AI Voice HQ
Inbound · RestaurantsAI phone ordering that reaches the kitchen. Every call answered, the menu understood, modifiers confirmed, and accurate orders delivered straight to the POS.
CHANNELinbound · pstn
STATUSin production
Voice AI PlatformsAgentic WorkflowsProduction Operations
We build the quiet machinery behind agents that answer, reason, route, and finish the work.
Essence Analytics is an applied-AI company. We build and operate voice AI platforms — AI Voice HQ and OffHook — and work alongside operating teams to put agentic workflows into production, with the guardrails, evaluations, and human review paths that dependable systems require.
Home of AI Voice HQ & OffHook · Partnering with operating teams on agentic workflows
✺Agentic workflows✺Voice operations✺Evaluation systems✺Knowledge pipelines✺Human-in-the-loop UX✺Tool orchestration✺Production telemetry✺Platform engineering✺Agentic workflows✺Voice operations✺Evaluation systems✺Knowledge pipelines✺Human-in-the-loop UX✺Tool orchestration✺Production telemetry✺Platform engineering
§ I — The Platforms
Platforms we build,run, and answer for.
Essence Analytics is the home of two voice AI platforms. They are products, not projects — instrumented, evaluated, and improved in production. They are also proof: everything on this page runs these systems every day.
aivoicehq.com
OffHook
Outbound · CampaignsVoice campaigns run as operations — scheduling, qualifying, confirming, following up — with every call scored against your own definition of done.
CHANNELoutbound · campaigns
STATUSin production
The platforms and the partnerships feed each other: what we learn running these systems in production becomes the playbook we bring to your workflows — and every deployment sharpens the platforms.
§ II — Working Together
Systems thatcoordinate work,not just generate text.
Where we work with organizations: the operating layer where agents meet your tools, data, approval paths, customer conversations, and team habits. Four practices, threaded through every deployment — ours and yours.
Agentic workflow architecture
Map high-friction processes into reliable agent loops with clear ownership, permissions, fallbacks, and the operating targets a real team can defend.
- Workflow & decision map
- Tool / data access plan
- Guardrail & escalation matrix
- Operating-metric scorecard
Voice AI implementation
Production voice for support, sales, dispatch, scheduling, intake, and qualification — designed with the QA loops required to keep them dependable on day 90.
- Conversation design & flows
- Telephony + CRM wiring
- Realtime grading rubric
- Drift & regression watch
Operational AI enablement
Turn working prototypes into adopted systems through prompt governance, knowledge pipelines, analytics dashboards, playbooks, and team training.
- Prompt & policy registry
- Retrieval & ingestion pipeline
- Operator dashboards
- Playbooks & enablement
Evaluation & quality systems
The instrumentation that separates demos from production: graded test sets, regression suites, live evaluators, and the review rhythm operators actually run.
- Eval set construction
- LLM-as-judge calibration
- Regression CI for prompts
- Weekly review cadence
0162%Inbound calls answered without a human
023.4×Throughput on intake & qualification
03<8wkFrom scoping to first production loop
0494%Agreement between graded eval and ops review
§ III — Method
A method built aroundshipping useful systems.
- STEP01
Diagnose the work
Find the work that is repetitive, expensive, measurable, and ready for a better operating model. We pick one workflow, not ten.
DIAGNOSTIC - STEP02
Design the agent loop
Define agent responsibilities, tool access, knowledge sources, guardrails, and review points — drawn out before a single prompt is written.
DESIGN - STEP03
Ship a controlled pilot
Launch in a bounded workflow with real users, known success criteria, and short feedback cycles. Volume is gated by quality, not optimism.
PILOT - STEP04
Instrument and harden
Add evaluations, monitoring, exception handling, and the team routines that keep the system improving long after handover.
HARDEN
§ IV — House Principles
Six rules werefuse to break.
Workflow before model
We design the loop first. The model is a component inside a system, never the system itself.
Boring before novel
Reliable systems beat impressive demos. We use the most boring tool that meets the bar.
Measured before scaled
Nothing graduates from pilot without an operator-defined success metric and a regression suite behind it.
Human-in-the-loop, on purpose
We design the review surface as carefully as the agent. Humans are not a fallback — they are part of the product.
Owned, not rented
Prompts, evals, and ops live in your repo. We hand over working systems, not a vendor dependency.
One messy thing at a time
We refuse to boil the ocean. One workflow, shipped well, earns the right to the next one.
§ V — Begin