configurations (personalized, visual similarity, last seen, top items, item comparison) are trained on catalog feeds and profile event history. We export recommendation model configurations and re-import them to the destination's recommendation engine where equivalents exist. Visual similarity models trained on Synerise's proprietary image embeddings require re-training on the destination platform.

Schemas (Brickworks)

Fully supported

Brickworks schemas are arbitrary record structures that store any data type. Exportable from the Data Modeling Hub. Schema definitions (field names, types, constraints) must be recreated on the destination. Singleton schemas and 'one to many filtered' field types have specific handling requirements.

Campaigns

Mapping required

Campaign definitions — email, SMS, push, WhatsApp — are accessible via the Campaigns API (100 endpoints). We export campaign configurations including templates, audience rules, and scheduling. Active campaign state (sends in progress, queue depth) cannot be migrated atomically.

Tags

Fully supported

Profile tags are exported as true/false boolean attributes in the profile export. Tag names are free-form strings. We preserve full tag sets per profile and import them as tag fields or custom properties on the destination platform.

Aggregates and Expressions

Mapping required

Synerise supports computed aggregates and expression results attached to profiles. These are exported as scalar values. Since aggregates are computed from underlying events, destination recomputation may produce different values if the event history is not fully replicated.

Gotchas

What to watch for in Synerise migrations

Issues we've hit on past Synerise migrations, tagged by severity. FlitStack AI handles every one — surfacing them up front because buyer engineering teams want to know.

High

Immutable custom attribute names cause migration mapping failures

High

Active automation workflow state cannot be preserved at cutover

Medium

5GB file and 10M record export caps require chunked migration planning

Medium

Visual similarity AI recommendations require full model retraining

Low

Reserved attribute names cannot be used in custom field creation

How a Synerise migration works

Four steps, Synerise-specific

Connect

API key-based authentication with JWT tokens issued per workspace into Synerise. Scopes limited to read-only on the data we move.

Map

We translate Synerise-specific structures (custom fields, objects, value lists) to the destination's model.

Sample

Test with a 50–200 record subset to validate Synerise quirks before production.

Migrate

Full migration with Synerise rate-limit handling. Rollback available throughout.

FAQ

Synerise migration FAQ

Answers to the questions buyers ask most during Synerise migration scoping. Not seeing yours? Book a call.

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Most Synerise migrations under 1M records finish in 48–72 hours end-to-end. Larger orgs with custom objects or buyer-side security review typically take 5–7 days.

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