Use-case ambiguity
Teams automate what is easy to demo rather than what creates measurable business value.
Enterprise AI & Contact Center Advisory
CX AI Advisors
Make enterprise AI work before it meets your customers.
Independent guidance for AI contact centers, real-time conversational AI, and agentic workflows - from strategy and vendor selection through evaluations, commercial design, compliance, and production readiness.
Built by operators with experience across Meta, Salesforce, Five9, Genesys, and Uniphore.
Enterprise AI decisions are being made faster than they can be proven.
AI contact-center and agentic-platform decisions combine business process, model behavior, real-time infrastructure, enterprise integrations, security, and unfamiliar pricing models. A strong demo is not evidence that a system will perform reliably in production.
Vendor overload
Feature lists obscure material differences in architecture, workflow depth, and operational maturity.
Weak evaluations
Happy-path scripts miss hallucinations, tool failures, edge cases, interruptions, and recovery behavior.
Incomparable economics
Per-minute, per-conversation, per-resolution, token, platform, and services fees hide true cost.
Production risk
Reliability, observability, security, privacy, residency, and escalation are addressed too late.
Pilot-to-scale gap
A successful demonstration does not provide a rollout plan, governance model, or production acceptance bar.
Buyer-side expertise from decision to deployment.
Six focused offerings that help enterprise teams move from ambition to production with evidence.
AI Contact Center Strategy
Prioritize journeys where AI assists, automates, or hands off—and define how value will be measured.
View service →Vendor Selection & RFP
Run an auditable selection process that tests workflows, architecture, risk, and operating fit.
View service →AI Evaluations & Acceptance
Prove systems work across representative and difficult cases before production approval.
View service →Pricing & Commercial Advisory
Normalize vendor economics and align pricing units with successful customer outcomes.
View service →Agentic Workflow & Architecture
Design how agents retrieve context, call tools, recover from errors, and preserve human accountability.
View service →Production Readiness, Risk & Governance
Confirm the platform can operate reliably, safely, and economically under real conditions.
View service →A practical path from ambition to reliable outcomes.
- 1
Align
Prioritize use cases and define measurable outcomes
Example output: Use-case portfolio and business KPI tree
- 2
Select
Compare platforms using buyer-specific requirements
Example output: RFP, weighted scorecard, shortlist, and TCO model
- 3
Prove
Test real workflows, edge cases, and integrations
Example output: Eval dataset, rubric, test report, and acceptance thresholds
- 4
Scale
Design architecture, operations, and rollout
Example output: Production readiness plan and phased deployment roadmap
- 5
Govern
Monitor quality, economics, compliance, and change
Example output: Control framework, review cadence, and improvement backlog
Measure outcomes, not demo performance.
We help teams convert business intent into an evaluation system that can test vendors, approve pilots, and monitor production. The rubric connects customer outcomes with AI behavior, system performance, risk, and economics.
| Dimension | What to measure |
|---|---|
| Business outcome | Correct resolution, task completion, containment with resolution, conversion, effort, handle-time impact |
| AI quality | Accuracy, groundedness, hallucination rate, instruction adherence, reasoning consistency, policy compliance |
| Workflow execution | Tool-selection accuracy, parameter accuracy, API success, state management, retries, idempotency, downstream completion |
| Real-time experience | End-to-end latency, time to first audio, interruption detection, turn-taking, silence handling, transcription and synthesis quality |
| Human handoff | Transfer success, context preservation, routing accuracy, failure recovery, customer disclosure |
| Reliability | Availability, failover, graceful degradation, rate limits, capacity, observability, incident response |
| Security and compliance | PII/PCI handling, access controls, encryption, retention, auditability, data residency, consent, model and vendor risk |
| Economics | Cost per completed outcome, per-minute and per-conversation cost, token/tool usage, implementation cost, support and overage exposure |
Business outcome
Correct resolution, task completion, containment with resolution, conversion, effort, handle-time impact
AI quality
Accuracy, groundedness, hallucination rate, instruction adherence, reasoning consistency, policy compliance
Workflow execution
Tool-selection accuracy, parameter accuracy, API success, state management, retries, idempotency, downstream completion
Real-time experience
End-to-end latency, time to first audio, interruption detection, turn-taking, silence handling, transcription and synthesis quality
Human handoff
Transfer success, context preservation, routing accuracy, failure recovery, customer disclosure
Reliability
Availability, failover, graceful degradation, rate limits, capacity, observability, incident response
Security and compliance
PII/PCI handling, access controls, encryption, retention, auditability, data residency, consent, model and vendor risk
Economics
Cost per completed outcome, per-minute and per-conversation cost, token/tool usage, implementation cost, support and overage exposure
Senior operators, directly involved.
Advice is delivered by principals—not handed to a junior delivery team.
Umer Rabbani
Director of Product Agentic Applications
Umer is a product executive with a technical background and 13+ years of industry experience across enterprise SaaS, customer-service AI, voice AI, and cloud contact centers. His experience includes building and scaling platforms at Salesforce, Five9, Genesys, and Uniphore; designing AI evaluation frameworks; and taking enterprise voice and agentic workflows from use-case definition through pilot and production readiness.
Deepak Dutta
General Manager & Global Vice President
Deepak is an enterprise product and customer-engagement executive with a technical background and more than 25 years of industry experience. At Meta, he worked across Business Messaging, real-time communications, conversational AI, and agentic business experiences supporting interactions at global scale. His experience also includes leading enterprise AI application portfolios and connecting customer experience, messaging, data, and workflow execution.
Focused support for the decision in front of you.
Executive AI Readiness Diagnostic
A focused assessment of use cases, architecture, operating readiness, risks, and next decisions.
Vendor Selection & RFP Sprint
Requirements, RFP, scenario design, vendor scoring, references, commercial comparison, and recommendation.
Pilot & Evaluation Program
Evaluation dataset, rubrics, adversarial and edge-case testing, acceptance thresholds, and executive readout.
Fractional Buyer-Side Advisor
Ongoing support across architecture, vendor governance, rollout, executive decisions, and production performance.
Typical scope confirmed after discovery.
Insights
Practical writing on RFPs, evaluations, voice AI, and production readiness is on the way.
Insights are coming soon
We will publish original articles here. Until then, here are topics we are preparing—not published pieces:
- A Practical RFP for Enterprise AI Contact Centers
- How to Build an Evaluation Rubric for Voice AI
- Comparing the Real Cost of Agentic Customer Service Platforms
Make your next AI decision defensible.
Tell us what you are evaluating, where the program is blocked, and what decision your team needs to make next.