Observe.AI

Observe.AI is a contact center intelligence platform that automates quality assurance, call coaching, and performance analytics. Founded in 2017 in San Francisco, the company has raised over $213M and serves customers in financial services, insurance, retail, and healthcare. The core product automates the scoring of 100% of calls against custom quality rubrics, surfaces coaching moments for supervisors, and handles after-call work automation. Observe.AI added real-time agent assist capabilities to compete with Cresta in the live coaching segment. Enterprise-only pricing, typical mid-size deployments in the $100K-$500K annual range.

Observe.AI started in 2017 in San Francisco with a focus on a specific, measurable problem: contact center quality assurance is expensive, slow, and based on too small a sample of calls to give managers an accurate picture of what is happening.

The company was founded by Swapnil Jain, Akul Penugonda, and Sharath Keshava Narayana. They raised a $54M Series B in 2021 and a $125M Series C in 2022, reaching a valuation over $1B. The customer base spans financial services, insurance, retail, healthcare, and BPO (business process outsourcing) operations.

The QA problem Observe.AI solves

Traditional contact center QA involves human reviewers. You hire QA analysts, give them a scoring rubric, and have them listen to a sample of calls. At most companies, this sample is 2-5% of total call volume. For a center handling 10,000 calls per week, that means reviewers are listening to 200-500 calls. The other 9,500 calls are invisible to management.

The sample bias issue is real: reviewers may consciously or unconsciously select calls that are easier to review, avoid difficult calls, or skew toward calls from certain agents. The feedback lag is also real: by the time a supervisor has a coaching conversation with an agent about a call, it is often a week or more later, well past the point where the feedback is tied to a recent memory.

Observe.AI addresses this by automating the scoring of every call against whatever framework you define. The rubric is yours: you specify which criteria matter, how they should be weighted, and what passing looks like. Observe.AI applies those criteria automatically to 100% of call volume.

The result is a complete picture rather than a sample. Managers can see individual agent performance with statistical significance rather than sampling noise. They can see which criteria agents struggle with most. They can measure the effect of training programs with before-and-after data that represents the whole call population.

The coaching workflow

Automated scoring is only useful if the insights reach people who can act on them. Observe.AI's coaching workflow is built to close that gap.

When automated scoring identifies a coaching moment, it is automatically surfaced to the agent's supervisor with the relevant call clip attached. The supervisor does not need to find the call in a recording system, navigate to the relevant moment, and manually prepare coaching feedback. The moment is packaged and delivered.

Supervisors can accept the coaching moment, add notes, and schedule a coaching session. Agents can see their own performance trends through a portal. Both sides have visibility into progress on specific criteria over time.

The practical impact is that supervisors spend less time on the administrative overhead of coaching and more time on the actual coaching conversations. In contact centers where supervisors manage 12-20 agents each, reducing administrative friction on coaching is meaningful.

Real-time assist features

Observe.AI added real-time agent assist capabilities to compete in the segment where Cresta has historically been stronger. The real-time product works similarly: transcribe the call as it happens, detect moments that warrant suggestions, surface relevant content to the agent during the call.

Honest assessment: the real-time features are functional but are newer than Cresta's. Teams for whom real-time guidance is the primary need should evaluate both products' current real-time implementations before deciding. Observe.AI's real-time product has improved significantly over the past 18 months, but Cresta has a head start in this specific capability.

Where Observe.AI is strong relative to competitors is in the combination of post-call analytics depth and coaching workflow tooling. The supervisor experience, the coaching moment management, and the performance dashboard design reflect years of iteration with QA and coaching teams specifically. That operational depth matters in day-to-day use.

Compliance monitoring

For regulated industries, compliance monitoring is a first-class feature. Financial services contact centers must ensure agents read required disclosures. Healthcare contact centers have HIPAA-relevant requirements. Insurance operations have state-specific compliance requirements.

Observe.AI can be configured to flag calls where required content was not delivered. When a call ends without the required disclosure statement, the system flags it automatically. This moves compliance monitoring from periodic audits (which have the same sampling problem as manual QA) to continuous monitoring of 100% of calls.

The false positive rate on compliance monitoring is something to calibrate carefully during implementation. Overly aggressive flagging creates noise that leads supervisors to ignore alerts. The calibration work is part of the implementation engagement and requires input from your compliance team on what constitutes a genuine violation versus edge cases.

Integrations

Observe.AI integrates with most major contact center platforms: Genesys, NICE CXone, Avaya, Talkdesk, Amazon Connect, and others. Telephony integration captures call audio for processing. CRM integrations with Salesforce, Zendesk, and ServiceNow push call summaries, scores, and outcomes back to agent workflow systems.

The CRM integrations are bi-directional in the more advanced configurations: Observe.AI pulls customer context from the CRM to inform analysis, and pushes call data back to the CRM for the agent's record of the interaction. This bi-directionality is more mature in Observe.AI than in some competitors and reduces the post-call work burden on agents.

Total cost and implementation reality

No published pricing. Enterprise deals, multi-year contracts. Typical entry-level for mid-size contact centers with 100-500 seats is in the $100K-$300K annual range. Larger deployments for 1,000+ seat operations scale accordingly.

Implementation timeline is typically 8-16 weeks. The biggest variable is how long QA rubric configuration and model calibration takes. Organizations with well-documented existing QA criteria and a dedicated project team on their side move faster. Organizations that need to first develop their quality framework from scratch take longer.

Professional services fees are typically separate from licensing fees. The first-year total including implementation is meaningfully higher than the annual licensing cost alone.

The ROI case to build: QA analyst labor savings from automated scoring, supervisor time savings from automated coaching moment identification, improvement in the performance metrics you care about (CSAT, FCR, AHT, conversion rate), and compliance risk reduction value. Contact centers with 200+ seats, high call volumes, and active QA programs tend to have a strong ROI story. Smaller operations need to be more careful about whether the cost is justified.

Key features

• Automated scoring of 100% of calls against custom quality frameworks
• Agent coaching workflows with auto-tagged coaching moments
• Real-time assist for agents during live calls
• Call summaries and after-call work automation
• Topic and sentiment analytics across full call population
• Compliance monitoring with automated flagging of required disclosures
• Bi-directional CRM integrations with Salesforce, Zendesk, and ServiceNow
• Custom rubric builder for QA scorecards

Pros and cons

Who is Observe.AI for?

• Financial services and insurance contact centers with compliance monitoring requirements
• Retail and e-commerce operations with high call volume needing full QA coverage
• Contact center managers building data-driven coaching programs at team scale

Alternatives to Observe.AI

If Observe.AI isn't quite the right fit, the closest alternatives are cresta-ai , talkdesk-ai , assemblyai , and deepgram . See our full Observe.AI alternatives page for side-by-side comparisons.