Ada to Freshservice migration requires transforming conversation-centric data into ITSM tickets via API. Plan for Ada's 12-month data window, rate limits on both sides, and variable-to-custom-field mapping.
Migrating from Ada to Freshservice requires transforming data between two fundamentally incompatible system architectures: Ada is a conversation-centric AI platform storing multi-turn chat sessions, bot variables, and end-user identities, while Freshservice is a ticket-centric ITSM platform structured around incidents, requesters, assets, and knowledge base articles. No native, supported migration path exists between the two systems, meaning all data movement requires custom ETL work via Ada's Data Export API and Freshservice's REST API v2. The core technical challenge is converting unstructured, multi-turn chat logs into structured ITSM tickets with proper requester linkage, status mapping, and chronologically ordered conversation notes. Additionally, Ada's 12-month API data retention ceiling imposes a hard constraint on historical data accessibility, and anonymous conversations — estimated at 8–15% of records — require special handling to avoid API rejections on the Freshservice side.
Read this first
Pair-specific gotchas that catch teams out. Each one has cost somebody a weekend.
Freshservice requires a Requester email to create a ticket
If an Ada chat was anonymous (in observed migrations, 8–15% of conversations lack email metadata), you must create a generic "Guest User" requester (e.g., anonymous@yourdomain.com) and map all anonymous conversations to that ID to prevent API rejections.
Ada's Data Export API limits queries to 60-day date ranges
For a full extraction, iterate in 60-day windows across your desired time span. Data older than 12 months is not available through this API — contact Ada support for a bulk export if you need it. Allow at least 2 weeks lead time for Ada support to process bulk export requests.
Freshservice public APIs are primarily intended for integration purposes, and not
Freshservice public APIs are primarily intended for integration purposes, and not recommended for large-volume API transactions. For migrations exceeding a few thousand tickets, request elevated rate limits through Freshservice's data migration partner program, which provides separate bulk migration endpoints accepting batched ticket and note creation. Allow 3–5 business days for Freshservice to process rate limit increase requests.
The runbook
Work top to bottom. Tick steps as you go — your progress is saved in this browser.
01 Discovery Establish why you are moving, what "done" means, and who signs off.
Objective A written scope with agreed success criteria, a named owner per workstream, and a budget approved by finance.
Keep these open
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Pull the real numbers out of Ada
Export counts for tickets (open and closed separately), contacts, organisations, attachments, macros, triggers, automations, views and SLA policies. Note the oldest ticket date — history depth drives the whole timeline. Estimating from memory is the single most common cause of a blown migration window.
Data Profiler Get real record counts instead of estimating from memory -
Decide what history actually moves
Agree a cut-off with the support lead: all history, last 24 months, or open tickets plus a read-only archive. Every extra year of closed tickets adds API time and cost without adding much agent value. Get this in writing — it is the decision people relitigate mid-cutover.
A "move everything" default is what turns a two-week migration into a two-month one.
COI & ROI Calculator Build the 36-month business case you will need for sign-off -
Confirm Freshservice can hold your support model
Walk your current workflow through Freshservice: multi-brand, business hours, SLA targets, CSAT, side conversations, public vs internal notes, and any channel you depend on (voice, chat, WhatsApp, social). List anything with no native equivalent — those are project risks, not configuration details.
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Build the business case
Model licence delta, migration effort, agent retraining, and the cost of staying put (Cost of Inaction). Executives approve a number, not a plan, and you will be asked for it again at the go/no-go.
Helpdesk Migration Planner Turn ticket volume into a dated Ada → Freshservice timeline -
Name owners and set the go/no-go date
One named owner each for data, configuration, integrations, and agent enablement, plus a decision-maker who can call a rollback. Put the go/no-go meeting in calendars now, 48 hours before the freeze.
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Create custom ticket fields
for every Ada variable you want to preserve. Freshservice custom field limits are plan-dependent: Starter plans support approximately 30 custom fields per ticket type, Growth supports around 60, Pro supports approximately 150, and Enterprise supports up to 300. Audit your Ada variable count against these limits before committing to a plan tier.
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Create a dedicated agent account
(e.g., "Ada Bot") to attribute bot messages. This consumes one agent seat in your Freshservice license — account for this in your seat count. Use a service account rather than a named user license to avoid audit complications.
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Configure ticket statuses
to accommodate Ada's resolution taxonomy.
Ada → Freshservice specifics
- Consolidation
- Moving away from a standalone chatbot to a unified ITSM platform that includes native virtual agents (Freddy AI), eliminating duplicate tooling and reducing vendor count.
- Handoff archive
- Ada was used as a front-line deflection layer with handoffs to a separate helpdesk. Now the team wants all conversation history preserved as Freshservice tickets for unified reporting.
- Compliance
- Regulated industries (healthcare, financial services, government) need permanent conversation records stored in their system of record, not scattered across a conversational AI layer with a 12-month retention ceiling.
- Must migrate
- Conversations with handoffs (real support interactions), end-user records, compliance-sensitive transcripts
- Nice to have
- Fully automated conversations that resolved without human intervention
Don't move on until
- Record counts confirmed for tickets, contacts, organisations and macros
- Success criteria signed off by the support lead
- Freeze window provisionally booked with the business
02 Data Audit Find out what is actually in the data before you try to move it.
Objective A profiled, cleaned export with every quality defect either fixed at source or explicitly accepted.
Keep these open
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Take a full Ada export and profile it
Export to CSV or JSON and profile every file: row counts, null rates per column, distinct values, and type consistency. Compare row counts against the API totals from Discovery — a gap here means your export is silently truncated, usually by pagination.
Data Profiler Profile the Ada export for nulls, outliers and type drift -
Validate file structure before anyone writes a transform
Check delimiters, quoting, encoding (expect UTF-8, watch for BOMs and Latin-1), duplicate headers, and embedded newlines in ticket bodies. Ticket descriptions with raw newlines and commas break naive CSV parsers and silently shift columns.
A single unescaped quote in one ticket body can shift every subsequent column without any error.
CSV Validator Catch broken headers and ragged rows in the raw export -
Inventory PII and set retention
Scan for emails, phone numbers, payment card fragments, national IDs and anything else regulated in ticket bodies and custom fields — support tickets are where customers paste things they should not. Decide what gets migrated, masked, or dropped, and record the legal basis.
Ticket bodies and attachments routinely contain card and ID data that never appears in a structured field.
PII & Compliance Scanner Find regulated fields before they land in a new system -
Quantify duplicates, orphans and dead references
Count duplicate contacts (same email, different casing), tickets whose requester no longer exists, organisations with no members, and attachments whose parent ticket is gone. Fix these in Ada where you can — migrating them just moves the mess.
Data Cleaner Strip empty rows, stray whitespace and dead columns -
Clean and normalise the export
Trim whitespace, drop empty rows and columns, normalise casing on emails and tags, and standardise every timestamp to UTC ISO 8601. Timezone drift is invisible at load time and shows up weeks later as SLA reports nobody can reconcile.
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Produce a masked copy for sandbox work
Generate a realistic but fake version of the export for testing and for any vendor who needs sample data. Loading real customer PII into a sandbox is a breach in most jurisdictions, and sandboxes are rarely covered by your DPA.
PII Masker Generate a safe copy for sandbox and vendor testing -
Clean up staging resources
Delete temporary object storage (pre-signed URLs for attachments), revoke Ada API keys if decommissioning, and archive the crosswalk table for audit purposes.
Ada → Freshservice specifics
- Attachment audit
- Verify that files attached in Ada appear as Freshservice attachments; check file sizes and accessibility
Don't move on until
- Export parses cleanly with no ragged rows or encoding errors
- PII inventory complete and retention decisions recorded
- Duplicate and orphan records quantified and triaged
03 Field Mapping Turn two schemas into one signed-off mapping spec.
Objective A reviewed field-level mapping covering every object, with an explicit decision for every field that has no target.
Keep these open
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Generate the first-pass Ada → Freshservice field map
Start from an automated match on both schemas, then review every row by hand. Automated matching gets the obvious 70% right and is confidently wrong on the rest — especially anything named "type", "status" or "custom_field_1".
Schema Mapper Opens pre-loaded with the Ada → Freshservice field pair -
Map status, priority and channel values, not just field names
Enumerate every value in each picklist on both sides and map them explicitly. Value-level mismatches are the defect class that survives all the way to production because the field itself mapped fine — a ticket that should be "Pending" arriving as "Open" reopens SLA clocks.
Statuses with no target equivalent (on-hold, pending-customer) need a policy decision, not a best guess.
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Decide how custom fields land
Create the target custom fields first, matching type exactly (a dropdown mapped to free text can never be mapped back). Where Freshservice has no equivalent, decide between a new custom field, a tag, or a note appended to the ticket body — and record which.
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Resolve identity and threading
Decide how source IDs are preserved — most platforms will not let you set the primary key, so keep the original ID in a custom field. Without it, reconciliation becomes fuzzy matching and every future support question about an old ticket is unanswerable.
Losing the original ticket ID makes reconciliation and rollback effectively impossible.
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Plan attachments, inline images and threading order
Confirm size limits, allowed MIME types, and whether inline images survive as attachments or need rehosting. Decide the comment ordering and author attribution rules: comments loaded out of order, or all attributed to the API user, destroy the conversation history agents rely on.
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Freeze and sign off the mapping spec
Version the spec, walk the support lead through it row by row, and get explicit sign-off. Any change after this point goes through change control — mid-flight mapping edits are how partial loads happen.
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Set up requester custom fields
for Ada-specific identifiers (end_user_id, chatter_id).
Don't move on until
- Every source field is mapped, deliberately dropped, or parked in a custom field
- Status, priority and channel value maps agreed with the support lead
- Mapping spec version-controlled and signed off
04 Test Migration Prove the pipeline on a small, representative slice.
Objective A pilot load into a Freshservice sandbox that reconciles cleanly and has been reviewed by real agents.
Keep these open
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Stand up a Freshservice sandbox that matches production config
Create the custom fields, groups, brands, business hours and SLA policies first. A pilot into a default sandbox tests nothing, because the failures you care about are all configuration mismatches.
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Pick a deliberately nasty pilot sample
Take 500-1000 records chosen for difficulty, not convenience: the longest ticket threads, tickets with the most attachments, non-Latin character sets, merged and split tickets, deleted requesters, and every status value. A clean random sample proves only that easy records are easy.
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Run the load with masked data and instrument everything
Log every API request and response with its source record ID. When 40 records fail out of 10,000 you need to know exactly which ones and why, without re-running the whole batch.
PII Masker Never load real customer PII into a sandbox -
Measure real throughput against the rate limit
Record achieved records-per-hour under Freshservice's actual rate limits, including retries and backoff. Extrapolate to the full volume: if the maths says the full load exceeds your freeze window, you fix that now, not on cutover night.
Published rate limits are ceilings, not throughput. Assume real-world rates are meaningfully lower once retries and backoff are counted.
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Reconcile the pilot and triage every failure
Diff source against target on record counts and field-level values. Every discrepancy gets a root cause and a fix — "probably fine" at pilot scale becomes thousands of broken records at full scale.
Migration Validation Tool Diff the pilot batch against source before scaling up -
Put real agents in front of the pilot data
Have two or three agents work sample tickets end to end in the sandbox. They find the things reconciliation cannot see: unreadable threading, missing context, macros that no longer make sense. Fix the mapping, then re-run.
Ada → Freshservice specifics
- Provision a sandbox environment
- Freshservice sandbox is available on Pro and Enterprise plans. It mirrors your production configuration but operates independently. API behavior in sandbox matches production, including rate limits, making it reliable for migration dry runs. Starter and Growth plans do not include sandbox — use a separate trial instance instead.
Don't move on until
- Pilot batch reconciles to 100% on record counts
- Agents have reviewed sample tickets and confirmed they are workable
- Measured throughput extrapolates to a viable full-load window
05 Cutover Execute the switch inside a controlled, reversible window.
Objective All in-scope data live in Freshservice, agents working in the new system, and a rollback path that stayed available throughout.
Keep these open
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Pre-load history before the freeze
Load closed tickets and contacts days or weeks ahead while Ada stays live. Only open tickets and the final delta need to move inside the freeze — this is the single biggest lever on window length.
Helpdesk Migration Planner Size the freeze window from Freshservice's real API limits -
Publish the runbook with times, owners and abort criteria
A timed sequence: freeze start, final export, delta load, channel switch, smoke test, go/no-go, agent switch. Name who does each step and the explicit condition that triggers a rollback. Decide the abort criteria before the night, when nobody wants to be the one to call it.
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Freeze Ada and take the final delta
Stop new ticket creation, let agents finish in-flight replies, then export everything changed since the pre-load. Announce the freeze to the whole business, not just support — someone always tries to raise a ticket during it.
Tickets created during an unenforced freeze land in the old system and are the most common source of permanently lost data.
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Load the delta and open tickets
Run the delta load, then reconcile counts before touching any channel. Do not repoint email until the delta has verified — an inbound ticket arriving mid-load is far harder to untangle than a few extra minutes of freeze.
Migration Validation Tool Confirm the final delta landed before you reopen -
Repoint channels and verify with live traffic
Switch email forwarding and MX or connector settings, update chat widgets and web forms, and re-authorise integrations. Then send real test tickets through every channel and confirm each lands, routes and triggers the right automation.
Email forwarding changes can take up to a full DNS TTL to propagate — check the TTL days in advance and lower it if needed.
Cron Expression Builder Schedule the delta syncs that run through the freeze -
Run the go/no-go and switch the agents
Walk the exit criteria with the decision-maker, call it explicitly, then move agents over with a named person on hand for the first few hours. Keep Ada read-only rather than cancelled — cancelling the old contract on day one removes your only fallback.
Ada → Freshservice specifics
- Platform switch
- The team is replacing Ada with Freshservice's Freddy AI agent and needs historical data for analytics and compliance.
Don't move on until
- Full historical load complete and counts matched
- Inbound channels repointed and verified with live test tickets
- Rollback decision point passed explicitly, not by default
06 Validation Prove the migration is complete, then close it out.
Objective Documented evidence that data, workflow and reporting all survived, and a signed acceptance.
Keep these open
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Run the full reconciliation
Compare source and target on every object: total counts, counts by status, counts by group, attachment counts, and field-level spot checks on a random sample. Produce one report you can hand to an auditor.
Migration Validation Tool Reconcile Ada and Freshservice record-for-record -
Verify field completeness, not just record counts
Re-profile the loaded data and compare null rates per field against the source profile. Matching record counts with a field that silently arrived empty is the failure mode counts alone will never catch.
Data Profiler Prove field completeness held up through the load -
Rebuild reporting and compare against baselines
Recreate your core dashboards — volume, first response time, resolution time, CSAT — and compare to pre-migration figures for the same period. Explain every variance; a changed SLA calculation is a real finding, not a rounding error.
SLA and first-response metrics are usually recalculated from the loaded timestamps, so they will differ if any timestamp mapping was approximate.
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Test the workflow layer end to end
Fire every trigger, automation, SLA escalation, macro and notification with a live ticket. Workflow does not migrate — it gets rebuilt — so it is untested until someone has actually watched it run.
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Confirm compliance and produce the audit trail
Re-scan the loaded data for regulated fields, confirm retention and deletion policies are configured in Freshservice, and file the evidence with your PII decisions from the audit phase.
PII & Compliance Scanner Produce the compliance evidence your auditor will ask for -
Sign off, then decommission on a schedule
Get written acceptance against the Discovery success criteria. Keep Ada read-only for an agreed period (30-90 days is typical), take a final archive export, and only then cancel. Diarise the decommission date so it does not quietly renew.
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Record count
Total conversations extracted from Ada = total tickets created in Freshservice (query crosswalk table for exact counts)
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Rebuild automations
Ada's conversation flows, processes, and custom instructions have no equivalent in Freshservice. Recreate routing rules, SLA policies, and any handoff logic using Freshservice's Workflow Automator.
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Configure Freddy AI
If replacing Ada's AI with Freshservice's Freddy AI, train it on imported Solution Articles and configure agent assist features. Expect to rebuild conversational logic rather than import it (see capability gap table above).
Ada → Freshservice specifics
- Message count
- Sum of messages per conversation = sum of notes/replies per ticket
- Field spot-check
- Sample 50 tickets and verify custom field values match Ada variables
- Requester linkage
- Confirm every ticket has a valid requester, not a fallback default (query for tickets where requester matches anonymous catch-all — if >15%, investigate)
- Timestamp verification
- Compare created_at on 20 migrated tickets against Ada source data to confirm timestamps were preserved
- UI validation (UAT)
- Have agents log into Freshservice and read 20–30 migrated chat transcripts — check line breaks, images, HTML rendering, and chronological ordering of notes
Don't move on until
- Full reconciliation report attached to the project record
- Reporting baselines match pre-migration figures within agreed tolerance
- Formal acceptance signed and archive retention scheduled
Field mapping reference
The field-by-field mapping for each object. Use this as the starting point for your mapping spec.
Ada Conversations Freshservice Tickets
| Ada field | Freshservice field | Notes |
|---|---|---|
| conversation_id | custom_fields.ada_conversation_id | Store as custom text field for traceability |
| created | created_at | ISO 8601 — Freshservice accepts this on ticket creation via API when using an admin API key. Standard agent keys may silently ignore this field — test in sandbox first. |
| channel | custom_fields.source_channel | Map to dropdown: web, email, SMS, social |
| end_user_id | requester_id | Must create requester first, then reference ID |
| resolution | status | Map Ada resolution states to Freshservice statuses (Open=2, Pending=3, Resolved=4, Closed=5) |
| variables (global/meta) | custom_fields.* | Create matching custom fields in Freshservice before import |
| Handoff occurred | custom_fields.handoff | Boolean flag |
| tags | tags | Direct transfer if using simple string tags |
Object / Object /
| Ada field | Freshservice field | Notes |
|---|---|---|
| Chatter id / end_user_id | Requester id | Maintain crosswalk table; deduplicate on email before creation |
| Chatter email | Requester primary_email | Required for Freshservice ticket creation; primary dedup key |
| Conversation id | Ticket custom field ada_conversation_id | Freshservice auto-generates IDs; store Ada ID in custom field |
| Conversation created_at | Ticket created_at | Requires admin API key; standard keys silently ignore this field |
| Message Array | Ticket description or Notes | Parse JSON array into HTML; convert Markdown before POST |
| Answer title | Solution Article title | Direct mapping |
| Answer content | Solution Article description | Convert Markdown to HTML before POSTing |
| variables.channel | Custom field source_channel | Map web, email, SMS, social |
| variables.email | Requester primary_email | Used for requester dedup and creation |
| variables.name | Requester first_name + last_name | Split on space; handle single-name cases |
| Handoff metadata | Custom field handoff or private note | Useful for audit context and reporting |
| Message sender_type | Note private flag | Bot messages → private notes; user messages → public replies |
Risk matrix
Per-object risk for this pair. Plan extra validation around anything marked high.
| Object | Risk | Notes |
|---|---|---|
| Conversations (Tickets) | high | Ada conversations require full structural transformation into Freshservice tickets, with no native import path and complex field mapping logic required for every record. |
| Messages (Ticket Conversations) | high | Multi-turn message arrays containing mixed bot, end-user, and agent messages must be individually classified and converted into Freshservice public replies or private notes, with rich message types requiring HTML serialization. |
| End Users (Requesters) | medium | Most end-user identity data is recoverable from Ada conversation metavariables, but anonymous conversations representing 8–15% of records require a fallback generic requester to avoid API rejections. |
| Conversation Variables (Custom Fields) | medium | Ada's variable schema is unstructured and conversation-specific, requiring manual analysis to determine which variables warrant custom fields in Freshservice and whether their data types are compatible. |
| Tags | low | Ada string-based tags transfer directly to Freshservice ticket tags via the API with no transformation required, provided tag values are plain strings without special characters. |
| Knowledge Base (Solutions) | medium | Ada's Knowledge Hub content must be extracted and converted from Markdown to HTML before being loaded into Freshservice's three-tier Categories → Folders → Articles hierarchy via the Solutions API. |
| Timestamps and Audit History | high | Freshservice's API silently ignores the created_at field when using standard agent keys, meaning historical conversation timestamps may not be preserved unless an admin API key is used and tested in a sandbox environment first. |
| Historical Data (Pre-12 Months) | high | Ada's Data Export API enforces a hard 12-month retention window, making any conversation data older than that window permanently inaccessible via API unless a prior offline archive exists. |
| Bot Configuration (Answers, Processes) | high | Ada's bot configuration objects including Answers, Processes, and Instructions have no equivalent in Freshservice and cannot be migrated; they must be fully rebuilt as Freddy AI agent workflows. |
| Attachments and Media Files | medium | Files shared in Ada conversations can be migrated as Freshservice ticket attachments, but Freshservice enforces a 40 MB per-attachment ceiling, requiring pre-migration size validation and potential exclusion of oversized files. |
The hard parts
What makes this specific migration difficult, beyond the mechanics.
Conversation-to-Ticket Transformation
Every Ada conversation must be individually transformed into a Freshservice ticket, with multi-turn message arrays flattened into structured HTML or threaded ticket conversation entries, requiring significant custom transformation logic.
Ada 12-Month Data Ceiling
Ada's Data Export API only surfaces conversation and message data from the past 12 months, meaning any historical records older than that window are inaccessible via API and cannot be migrated without a prior data archive.
Anonymous Requester Resolution
An estimated 8–15% of Ada conversations lack email metadata, making them ineligible for direct Freshservice ticket creation since a valid requester email is a mandatory API field.
Variable-to-Custom-Field Mapping
Ada's global, meta, and local conversation variables must be individually mapped to pre-created Freshservice custom fields, requiring schema planning and field creation before any data load begins.
Dual-Side API Rate Limiting
Ada's Data Export API enforces a limit of 10 requests per second per endpoint with a 60-day query window, while Freshservice enforces 100–500 requests per minute depending on plan tier, requiring coordinated backoff logic on both sides.
Rich Message Format Flattening
Ada exports structured message types including carousel cards, quick replies, and tool call events that have no native equivalent in Freshservice ticket notes and must be serialized into readable HTML before import.
What breaks
Known failure modes. Have a recovery plan for each before you cut over.
HTML/Markdown rendering:
Ada supports Markdown in its chat UI. Freshservice tickets use a rich text editor (HTML). If you do not convert Markdown to HTML during the transformation phase, your tickets will display raw asterisks and hashes. Use markdown2 (Python) or marked (Node.js) for conversion.
Inline attachments:
Users often upload screenshots in Ada chat. You must download these files from Ada's URLs and upload them to Freshservice as multipart form data. This requires a two-step API process: create the ticket, then attach the file. Freshservice enforces a 40 MB attachment ceiling per file. Ada attachment URLs may expire — download to staging storage before beginning the Freshservice load phase.
Anonymous users:
Ada conversations without email or identifying metadata cannot be linked to a meaningful Freshservice requester. Create a catch-all requester (e.g., "Anonymous Web Visitor") or skip these conversations. Tag anonymous-sourced tickets for easy filtering.
Variable sprawl:
Ada allows unlimited variables per conversation. Freshservice custom fields are finite and plan-dependent (Starter: ~30, Growth: ~60, Pro: ~150, Enterprise: ~300 per ticket type). Audit which variables carry business value before creating fields — prioritize variables used in >5% of conversations.
12-month data cliff:
The Data Export API has date range limitations — a query's end date cannot be more than 60 days after its start date, and data is available only from the past 12 months. If you need older data, contact Ada support for a bulk export. Allow 2+ weeks lead time.
Duplicate end users:
Ada may create multiple end_user_id values for the same person across channels. After timeout, the next message creates a new conversation_id, and Ada also generates a new chatter_id/end_user_id pair for the session. Deduplicate on email before creating Freshservice requesters. Build an email → requester_id lookup table and resolve conflicts before the load phase.
Attachment staging:
If you use Freshservice's partner bulk migration APIs, attachments must be provided as publicly accessible URLs. If Ada files are private, you need a temporary object store (S3 with pre-signed URLs, expiry set to 72 hours) and a cleanup plan after migration completes.
Timestamp preservation:
Freshservice allows setting created_at on ticket creation via API, but only when using an admin-level API key. Standard agent API keys silently ignore the created_at parameter, resulting in all tickets showing the import date. Conversation notes do not support created_at override — notes are timestamped at creation time. To preserve chronological order, insert notes sequentially with brief delays between them. Test this behavior in sandbox before production migration.
Freshservice workflows during migration:
Ticket creation triggers automation rules, SLA timers, email notifications, and Slack/Teams integrations. Before bulk loading, either disable these automations temporarily or create a migration-specific group/category that bypasses workflow rules. Re-enable after migration and validation are complete.
Tools used in this playbook
All free, all run entirely in your browser — nothing is uploaded.
FAQ
Can I migrate Ada conversation history into Freshservice tickets?
Yes. Ada's Data Export API lets you extract conversations and messages as JSON. Each conversation maps to a Freshservice ticket, with individual messages becoming ticket notes or replies. You need to create requesters first, then tickets, then add conversation entries via the Freshservice API. Freshservice has no native 'chat' object, so transcripts must be parsed and formatted as HTML.
What are the API rate limits for both platforms during migration?
Ada's Data Export API allows 10 requests per second per endpoint, with a max page size of 10,000 records and 60-day query windows. Freshservice rate limits vary by plan: Starter gets 100 req/min, Growth 200, Pro 400, Enterprise 500. Migration partners can request up to 700/min. Both sides require exponential backoff on 429 responses.
How do Ada variables map to Freshservice?
Ada's global and metavariables (email, name, custom data) map to Freshservice custom ticket fields. You must create these custom fields in Freshservice before importing data. Not all variables carry business value — audit them first to avoid creating unnecessary fields.
Can I migrate Ada bot logic to Freshservice?
No. Ada's conversation flows, processes, decision trees, and AI agent instructions have no direct equivalent in Freshservice. If you're replacing Ada's AI layer, you need to rebuild automation logic using Freshservice's Workflow Automator and configure Freddy AI separately.
How do I handle anonymous Ada chatters in Freshservice?
Freshservice requires a Requester email for ticket creation. Assign a dummy email (e.g., anonymous@yourdomain.com) or map anonymous conversations to a generic 'Guest' Requester profile to prevent API rejections.