Get Your Data
Observe.AI - How to Get Your Conversation Data
A practical guide to getting your contact center interaction data out of Observe.AI - covering REST API access, interaction and evaluation data extraction, incremental polling, coaching analytics, and how to route structured data into your downstream systems.
What you'll learn
- What interaction data you can extract from Observe.AI - transcripts, evaluations, agent performance, and coaching insights
- How to access data via the Observe.AI Reporting API - authentication, endpoints, and pagination
- Three extraction patterns: historical backfill, incremental polling, and event-driven
- How to connect Observe.AI data pipelines to Zapier, n8n, and Make
- Advanced use cases - compliance auditing, agent coaching, QA dashboards, and workforce analytics
Data
What Data You Can Extract From Observe.AI
Observe.AI captures far more than just the recording. Every contact center interaction produces a set of structured assets that can be extracted via the Reporting API - the transcript itself, QA evaluation scores, agent performance metrics, sentiment analysis, coaching moments, and contextual metadata about the interaction.
Common fields teams care about
API Access
How to Get Data via the Observe.AI Reporting API
Observe.AI exposes interaction and evaluation data through REST Reporting APIs. The workflow is: authenticate with an API key or token, list interactions by date range, then fetch transcripts, evaluations, and coaching data for each interaction.
Authenticate
Observe.AI uses API key or token-based authentication issued by your Observe.AI admin. Pass the token in the Authorization header as a Bearer token on every request.
Authorization: Bearer <your_api_token> Content-Type: application/json
List interactions by date range
Call the interactions endpoint with date range filters. Results are paginated - each response includes a cursor or offset to fetch the next page. Filter by team, agent, or interaction type to narrow results.
GET https://api.observe.ai/v1/interactions
?start_date=2025-01-01T00:00:00Z
&end_date=2025-02-01T00:00:00Z
&limit=100
&offset=0The response returns an array of interaction objects with id, agent_id, duration, timestamp, and metadata. Keep paginating by incrementing offset until the result set is empty.
Fetch transcript and evaluation data
For each interaction ID, request the transcript and evaluation data. The transcript contains speaker-labeled utterances with timestamps. The evaluation endpoint returns QA scores, individual criteria results, and coaching flags.
GET https://api.observe.ai/v1/interactions/{interaction_id}/transcript
GET https://api.observe.ai/v1/interactions/{interaction_id}/evaluationThe transcript response includes speaker-labeled utterances with speaker_role (agent/customer), start_time, end_time, and text. The evaluation response includes overall_score, criteria[] with individual scores, and coaching_moments[].
Handle rate limits and processing delays
Rate limits
Observe.AI enforces per-endpoint rate limits. When you receive a 429 response, back off using exponential delay. For bulk operations, pace requests to stay within published limits - especially important for contact centers processing thousands of interactions daily.
Processing timing
Transcripts and evaluations are not available the instant a call ends. Observe.AI processes recordings asynchronously - transcription, sentiment analysis, and QA scoring all run in sequence. Typical lag is minutes to hours. Build a buffer into your extraction timing or implement a retry with exponential backoff for recently completed interactions.
Patterns
Key Extraction Flows
There are three practical patterns for getting interaction data out of Observe.AI. The right choice depends on whether you're doing a one-off migration, running ongoing extraction, or need near real-time processing of contact center interactions.
Backfill (Historical Export)
One-off migration of past interactions
Define your date range - typically 3-6 months of historical interactions, or all available data if migrating. Contact centers generate high volumes, so scope carefully
Call the interactions endpoint with start_date and end_date filters. Paginate through the full result set using offset, collecting all interaction IDs
For each interaction ID, fetch the transcript and evaluation data. Pace requests at 1-2 per second to stay within rate limits
Store each transcript with its evaluation data and metadata (interaction ID, agent, team, duration, disposition) in your data warehouse or object store
Once the backfill completes, run your analysis pipeline against the stored data in bulk
Incremental Polling
Ongoing extraction on a schedule
Set a cron job or scheduled trigger (every 30 minutes, hourly, etc.) that runs your extraction script. Contact center volumes often justify more frequent polling than sales tools
On each run, call the interactions endpoint with start_date set to your last successful poll timestamp. Filter by status to only fetch fully processed interactions
Fetch transcripts and evaluation data for any new interaction IDs returned. Use the interaction ID as a deduplication key to avoid reprocessing
Route each transcript, evaluation, and metadata to your downstream pipeline - analysis tool, warehouse, or automation platform
Update your stored timestamp to the current run time for the next poll cycle
Event-Driven (Webhook / Notification)
Near real-time on interaction processing
Check with your Observe.AI admin whether webhook or event notification is available on your enterprise plan. Configuration varies by account
If available, register a webhook endpoint to receive events when interactions are fully processed (transcribed, evaluated, and scored)
When the event fires, parse the payload to extract the interaction ID, agent info, and initial metadata
Fetch the full transcript and evaluation data via the API using the interaction ID from the event, then route downstream
Automation
Send Observe.AI Data to Automation Tools
Once you can extract interaction data from Observe.AI, the next step is routing it through Semarize for structured analysis and into your downstream systems. Below are end-to-end example flows - each showing the full pipeline from Observe.AI extraction through Semarize evaluation to your CRM, Slack, or database output.
Observe.AI → Zapier → Semarize → CRM
Poll Observe.AI for new interactions on a schedule, fetch the transcript and evaluation data, send it to Semarize for structured analysis, then write the scored output - signals, flags, and coaching recommendations - directly to your CRM or workforce management system.
Setup steps
Create a new Zap. Choose "Schedule by Zapier" as the trigger and set the interval to hourly. This polls for new interactions since Observe.AI doesn't have a native Zapier trigger.
Add a "Webhooks by Zapier" Action (Custom Request) to list new interactions from Observe.AI. Set method to GET, URL to the interactions endpoint, add your Bearer auth header, and pass start_date as your last poll timestamp.
Add a Looping by Zapier step to iterate through each interaction. Inside the loop, add another Webhooks action to fetch the transcript for each interaction ID.
Add a Webhooks by Zapier Action to send the transcript to Semarize. Set method to POST, URL to https://api.semarize.com/v1/runs. Add your Semarize API key as a Bearer token. Set kit_code, mode to "sync", and map the transcript text into input.transcript.
Add a Formatter step to extract individual brick values from the Semarize JSON response - compliance_score, agent_qa_score, coaching_flag, etc.
Add a Salesforce (or HubSpot, Sheets, etc.) Action to write the extracted scores and signals to your CRM or workforce management record.
Test each step end-to-end, then turn on the Zap.
Observe.AI → n8n → Semarize → Database
Poll Observe.AI for new interactions on a schedule, fetch transcripts and evaluation data, send each one to Semarize for structured analysis, then write the scored output to your database. n8n's native loop support handles pagination and high-volume batch processing efficiently.
Setup steps
Add a Cron node as the workflow trigger. Set the interval to 30 minutes - contact centers benefit from more frequent polling due to higher interaction volumes.
Add an HTTP Request node to list new interactions from Observe.AI. Set method to GET, URL to the interactions endpoint, configure Bearer auth, and set start_date to one interval ago.
Add a Split In Batches node to iterate over the returned interaction IDs. Inside the loop, add an HTTP Request node to fetch each transcript via the transcript endpoint.
Add a Code node (JavaScript) to reassemble the utterances array into a single transcript string. Join each utterance's text, prefixed by speaker role (Agent/Customer).
Add another HTTP Request node to send the transcript to Semarize. Set method to POST, URL to https://api.semarize.com/v1/runs. Add your API key as a Bearer token. Set kit_code, mode to "sync", and map the transcript into input.transcript.
Add a Code node to extract the brick values from the Semarize response - qa_score, compliance_flag, coaching_action, evidence, confidence.
Add a Postgres (or MySQL / HTTP Request) node to write the structured output. Use interaction_id as the primary key for upserts.
Activate the workflow. Monitor the first few runs to verify data is flowing correctly through all nodes.
Observe.AI → Make → Semarize → CRM + Slack
Fetch new Observe.AI interactions on a schedule, send each to Semarize for structured analysis, then use a Router to branch the scored output - alert on compliance violations via Slack and write all QA signals to your CRM or workforce management system.
Setup steps
Create a new Scenario. Add a Schedule module as the trigger, set to your desired interval (30-60 minutes is typical for contact center volumes).
Add an HTTP module to list new interactions from Observe.AI. Set method to GET, URL to the interactions endpoint, configure Bearer auth, and filter by start_date since the last run.
Add an Iterator module to loop through each interaction. For each, add an HTTP module to fetch the transcript via the transcript endpoint.
Add another HTTP module to send the transcript to Semarize. Set URL to https://api.semarize.com/v1/runs, add your Bearer token, and set kit_code, mode to "sync", and input.transcript from the previous step. Parse the response as JSON.
Add a Router module. Define Branch 1 with a filter: bricks.compliance_flag.value equals true. Leave Branch 2 as a fallthrough (no filter).
On Branch 1, add a Slack module to alert your QA team when a compliance violation is detected. Map the QA score, compliance flag, and interaction ID into the message.
On Branch 2, add a Salesforce module to write all brick values (qa_score, compliance_flag, coaching_action) to the appropriate record.
Set the scenario schedule and activate. Monitor the first few runs in Make's execution log.
What you can build
What You Can Do With Observe.AI Data in Semarize
Semarize unlocks custom compliance grounding, cross-team benchmarking, data-driven coaching analysis, and the ability to build your own tools on structured contact center signals.
Custom Regulatory Framework Scoring
Your Rules, Your Timeline
What Semarize generates
Regulatory requirements change quarterly, and your compliance scoring needs to keep pace. Pull interaction transcripts from Observe.AI and run them through your own compliance kit in Semarize. You define the exact regulatory phrases, disclosure requirements, and prohibited language for YOUR jurisdiction. When TCPA requirements change in March, you update your Semarize kit the same week — scoring stays current on your timeline, not anyone else’s. Every call gets scored against your current policy, and the structured output feeds directly into your compliance reporting database.
Learn more about QA & ComplianceCross-Platform Unified Quality Framework
One Framework, Every Channel
What Semarize generates
Your contact centre handles phone calls through one platform, chat interactions through Zendesk, and video support through Zoom. Each channel has its own quality tool - none of them score interactions the same way. Run transcripts from every platform through the same Semarize evaluation kit. Every agent gets a unified quality score regardless of channel, scored against the same rubric with the same weights. When you discover that Agent A scores 85 on phone but 52 on chat, the coaching conversation is specific: their verbal empathy is strong but written empathy needs work. One framework, one scoring system, every channel - with structured output you own in your warehouse.
Learn more about Data ScienceProduct Knowledge Gap Detection
Grounded Accuracy Verification
What Semarize generates
Run a knowledge-grounded kit against your product documentation and policy handbook on every interaction. Semarize checks each agent’s statements against the source of truth: was the refund policy quoted correctly? Did they cite the right warranty terms? Is the troubleshooting sequence they walked through still current? After scoring 5,000 interactions, the data shows 18 agents consistently misstate the refund policy — and 7 agents reference a warranty extension programme that ended last quarter. Training targets the exact knowledge gap and the specific document section instead of running generic refreshers.
Learn more about QA & ComplianceCustom QA Reporting Pipeline
Queryable, Joinable Data
What Semarize generates
A workforce analytics manager vibe-codes a Power BI dashboard that pulls Semarize structured output from every Observe.AI interaction. The dashboard joins conversation quality scores with workforce management data — schedule adherence, handle time, and CSAT. It reveals that agents with high empathy_score AND low handle_time don’t exist: the best agents spend more time. Management adjusts AHT targets for high-empathy agents. Customer satisfaction increases 8% in the next quarter — because the data was queryable, joinable, and fully under your control.
Learn more about RevOpsWatch out for
Common Challenges & Gotchas
These are the issues that come up most often when teams start extracting interaction data from Observe.AI at scale.
Enterprise-gated API access
The Observe.AI Reporting API is not available on all plans. API access requires an enterprise subscription and explicit enablement by your account team. Confirm your plan includes API access before building integrations.
Processing delay on interaction data
Observe.AI processes recordings asynchronously - transcription, sentiment analysis, and QA evaluation all happen after the call ends. Attempting to fetch interaction data too soon will return incomplete or missing results. Build in a delay or retry mechanism.
API rate limits and throttling
The API enforces rate limits that can be restrictive for high-volume contact centers. Implement exponential backoff and pace bulk operations to avoid hitting ceilings, especially during historical backfills of thousands of interactions.
Pagination across large result sets
Contact centers generate far more interactions per day than typical sales tools. Interaction listing endpoints return paginated results - track your cursor position carefully. Losing a cursor mid-backfill on a 10,000+ interaction dataset means re-scanning from the start.
Evaluation data availability timing
QA evaluations and coaching scores may not be available at the same time as the base transcript. Evaluations often require additional processing time or manual review steps. Design your extraction flow to handle partial data and backfill evaluation scores when they become available.
Speaker identification in multi-party calls
Conference calls, transfers, and multi-agent interactions can produce speaker label inconsistencies. When a call is transferred between agents, the new agent may not be properly identified. Validate speaker labels before using them for per-agent performance analysis.
Duplicate processing in high-volume environments
Without idempotency checks, re-running an extraction flow can process the same interaction twice. Use interaction IDs as deduplication keys. In contact centers processing thousands of calls daily, duplicates compound quickly and skew analytics.
FAQ
Frequently Asked Questions
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