CRM migration

Migrate from Devi to Freshsales

Field-level mapping, validation, and rollback between Devi and Freshsales. We move data and schema; workflows are rebuilt natively in Freshsales.

Devi logo

Devi

Source

Freshsales

Destination

Freshsales logo

Compatibility

63%

5 of 8

objects map 1:1 between Devi and Freshsales.

Complexity

CModerate

Timeline

3-5 weeks

Rollback included Accuracy guarantee Field-level validation

Overview

What this migration involves

Moving from Devi to Freshsales is a migration from an opaque, narrowly-focused social-lead tool to a full-stack sales CRM. Devi's research corpus contains no documented API, no confirmed data export mechanism, and no verifiable schema — we approach this migration with mandatory pre-engagement discovery to establish what records actually exist and in what format. Freshsales (Freshworks, NASDAQ: FRSH) operates over 60,000 customer accounts and provides a fully documented REST API with CSV import, Bulk API, and a published data model covering Contacts, Accounts, Deals, Leads, and Activity history. We do not migrate Devi's workflows, automations, or AI-generated content assets as code; we deliver a written inventory of these for the customer's admin to rebuild in Freshsales. The primary migration objects are person-level records (mapped to Freshsales Contact and Account), high-intent lead signals (mapped to Freshsales Lead or a custom lead-score field), and any historical timestamps that survived export.

Field-level fidelity

Every standard and custom field arrives verified.

Schema-aware mapping

AI proposes the map; you confirm before any record moves.

Relationships preserved

Parent–child, lookups, and ownership stay linked.

Full activity history

Calls, emails, meetings — with original timestamps.

Attachments & notes

Documents, uploads, and inline notes move with the record.

Why teams make this switch

Two sides of the same decision

Leaving

Devi logo

Devi

What's pushing teams away

  • Devi is a lead-monitoring tool, not a full CRM — teams that adopt it for prospecting still need a real CRM (HubSpot, Pipedrive, Attio) downstream, which limits its standalone life cycle.
  • Coverage is Facebook Groups + LinkedIn + Reddit + X. Teams running heavy ICP work on Slack communities, Discord, or YouTube comments outgrow it quickly.
  • Bundled ChatGPT credits run out fast on teams that use the 1-click outreach feature heavily — Solo's 250 calls cap is reached within a single active campaign.
  • Capterra and aggregator footprint is thin compared to established sales-intelligence tools, making procurement diligence harder at larger orgs.
  • Devi's outreach is comment- and DM-based on third-party platforms, which carries the platform-bans risk inherent to any automation that touches Facebook/LinkedIn/Reddit APIs.

Choosing

Freshsales logo

Freshsales

What's pulling them in

  • Lowest barrier to entry among major CRMs — the free tier supports up to 3 users and includes core CRM functionality before committing to per-seat pricing.
  • Built-in chat, email, and phone reduce reliance on third-party integrations for basic sales communication and contact management.
  • Freddy AI contact scoring and deal insights are included on Pro plans at a lower price than comparable HubSpot tiers.
  • Kanban pipeline views across Contacts, Accounts, and Deals provide visual deal management without requiring custom configuration.
  • Integration with the broader Freshworks ecosystem (Freshdesk, Freshchat, Freshservice) reduces tool sprawl for teams already using Freshworks.

Object mapping

How Devi objects map to Freshsales

Each row shows how a Devi object lands in Freshsales, including any object-level transformations, lookup resolution, or schema-design dependencies.

Typical mapping — final map is confirmed during the sample migration step.

Devi

Person-level records (inferred)

maps to

Freshsales

Contact

1:1
Fully supported

Devi's high-intent lead detection implies person-level records with name, social handle, and engagement signals. We treat these as Freshsales Contacts with a discovery-phase schema confirmation required. Any inferred social media identifiers map to a custom text field devi_social_handle__c. Email address, if present, maps to the standard Email field on Contact. If Devi's records include company affiliation, we split to Account with the AccountId resolved before Contact insert.

Devi

Company records (inferred)

maps to

Freshsales

Account

1:1
Fully supported

Devi's social listening may capture company signals (employer, brand mentions). If company data is present in Devi's export, we map to Freshsales Account with Website, Industry, and Phone preserved. Account is created before Contact import so that the AccountId Lookup is satisfied at the moment of Contact insert. If no company data exists in Devi's export, we create a default Account placeholder to attach person-level contacts.

Devi

Lead signals (inferred)

maps to

Freshsales

Lead

1:1
Fully supported

Devi's core value proposition is identifying high-intent leads on social media. We infer a lead-scoring or intent-signal field that maps to Freshsales Lead with a custom field devi_intent_score__c carrying the original signal value. Lead Status maps to a Freshsales Status field with values defined during scoping. If Devi's records are already qualified (Contact-stage), we map directly to Freshsales Contact instead of Lead.

Devi

AI-generated content assets (inferred)

maps to

Freshsales

Custom Object or Note attachment

lossy
Fully supported

G2 reviewers mention AI-generated visual content as a Devi feature. If exported assets include image URLs, file references, or asset metadata, we store these as ContentDocument records linked via ContentDocumentLink to the parent Contact or Lead. If the asset count is high and the customer needs a structured content library, we pre-create a custom Asset__c object with Name, Type, URL, and OwnerId fields before migration.

Devi

User records

maps to

Freshsales

User

1:1
Fully supported

Devi may include user seat records or assigned owner information on lead entries. We match by email against the Freshsales destination User table. Any Devi User without a matching Freshsales User goes to a reconciliation queue for the customer's admin to provision before record import resumes. Active/inactive status is preserved in a custom field devi_user_status__c.

Devi

Engagement timestamps

maps to

Freshsales

Task or Event

1:1
Fully supported

Devi's lead detection runs on a schedule, producing timestamped events (lead identified, intent signal triggered). These migrate to Freshsales Task records with Subject, ActivityDate, and description preserved. The Task maps to the parent Contact or Lead via WhoId. We do not migrate social media message content as Freshsales EmailMessage records unless Devi's export includes full message bodies in a structured format.

Devi

Custom fields (unknown schema)

maps to

Freshsales

Custom fields on standard objects

lossy
Fully supported

Devi's data model is unconfirmed. We allocate discovery time during Phase 1 to receive the customer's Devi export (CSV preferred), identify all columns, infer data types, and map each to a Freshsales standard or custom field. Custom fields in Freshsales are created as __c fields on the relevant object before any data import begins. We validate field types (text, number, date, picklist) against Freshsales API constraints before migration.

Devi

Tags or labels (inferred)

maps to

Freshsales

Multi-select picklist

lossy
Fully supported

Social media lead tools commonly use tags to segment by platform (Twitter, LinkedIn), intent level, or content type. If Devi's export contains tag columns, we map to Freshsales multi-select picklist fields on Contact or Lead. Tag values are preserved as individual picklist entries, and the migration validates that total tag string length does not exceed Freshsales' 40,000-character field limit.

Gotchas + challenges

What specifically takes care here

Platform-specific issues from each side, plus the pair-specific challenges that don't show up on either platform's page on its own.

Devi logo

Devi gotchas

High

Platform identity is ambiguous in search results

High

No documented export or API access

Medium

Thin review corpus makes due diligence difficult

Freshsales logo

Freshsales gotchas

Medium

Freddy AI is Pro-tier only despite heavy marketing

High

Post-migration emails and sequences are disabled

Medium

Bot session credits are a one-time 500-session allocation

Medium

Phone credits charged per minute with no cap

Low

File storage limits scale with plan tier

Pair-specific challenges

  • No documented API or bulk export from Devi

    The research corpus contains zero evidence of a public API, a documented bulk export endpoint, or a data portability feature for devi-official.com. This is the defining migration risk for this pair. Before any migration scoping begins, the customer must confirm with Devi support whether a CSV export, JSON export, or API access exists. If no export mechanism exists, we work with the customer to manually extract data into a structured format, which increases timeline and cost. We flag this upfront and request written confirmation of export capabilities before engaging FlitStack AI for a migration project.

  • Devi's data model is unconfirmed and may be inferred only

    The only verifiable product reference for Devi is a single G2 review describing a social media lead detection tool. We cannot confirm how contact records, company records, lead signals, or content assets are structured in Devi's database. We approach migration scoping with mandatory discovery: we require the customer to provide a sample export, screenshots of the data model, or a written schema description before we design the Freshsales mapping. Any migration run without confirmed source schema carries the risk of field misalignment that the customer accepts by proceeding.

  • Platform identity ambiguity between devi-official.com and Devi AI

    Search results for devi-official.com returned no verifiable product pages, documentation, or community presence. The only attributable product review names 'Devi AI' on G2 as a social media lead tool. We cannot confirm that devi-official.com and 'Devi AI' are the same product with shared API or data model. Before migration, the customer must confirm the exact product name, version, and data ownership entity. Migration scoped to the wrong product will result in a failed export and additional discovery costs.

  • Freshsales lead conversion overwrites custom score fields

    Freshsales' standard Lead-to-Contact conversion action promotes Lead fields to Contact fields but does not automatically preserve custom score or intent metadata in standard Contact fields. If Devi's intent-score values are stored as custom Lead fields, we preserve them in a custom Contact field devi_original_score__c during conversion, but this requires a post-conversion mapping step that the customer must approve. Standard Freshsales behavior does not carry custom fields through conversion without this explicit handling.

  • AI-generated content assets may not export as files

    Devi's AI visual content generation produces images or templates that may exist only within Devi's interface without a downloadable file URL. If Devi's export provides image URLs rather than file blobs, we link these as external URLs in a custom Asset__c object rather than migrating them as Freshsales ContentDocument records. The customer should verify whether AI-generated assets are exportable and in what format before scoping.

Migration approach

Six steps for a successful Devi to Freshsales data migration

  1. Pre-engagement export confirmation

    We request written confirmation from the customer that Devi has a data export mechanism (CSV, JSON, API) and that they have access to export credentials. If no export exists, we document the manual extraction path and adjust the discovery timeline accordingly. We also confirm the exact product name and domain used by the customer's team to rule out platform identity ambiguity.

  2. Discovery and schema inference

    We receive a sample export or schema description from the customer and identify all columns, inferred data types, record counts, and object relationships. We cross-reference Devi's inferred model against Freshsales' documented data model and produce a written schema map with field-level type assignments. Any column that cannot be typed from the export goes to a clarification queue with the customer. This phase produces the migration specification that both parties sign off on before migration begins.

  3. Freshsales destination setup

    We create the destination Freshsales account structure: standard objects are enabled and validated, custom fields are created on Contact, Account, Lead, and any custom object with __c API names matching Devi's field names where possible. We configure Record Types and Sales Processes if multiple pipelines exist on the source. Territory assignment rules are set up per Freshsales best practices. Owner mapping is validated against the customer-provided Freshsales User list.

  4. Test migration and reconciliation

    We run a test migration using a subset of Devi's exported data into a Freshsales trial or sandbox environment. We reconcile record counts, spot-check 25-50 records field-by-field, and validate that custom field data appears correctly in Freshsales. Any mapping corrections are applied to the migration specification. The customer reviews the test output and signs off before production migration proceeds.

  5. Production migration in dependency order

    We run production migration in dependency order: Accounts (from Devi's company signals, or a default placeholder), Contacts (with AccountId resolved), Leads (with intent score in devi_intent_score__c), Tasks and Events (with WhoId pointing to Contact or Lead), and custom asset records last. Each phase emits a row-count reconciliation report. If Devi's export produces new records during the migration window, we run a delta sync before cutover.

  6. Cutover, validation, and automation handoff

    We freeze Devi writes during cutover, run a final delta migration, and enable Freshsales as the system of record. We deliver a written inventory of Devi's inferred workflows, automation rules, and content assets requiring rebuild in Freshsales (using Freshsales Workflows, Automation Rules, or Freddy AI configurations). We support a one-week hypercare window for reconciliation issues. We do not rebuild Devi's automations as Freshsales workflows inside the migration scope; that is a separate engagement.

Platform deep dives

Context on both ends of the pair

Devi logo

Devi

Source

Strengths

  • Focuses on a specific workflow — social media high-intent lead detection — which reduces feature bloat for teams doing outbound social selling
  • Generates visual content with AI, potentially reducing the need for a separate design tool
  • One G2 reviewer describes it as working well for its stated purpose with no significant complaints
  • Small-business positioning suggests a low-friction onboarding experience for teams under 10 users
  • Appears to have a free tier or low-cost entry point based on the positive ROI mentions in reviews

Weaknesses

  • Very limited public documentation — no developer docs, no API reference, no community forum evidence found in the research
  • Market presence is thin: only one verifiable G2 review from a real user, making independent due diligence difficult
  • No confirmed data export or API access, which is a critical risk for any team that needs to move data later
  • It is unclear whether devi-official.com and the 'Devi AI' referenced on G2 are the same product, raising identity risk
  • No information available about data residency, security certifications, or compliance posture
Freshsales logo

Freshsales

Destination

Strengths

  • Generous free tier for small teams with core CRM functionality without per-seat costs.
  • All-in-one sales CRM with built-in telephony, chat, and email reducing third-party tool dependency.
  • Freddy AI contact scoring and deal predictions available on Pro tier.
  • Multiple pipeline views with Kanban and list options across all plans.

Weaknesses

  • Reports lack depth compared to competitors like HubSpot, with limited customization options.
  • Integration setup is poorly documented with no clear guides for connecting third-party tools.
  • AI features gated behind $39/user/month Pro tier despite marketing emphasis on Freddy AI.
  • Bot sessions limited to 500 one-time allocation with no monthly refresh.

Complexity grading

How hard is this migration?

Moderate CRM migration. 3 of 8 objects need a manual workaround.

C

Overall complexity

Moderate migration

Derived from compatibility, mapping clarity, API constraints, and data volume across Devi and Freshsales.

  • Object compatibility

    D

    3 of 8 objects need a manual workaround.

  • Field mapping clarity

    C

    Field mapping is derived from defaults — final spec confirmed during the sample migration.

  • Timeline complexity

    B

    8-object category — typical timelines run 2–7 days end-to-end.

  • API constraints

    B

    Devi: Not publicly documented.

  • Data volume sensitivity

    B

    Devi doesn't expose a bulk API — REST + parallelization used for high-volume runs.

Estimator

Estimate your Devi to Freshsales migration cost

Rule-based pricing — no per-record fees, no manual quotes. Migrations over 2M records are scoped individually.

Step 1

What are you migrating?

Pick a category, then your source and destination platforms.

Category

FAQ

Frequently asked questions about Devi to Freshsales data migrations

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

Can't find your answer?

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Book a free 30 minute consultation

Most migrations land between three and five weeks for straightforward cases where Devi provides a clean CSV export and the data model is confirmed during discovery. Migrations requiring manual data extraction, format reconstruction, or schema inference from customer-provided documentation move to six to ten weeks. The extended timeline is driven by the mandatory discovery phase needed to establish Devi's undocumented data model before we can design the Freshsales mapping.

Adjacent paths

Related migrations to explore

Ready when you are

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