Leave one workflow and your number. The AI identifies itself, asks a few focused questions and prepares a structured brief.
Agent Factory is how DRING builds, tests and improves agents. Your calls and workflows become a working agent, hard conversations become tests, and live results decide each release.
Before its first real call, every agent runs 1,000 to 10,000 simulated conversations built for your company. After launch, every call is scored. The patterns become one proposed change, and it ships only after it passes the tests and you approve it.
You are not choosing a tool. You are choosing a team that works beside you through the first months. One workflow proves the model, live evidence decides when the next one is ready, and your AI staff grows from there, one tested release at a time.
Gartner expects a large share of AI agent projects to be canceled. Each cause below maps to a rule Agent Factory applies to every release. See the full comparison
A platform arrives with a login. Your team is expected to make it work alone.
Gartner: unclear business valueYou approve the scope and every release. A named DRING team makes each change and verifies it.
An agent goes live on thin data and a few test calls. Your customer becomes the test.
Gartner: poor data quality1,000 to 10,000 simulated conversations before launch, then a regression run on every release.
Vendors who never ran a queue miss handovers, peak hours, the angry third call.
Gartner: often misappliedAround 300K conversations a month across 9 countries show which change to make next.
We turn the way your operation works into something an agent can execute, measure and improve. Each stage ends with something your team can read, check and sign off.
We read the work as it really happens, across recordings, transcripts, workflows and approved sources, including the words and moments that change the outcome.
Voice, conversation logic, knowledge, tools and outcome handling are shaped around one job to be done.
1,000 to 10,000 simulated conversations built for your company, plus human review, try the hard cases before the agent meets a real caller.
Every live call is scored. The patterns become one proposed change, tested and released only with your approval.
Where you decide. You approve the scope before the build and every release before it goes live. DRING turns the evidence into a focused recommendation, then makes and verifies the change. The methods inside the Factory stay ours; the evidence behind every release is yours to see.
The same system builds every agent, so your tenth agent gets the same tests and release discipline as your first. A workflow brief and your knowledge go in; a tested agent and its live feedback come out.
Your operating knowledge becomes a clear brief for the first agent release.
Live conversations produce the evidence for the next controlled improvement.
Six steps take a workflow from a brief to live calls, and your first agent is live in a week. After that, the same steps repeat for every release.
We start with call evidence, workflow rules, approved knowledge, system access, languages and the KPI that defines success.
Conversation logic, voice, memory, tools and outcome handling are shaped around one clear operational job.
Simulations exercise interruptions, silence, objections, accents, missing data, policy boundaries and requests outside scope.
Automated checks, regression coverage and human beta review show whether the agent is ready for controlled traffic.
Your team sees the dashboard, tests the agent on the platform and gives structured feedback before a staged rollout.
Scored calls and team feedback become focused changes, new test cases and the next measurable release.
The Factory does not begin with a generic prompt. It begins with how your team already works and where the customer gets stuck.
Call reasons, branches, examples, business hours, retry rules and the cases that should never be automated.
Approved policies, product information, service language, transcripts and the source systems the agent is allowed to read.
CRM, calendar, ticketing, payments and telephony actions, each with explicit permissions and a visible result.
Resolution, qualified leads, booking completion, sentiment, handover accuracy or the operational measure that matters.
Every live call leaves a signal: callers repeating themselves, handovers coming too late, answers drifting from policy, a campaign losing pace. Here is the release from the scorecard above, seen through its KPI.
Refund callers on a support agent reached a person too late. The change, an earlier offer of a person, passed its tests and your approval. It then went out in stages, and the KPI was checked at each stage before the next. One change at a time keeps the agent improving without turning your live line into an experiment.
Explore call analyticsModel providers are components. Your advantage is the system that turns them into reliable, improving operations.
Generation is tied to tools, telephony, policies, test scenarios and human escalation from the start.
Every new agent and every major release moves through the same simulation, scoring and staged rollout discipline.
Live feedback becomes a prioritized change, not a forgotten transcript. Your team can see what changed and why.
A sales agent is tuned for qualified conversations, a support agent for resolution. The Factory does not confuse activity with outcome.
People keep the sensitive, ambiguous and high-value moments. The agent passes context so the customer does not start again.
Speech, language and voice models can be chosen per agent and use case, while the release loop stays the same.
A script is a starting point, not something the agent reads word for word to whoever is on the line. Emotion and need signals detected during the call change which path the conversation takes next.
No extra reassurance turns. The agent moves directly into handling the request the caller already stated.
Replies get shorter and a person is offered sooner, before the frustration has a chance to compound.
The agent asks what is actually needed instead of guessing and acting on the wrong one.
Discovery shortens and the agent moves toward booking, confirming or transferring while the interest is there.
Start with one workflow and expand when the live evidence says it is ready.
Order status, returns, warranty, account questions and after-hours coverage.
Support agentsDealer campaigns, dormant lead reactivation, discovery and meeting booking.
Sales agentsMultilingual intake, appointment workflows and careful coordinator handover.
Healthcare workflowsDriver lines, shipment status, check calls and exception-first dispatch support.
Logistics workflowsNo. The prompt or conversation logic is only one output. Agent Factory connects generation to simulation, scoring, telephony, tools, staged release and live KPI feedback.
No silent production changes. Feedback identifies a candidate improvement; the change is tested against the current release, reviewed and promoted through controlled rollout.
We need the workflow, call examples or recordings, approved knowledge, system access, escalation rules, languages and the outcome you want to improve. We help turn those inputs into the first release plan.
Yes. Handover to a person is designed into the workflow. The teammate who takes the call gets the caller's goal, the context and the reason for the handover.
Start with one queue and one KPI. Add another workflow when the first has a stable quality baseline, a clear owner and enough evidence to expand safely.
You see the agreed scope, test results, quality signals, the acceptance view, release notes and KPI movement for every release.
Send the form and DRING calls you in two minutes. Tell us the queue, the KPI and the workflow you want to improve, and we come back with a plan for your first agent.