Presented by Salesforce
Smarsh, a global provider of cloud-native, AI-driven solutions that capture, archive, and analyze communications data and intelligence for highly regulated industries, set an ambitious goal: use AI to scale its workforce and increase productivity by 30%. But its customer service team had already identified the real challenge — customers were navigating a maze of products, documentation, and compliance requirements.
The solution wasn’t just more automation. It was a single, intelligent entry point into support.
"At the team level we asked ourselves, how can we become a better support organization for our regulated industry customers given that we keep on acquiring companies and have so many products to support?" says Rohit Khanna, Smarsh chief customer officer. "How do we harness the knowledge we have internally and present that to these customers in a way that makes our teams more efficient, and customer service more effective?"
In practice, that meant building an intelligent, human-centric “front door” trained on Smarsh’s proprietary knowledge. The system centralizes the support journey, distilling complex AI infrastructure into a simple, practical experience. Customers bypass complex navigation trees and describe what they need in plain language, and the AI directs them to the right solution — reducing the friction of traditional self-service.
Archie, the Smarsh AI support agent
Smarsh named its AI support agent "Archie." While many AI initiatives stall during the last mile — the difficult transition from a successful pilot to a durable, production-scale operation — Smarsh avoided this by building on a deeply unified platform. The company chose Salesforce’s Agentforce 360 Platform to ensure Archie had the shared context, controlled execution, and orchestration required for an agentic enterprise.
By deploying Agentforce rather than a bespoke DIY solution, Smarsh ensures Archie can plan and execute work across systems for smarter self-service and faster resolutions. This approach allows Smarsh to move work forward automatically across data and workflows, achieving greater efficiency without compromising the strict compliance rigor required by their industry.
As a result, the company expects to see a 20% increase in its customer self-service success rates, 25% faster issue resolution compared to traditional self-service search and browse methods, and a 30% boost in service representative productivity.
The bleeding edge of customer service AI
Both generative and agentic AI are rewriting the customer service playbook, yet the technology’s nascency can create intimidating hurdles. An organization can reap major rewards by moving decisively when launching AI initiatives, but it still requires care, forethought, and the right partnerships, Khanna says. Part of that is careful vendor choice.
"We're a Salesforce shop,” he shared. “We use a core set of Salesforce products, including Data 360, Agentforce Service, Agentforce Sales and more, so it was wise to hang our hat on an AI agent provided to us by Salesforce rather than buying something from outside. We know that in the beginning, as new tech comes, it will be challenging, but Salesforce is up to the task and we'll evolve together."
From day one, effective AI has demanded a single non-negotiable prerequisite: clean, secure data. Grounding generative AI in an organization’s verified corporate knowledge and internal data slashes hallucination risk while delivering a significantly better user experience. Smarsh, however, didn’t wait for the industry to catch up. The company anticipated this need nearly half a decade ago, spending years meticulously rationalizing, annotating, and anonymizing its data to prepare for this exact moment.
"A lot of people run into challenges and don’t complete their AI projects because the data’s not ready and it's not there," Khanna says. "We started out strong, right out of the gate because our data was already clean and locked down, and today we’re in production with a service agent as we speak."
Prioritizing data trust
Given Smarsh’s focus on regulatory compliance, Archie was introduced to replace the company’s previous self-service customer support chatbot. Janine Deegan, digital support program manager at Smarsh, worked with the Salesforce admin team on Smarsh's Agentforce deployment.
"With Archie, the goal was to move beyond experimentation and make AI genuinely usable in a regulated environment. It wasn’t as simple as just switching on an agent; we had to build a system that gave that raw intelligence the context and control our industry actually requires, which is why we chose Salesforce,” Deegan says. “By connecting our documentation directly to Agentforce, which is backed by the Salesforce Trust Layer, we turned our static data into a live, trusted resource that handles the precision needed for a regulated space."
Given its criticality, Khanna adds that maintaining pristine, secure documentation and data requires constant vigilance. To guarantee this, Smarsh erased the lines between departments, fusing the documentation team with the AI team. Now the two work in a tight loop: all of the material the document team produces, the AI team checks, verifies, and opens it up to the LLM.
AI and regulatory compliance
"We’re in a compliance world. We’re custodians of archival data for all of our financial institutions, and our data is so sacred that we don’t give it away, " Khanna explains. "We have to be very cognizant of security and identity as we open up our systems to agentic AI."
Infosec requirements were a critical consideration for rolling out Agentforce. Smarsh is regularly audited not just by regulatory bodies but also by the banks and financial institutions that have to comply with stringent data protection rules and ask for model risk management, (MRM).
"The safety regulators and banks ask for MRM," Khanna says. "They say, ‘Tell me that all my data is not going to the public because it’s connecting with an LLM. Tell me about the LLM. Tell me about the model you’re using.’ We worked with Salesforce so we could get MRM approval for our customers. And thanks to Salesforce’s knowledge base and documentation, we're always able to explain to these regulatory bodies what and why Archie is answering."
Boosting customer adoption
Customer buy-in is always a major challenge when it comes to new AI tools, and Archie was no exception. On the initial rollout of the new interface, some customers were confused by the new text box in the center of their screen and didn’t immediately understand how to interact with it.
“We learned the hard way that we needed better change management, and to make sure our industry customers understood they could simply ask questions in natural language,” Khanna says.
Personalization, they soon realized, was the key to gen AI adoption.
"Once customers had a better understanding of how Archie could be used for more efficient self-service, suddenly our adoption rate went up to 59%," he says. "Personalization was very critical for us. Now we see the uptake, and we hope to see that continue when we roll out Archie to the rest of our products."
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