The modern call center and helpdesk environment is living through an unprecedented contradiction. Customer expectations are compounding in order of magnitude, while the operating model behind most support organizations is still built on fragile human availability and legacy tools.
Attrition rates in contact centers already average above 50% in many markets, with Deloitte benchmarks showing 52% annual agent turnover in 2023, a level that is structurally unsustainable for any knowledge-heavy operation. I have been witnessing this in my last 20 years of career.
Each lost agent costs between $10,000 and $20,000 to replace when recruitment, onboarding, and training are factored in. At the same time, the average inbound phone contact costs around $7.16, more than 40% higher than a web chat, which means every extra minute of handling time eats directly into margin. On the customer side, language and immediacy are turning into non-negotiables. Studies show that around 68–76% of consumers prefer to interact with brands in their native language, and more than 40% refuse to buy from a company that does not support their language properly. Yet most support operations are constrained to a handful of languages and a patchwork of outsourced teams, freelancers, and BPO contracts. Staff fall ill, take vacations, churn, or move to different roles just when volumes spike. Internal helpdesks for employees face the same pattern, with IT, HR, and operations teams overwhelmed by repetitive questions, long ticket backlogs, and fragmented tools.
This is the environment where our startup and product NeoSentia enters as a multiplier rather than a simple automation layer.
NeoSentia’s proposition is straightforward and radical at the same time: an infinite digital workforce of AI human twins, trained once, that can absorb the knowledge, tone, and operational workflows of a support organization and keep working 24/7 in every language.
The economic model is usage-based instead of headcount-based.
At around 11 euros per million tokens, the cost of operating a NeoSentia twin agent, trainable in any topic and ready within two hours, becomes an order-of-magnitude cheaper than staffing equivalent human capacity across multiple shifts and languages, especially when you layer in recruitment, training, occupancy, overhead, and quality management.
“Dumb chatbots” versus Neosentia Human Digital Twin
The scientific core of NeoSentia is what truly differentiates it from “dumb bots.” Traditional chatbots rely on decision trees and fragile intents. They are brittle, easily confused, and impossible to scale across complex product catalogs or intricate internal policies. By contrast, NeoSentia is built as a retrieval-augmented intelligence system. Knowledge from documents, websites, learning portals, interaction logs, and product databases is converted into high-dimensional embeddings and stored in a vector database. When a customer or employee asks a question, the twin does not hallucinate from a generic model; it performs structured Retrieval-Augmented Generation (RAG), pulling the most relevant passages from this vector space and grounding every answer on curated, auditable knowledge.
This architecture addresses the central failure mode that enterprises fear in LLM usage: ungrounded answers. RAG, supported by a domain-specific vector store, constrains the model to the organization’s truth while still leveraging the generative capabilities of the underlying model. The result is an unparalleled combination of fluency and factual reliability. It is the difference between a scripted FAQ and a living, contextual expert.
NeoSentia goes further by modeling personality and culture as first-class citizens. Instead of building generic assistants, NeoSentia uses our Personality Models that learn from books, training materials, leadership content, call transcripts, and even video transcripts to encode how an organization speaks, decides, and cares. This personality layer is also embedded in a vector space alongside knowledge. When the twin responds, it retrieves not just the “what” from knowledge documents, but also the “how” from personality and culture examples. That is why a NeoSentia twin for a bank sounds different from a twin for a gaming brand or a healthcare provider, even when they answer similar questions. It is also why a NeoSentia twin can behave like a specific leader, team, or brand archetype without needing task-specific hardcoding.
For call centers and helpdesk organizations, this has profound implications. A NeoSentia twin can sit at the front line as a customer-facing agent handling high-volume, low-complexity interactions across all channels—web, chat, email, messaging, and even voice. At the same time, the same twin can operate as an internal IT or HR helpdesk, resolving routine questions about tools, access, benefits, or policies, freeing human teams to focus on escalations and complex cases. In both scenarios, the twin uses the same RAG engine and vector knowledge base, which means every policy update, new product, or procedure change instantly propagates across the entire digital workforce without retraining hundreds of humans.
NeoSentia is also a powerful onboarding instrument. New joiners can be onboarded by interacting with a digital twin of the organization’s best support leader, top agent, or key subject-matter expert. Instead of reading static manuals, they can ask practical questions, simulate scenarios, and observe best-practice decision patterns captured from real historical behaviors. Leaders can “encode” their judgment and leadership style in a twin, offering an always-available mentor to new managers and team leads. This turns knowledge transfer from an exhausting human burden into a scalable, always-on capability.
Because NeoSentia is agentic, it does not stop at answering questions. These twins can be plugged into workflows: creating or updating tickets in your ITSM or CRM system, escalating to human agents with full context, scheduling callbacks, triggering quality checks, updating knowledge articles when they detect repeated confusion, or orchestrating multi-step resolutions that span several systems.
Gartner already projects that agentic AI will autonomously resolve around 80% of common customer service issues by 2029 and reduce operational costs by about 30%. NeoSentia is explicitly designed to ride this wave, turning “answering” into “acting.”
The insight layer is where NeoSentia becomes an unparalleled strategic asset. Every interaction is logged as structured data: question, intent, missing answer, sentiment, product, language, resolution path, and handoff behavior. You do not just get transcripts; you get a continuously updating map of what your customers and employees are actually trying to do. When customers repeatedly ask for a feature you do not offer and the twin replies “we don’t have that yet,” this becomes quantified demand data. When internal employees ask the same IT question thousands of times, you see exactly where workflows and tools are broken. When multilingual interactions spike in specific regions, you understand where to invest in localization.
Studies already show that introducing multilingual support can increase customer satisfaction by 30% and has significant revenue impact.
NeoSentia gives you the raw intelligence to move from anecdote to quantified strategy.
On core KPIs, the impact compounds. Average handling time falls because knowledge retrieval is instantaneous and answers are synthesized in seconds. First-contact resolution rises as grounded, context-aware responses eliminate unnecessary transfers and callbacks. Cost per contact decreases as AI agents absorb a growing share of volume at a fraction of the labor cost. Forecasts from multiple industry sources suggest that up to 95% of interactions could be touched by AI by the middle of this decade, and early adopters are already reporting several-times ROI on AI service investments. Customer satisfaction improves when wait times disappear and language barriers dissolve. And for internal helpdesks, ticket backlogs shrink, while employee experience scores improve because the friction of “I don’t know who to ask” vanishes.
NeoSentia integrates seamlessly with the direction described in your own work on the future of freelancing and cognitive twins: human talent transitioning from doing repetitive tasks to training, supervising, and evolving digital twins that carry their expertise into infinite scale. Agents and freelancers no longer fear replacement; they become orchestrators of a digital workforce that reflects their best practices and frees them to focus on complex empathy, negotiation, and imagination.
The call to action for any call center or helpdesk-driven organization is simple: experiment early, learn fast, and treat NeoSentia as a strategic pillar rather than a side project. Start with a focused use case, such as a multilingual support twin for a specific product line or an internal IT twin for common access and password issues. Connect it to your knowledge sources, ticketing systems, and reporting stack. Watch how the digital twin learns, how agent workloads shift, how insights begin to surface from the interaction data. Then scale, not by hiring, but by replicating and adapting twins across business units, geographies, and functions.
The contact center of the next decade will not be defined by rows of headsets. It will be defined by a hyper-personalized, vector-driven, agentic AI workforce that embodies the company’s knowledge, judgment, and culture.
NeoSentia is built precisely for that world: an infinite workforce, trained once, retrievable on demand, relentlessly available, and continuously improving. The organizations that step into this shift now will lead the unfolding era of digital labor; the ones that hesitate will find themselves constrained by a model that no longer matches the speed, scale, and complexity of their customers’ reality.
MN Consulting Services 