Coach by Lorikeet: Reframing How Analytics Measures AI Support in Regulated Industries

AI-driven customer support vaulted from convenience to critical infrastructure in a few short years. Where a human-to-human phone call once carried the full burden of accountability and nuance, today conversations often pass through language models, domain-specific assistants, and automated escalation logic. That shift promises scale and speed — and it also introduces a new class of measurement problems for regulated industries where compliance, auditability, and risk control are not optional.

Enter Coach, Lorikeet’s new self-service analytics agent. It is not merely a dashboard or a labeling tool. It is a purpose-built lens for translating conversational signals into auditable, actionable insights tailored to environments where a misstep can reverberate through legal, financial, and human consequences. For the analytics community, Coach raises a clear set of questions: how do we quantify the performance of AI support systems when stakes include both customer outcomes and regulatory obligations? And what does a mature measurement practice look like when it must serve engineers, compliance teams, and business leaders simultaneously?

The measurement gap in a regulated world

In many regulated sectors — banking, healthcare, insurance, and pharmaceuticals, among others — organizations have long relied on rigorous metrics, controls, and audits to maintain public trust. Those practices assumed human judgment as the central decision-maker, where errors were assessed case by case and accountability attached to identifiable actors. AI replaces or augments parts of that decision loop, producing decisions, suggestions, and texts at speed and scale. Typical observability stacks capture uptime and latency; customer experience tooling measures satisfaction and resolution rates. But for regulated oversight, those signals are insufficient.

We need evaluation frameworks that can demonstrate compliance, track drift in decision logic, quantify fairness across segments, and produce immutable evidence for audits. We need to know when an assistant recommended a procedure inconsistent with policy, and why. We need sampling that uncovers rare but consequential failures. We need a single pane of glass that ties conversational traces to policy checks, training data provenance, and operational metrics.

How Coach reframes analytics for AI support

Coach positions itself as a self-service analytics agent. That phrasing is important: the goal is to make complex evaluation workflows available on demand, without a prolonged ticketing process or bespoke engineering project. For analytics practitioners, that translates into three practical capabilities.

• Continuous, contextual evaluation: Coach ingests conversation logs and enriches them with contextual signals — intents, entity extraction, regulatory relevance flags, escalation triggers, and outcome labels. Rather than static sampling, it continuously evaluates streams and surfaces trends and anomalies tied to business-critical policies.
• Interpretability and traceability: Model outputs are linked to the prompts, policy checks, and training slices that influenced them. This linkage makes it possible to trace an adverse outcome back to a specific version of a model or a training cohort, a vital feature when producing audit trails.
• Actionable diagnostics: Beyond metrics, Coach synthesizes recommendations: where to tighten guardrails, what data to augment, which segments show performance degradation, and which regulatory checks are frequently failing.

What analytics teams can measure with Coach

At the core of Coach are measurement concepts that align with both operational performance and regulatory scrutiny. These include:

• Compliance coverage — percentage of conversations that passed required policy checks or were routed for human review when policy ambiguity was detected.
• Resolution accuracy — alignment between assistant recommendations and verified outcomes, measured across customer cohorts and product lines.
• Escalation fidelity — whether classification thresholds and rule triggers correctly escalate cases that require human intervention.
• Drift and concept shift — identification of linguistic or behavioral changes that degrade model performance over time.
• Fairness and segmentation analyses — disparate impact metrics across demographic or product cohorts where permitted by privacy constraints.
• Audit readiness — packaged evidence for a given time window: conversation transcripts, decision rationale, policy checks, and versioned model artifacts.

Each metric becomes more powerful when tied to sampling strategies that prioritize risk: large-value transactions, regulatory-critical topics, or low-confidence model outputs. Coach embeds such sampling logic, enabling analytics teams to focus their attention where it matters most.

Privacy, governance, and the art of safe telemetry

Collecting rich conversational signals in regulated contexts is fraught with governance challenges. Telemetry that improves measurement can also increase exposure. Coach is designed with several principles in mind that analytics teams should expect from any tool in this space.

• Minimized surface area: capture only the attributes necessary for evaluation. Sensitive identifiers are tokenized or redacted before ingestion.
• Provenance-first architecture: every analytic artifact links back to its source, with immutable timestamps and artifact hashes to support forensic review.
• Role-based access controls: analytic outputs can be scoped so that compliance officers, data scientists, and operational managers see only the views they need.
• Privacy-preserving analytics: where raw text cannot leave controlled environments, Coach can operate over synthetic or differential-privacy transformed data to maintain measurement fidelity while reducing leakage risk.

From metrics to governance: closing the operational loop

Analytics that simply report are useful; analytics that trigger better behavior change the organization. Coach frames its outputs as levers that feed back into development, training, and policy. A typical flow looks like this:

1. Coach identifies a rising trend in policy-check failures around a specific product language.
2. Automated alerts surface failing examples and associated model versions.
3. Model owners retrain or augment prompt guidelines; policy teams adjust rules to close gaps.
4. Post-deployment, Coach measures the remediation’s effect and quantifies residual risk.

This continuous loop reduces the time from detection to remediation and embeds data-driven governance into day-to-day operations. For regulated industries, the result is not just better customer support — it is demonstrable risk reduction backed by evidence.

Integration points for analytics platforms

Coach is engineered to sit within an ecosystem. Key integration expectations for analytics teams include:

• Ticketing and CRM: link conversational evaluations to customer records to measure downstream business outcomes like retention or chargebacks.
• Observability and logging: correlate model health signals (e.g., latency, rate limits) with conversation quality issues.
• Model registries and MLOps: tie performance regressions to model lineage and deployment metadata for fast rollbacks and A/B analysis.
• Business intelligence: export clean, auditable aggregates for board reporting and regulatory submissions.

Real-world scenarios: where Coach delivers value

Imagine the following scenarios that analytics teams encounter weekly:

• Banking: A conversational assistant recommends an overdraft workaround. Coach identifies the recommendation as a policy violation, surfaces similar prior examples, and quantifies the potential exposure in dollars and affected customer accounts.
• Healthcare: An assistant suggests a medication dosage. Coach cross-checks clinical policy, flags noncompliant suggestions, and routes ambiguous cases to clinicians while generating an audit trail for regulators.
• Insurance: An AI agent assigns claims to categories with varying compliance implications. Coach measures category accuracy and detects drift after a change in policy language.

In each case, the analytics value lies in converting free-form conversation into structured evidence that informs both immediate operational decisions and long-term governance strategies.

What this means for the analytics community

The release of self-service agents like Coach signals a maturing market in which measurement is being productized for regulated contexts. That has several implications for the analytics community.

• Standardization of measurement: When teams adopt similar evaluation frameworks, comparisons and industry benchmarks become possible. The community can move from bespoke, one-off audits to repeatable, comparable evaluations.
• Operational accountability: Measurement artifacts can travel upward — from operational squads to legal and audit bodies — reducing friction in cross-functional decision-making.
• Better tooling for risk prioritization: Data-driven sampling and automated auditing reduce the reliance on anecdote and intuition when allocating scarce review resources.

Challenges and the path forward

No analytics product fully solves the social and technical problems of AI governance. Several obstacles remain:

• Defining the right KPIs: Not every business or regulator will accept the same measures. Work remains in translating policy into operational metrics that are both meaningful and defensible.
• Data access constraints: Privacy and legal frameworks will limit the granularity of telemetry available for analysis. Techniques like federated evaluation and synthetic simulations will matter more.
• Human-machine handoffs: Designing clear escalation policies and measuring their fidelity requires deliberate cross-functional collaboration.

Nevertheless, the analytics community is well positioned to lead. By codifying evaluation patterns, sharing anonymized benchmarks, and advancing reproducible methodologies, practitioners can transform governance from a monthly checklist into a continuous, data-driven capability.

Conclusion: measurement as trust infrastructure

Coach is more than a product announcement. It is part of a broader evolution in how organizations measure the behavior of conversational AI in high-stakes settings. For analytics teams, that evolution offers a chance to design observability systems that serve both operational excellence and regulatory accountability.

When measurement is built with provenance, privacy, and governance at the center, it becomes the scaffolding for institutional trust. In regulated industries, that trust is the primary currency. Tools that convert ephemeral conversations into auditable, actionable insight will determine which organizations can scale AI responsibly — and which will be forced into expensive retrofits. Coach may not be the final answer, but it signals a practical, analytics-led path toward turning AI support from a risky innovation into a governed, measurable part of the enterprise.

For the analytics community, the invitation is clear: adopt rigorous measurement, demand traceability, and build systems that translate model outputs into repeatable evidence. The future of AI support in regulated industries depends on it.