AI agents that resolve customer issues, not just route them.
AI agents that resolve customer issues, not just route them.
AI agents that resolve customer issues, not just route them.
Every pilot stalls in the same place. The agent handles easy questions and routes the rest. Inbenta Encore resolves the complex, multi-step interactions others skip, inside governance your compliance team will approve.
Your current AI stack was built for a simpler problem.
Your current AI stack was built for a simpler problem.
Your current AI stack was built for a simpler problem.
The first wave of virtual agents was designed for FAQ deflection. The second wave, LLM wrappers, was designed for fluent responses. Neither was designed for what enterprise CX now has to deliver: multi-step resolution across fragmented systems, in regulated environments, without hallucination risk.
That is why pilot purgatory keeps repeating. The tool works in the demo. It works in a narrow proof of concept. It fails the moment a customer asks a question the scripts did not anticipate or the system of record the agent needs to reach sits behind an integration nobody scoped. The problem is not effort. The problem is that the architecture was built for a simpler problem than the one you are trying to solve.
Rule-based bots hit a ceiling. LLM wrappers hit a wall.
Rule-based bots hit a ceiling. LLM wrappers hit a wall.
Rule-based bots hit a ceiling. LLM wrappers hit a wall.
A rule-based virtual agent follows a decision tree. Every branch is pre-scripted, which means every unanticipated phrasing breaks the flow. Teams spend months writing intents and still watch containment plateau.
An LLM wrapper generates answers at runtime. It handles phrasing variation beautifully and hallucinates confidently. The same architecture that makes it feel intelligent makes it unsafe for anything that touches regulated data, account actions, or customer commitments.
The AI agent vs chatbot distinction is structural, not stylistic. A chatbot responds. An AI agent receives a goal, determines the path, retrieves governed data from connected systems, and executes. Encore is the second kind.
Encore generates governed, pre-validated intents at ingestion time through Knowledge Engineering. At runtime, Programmed Intelligence, powered by Encore's dual-LLM architecture, matches customer intent and returns the pre-approved answer or executes the next action through 850+ prebuilt integrations. The LLM is never the answer engine; it is the orchestration and enrichment layer.
That architecture is what makes AI agent orchestration reliable at enterprise scale:
- Source content is ingested and converted into governed, ready-to-deploy intents in 30 to 60 minutes
- Encore is model-agnostic, so you can use your preferred LLM provider or switch as the market changes
- 850+ prebuilt integrations plug Encore into Genesys, Salesforce, IBM, and other legacy stacks without rip and replace
- Deployment is +75% faster than alternatives, with production go-live in days to weeks, not months
The buyer who has sat through pilot purgatory recognizes what is different: this is not a platform that needs six months of intent scripting before it does anything useful.
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Self-service rate collapses if the AI gives wrong answers. Containment collapses. Cost reduction collapses. Every downstream metric depends on the answer being correct.
Encore delivers +98% accuracy through architecture, not training volume. Programmed Intelligence is deterministic-first: every response traces to a governed source rather than being generated probabilistically at runtime. Accuracy is not a feature layered on top; it is foundational to how the system works. The downstream outcomes follow: +35% better first-contact resolution, +30% CSAT improvement, +50% overhead cost reduction.
Teams evaluating AI customer service agent platforms should treat anything below this accuracy bar as disqualifying. A fast wrong answer is worse than a slow right one.
See Encore handle your highest-volume interactions end to end. Schedule a demo.
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Safe means the AI will not cause harm. Auditable means you can prove to a regulator, or to your own CISO, CRO, or compliance lead with veto authority on AI deployment, what the assistant said, why it said it, and where the data came from. In financial services, travel, online gambling and gaming, and B2B SaaS with regulated customers, auditability is the condition of deployment, not a feature request.
Encore was built for that environment:
- Full audit trail of every interaction, source consulted, and action taken
- Role-based access controls so compliance, CX, and IT teams work within their scope
- Configurable data residency to meet regional regulatory requirements
- Explainable responses via Programmed Intelligence, traceable to the governed source that produced them
This is what separates enterprise-ready AI agent software from tools that look impressive in a sandbox and trip compliance review three months into the deal.
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Glass box architecture: every decision is traceable
Enterprise results, not vendor promises
Production outcomes from Inbenta customers running complex, regulated CX operations:
M&T Bank
Nationwide Building Society
Neoenergia
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Most demos are generic walkthroughs. This one is specific to your stack, your use case, and the governance requirements your compliance team will review. Most customers go from ingestion to live agents in days, not months.
The architecture decisions enterprises make this year will lock in for the next several. Getting the decision right matters more now than it did last year.
Frequently asked questions about AI agents
Frequently asked questions about AI agents
Frequently asked questions about AI agents
AI agents are autonomous systems that receive a goal, determine the execution path, and resolve multi-step interactions without pre-scripted decision trees. Encore delivers this with +98% accuracy via Programmed Intelligence, grounded in governed knowledge via Knowledge Engineering rather than runtime generation, and executes actions through 850+ prebuilt enterprise integrations.
A chatbot follows a script. It matches keywords, runs decision trees, and breaks on phrasing the script did not anticipate. An AI agent receives a goal, determines the path, retrieves live data from connected systems, and executes. Encore is the platform that makes the second kind work in production, with the governance and auditability regulated enterprises require.
Encore generates governed, pre-validated intents at ingestion time through Knowledge Engineering. At runtime, Programmed Intelligence, powered by Encore's dual-LLM architecture, matches intent and returns the pre-approved answer. Every response traces to a governed source, which is why +98% accuracy holds in production rather than in demos.
Encore ships with 850+ prebuilt enterprise integrations, including Genesys, Salesforce, IBM, and other legacy CCaaS and CRM stacks. Encore sits on top of the systems you already run rather than replacing them. No rip and replace, no parallel infrastructure, no forced migration before you can deploy.
Source content is ingested and converted into governed intents in 30 to 60 minutes. Full deployment is +75% faster than alternatives, with production go-live in days to weeks, not months. Programmed Intelligence understands intent from day one, so there is no training phase before real customer traffic can flow through the system.
Encore uses consumption-based pricing with no lock-in, and your data stays under your control. ROI shows up in resolution rate, cost per interaction, and agent capacity rather than per-seat licensing. Customers report +50% overhead cost reduction, and M&T Bank's $2M+ saved is a reference point for what this looks like at scale.
Yes. Encore supports 90+ languages, 35+ natively, via Programmed Intelligence. Every language draws from the same governed Knowledge Engineering layer, so accuracy and auditability hold across markets. Global Inbenta deployments run multilingual support at scale on this architecture without translation drift between regions.
For enterprises shaping customer experience.
