In 2025, most companies are replacing rule-based bots with LLM- or ML-powered assistants. Based on their AI chatbot development case studies, experts of a custom software development company Belitsoft tell how the switch to large language models gave companies fluency.
This agency confirmed its 20+ years of expertise with a 4,9/5 score from clients on the most authoritative B2B review platforms (G2, Gartner, and Goodfirms). Belitsoft provides its customers with a thorough project plan and cost estimation, whether it is a startup, enterprise, or mature eCommerce business. Their experts assist with workflows and bot personas, structuring conversations, opting for an LLM model and tuning, embedding and vectorizing data. Finally, they recommend the optimal cloud technologies — Azure, AWS, and others.
Major Trends in AI Chatbot Development (2025)
The real shift came when companies started tying that fluency to actual systems. A bot that sounds great but can’t verify account details or trigger a refund isn’t an assistant. The ones that matter plug into CRMs, policy engines, product databases – and surface the right thing before the user even finishes asking.
That’s what telecommunications companies built. A background tool to free up thousands of support agents with answers. Call times dropped. Agents spend more time on revenue, not resolution. That’s where the 40% sales bump came from.
In finance, the same principle holds. Bank’s financial assistant doesn’t need to be charming – it must catch weird spending, push alerts that matter, and point users toward better habits. Bot for an insurance company doesn’t need a personality. It just needs to understand 90% of routine questions and get out of the way. These aren’t chatbots with “tone”. They’re tuned tools doing work that used to drag on people. And the more vertical they go, the more they deliver.
A chatbot on the website is fine. But if the issue starts over the phone, the system must be there too. The reason voice chatbots are climbing in usage isn’t that people love talking to bots. It’s that typing on a phone when you’re annoyed or in a rush is worse.
The best chatbots in 2025 complete tasks quickly. That’s the bar now. Not “did it understand me?” But “did it finish the job before I had to ask twice?”
The best bots in 2025 barely get noticed – because the customer gets what they needed and moves on. That’s the whole point. What companies are learning – finally – is that a chatbot shouldn’t perform. It should disappear.
AI Chatbot Use Cases in the USA (2025)
Healthcare
The chatbot gold rush in healthcare isn’t about chat at all. It’s about workflow. They’re doing it because the current system runs on human time, and human time is maxed out.
At Emory, “virtual nursing” means using AI and remote staff to strip the cruft from real nurses’ schedules. Admissions paperwork, discharge briefings, fall monitoring – offloaded to a hybrid of AI and tele-nurses.
Same story, different interface, at Seattle Children’s. Their “Pathway Assistant” does one job: navigate complex clinical care pathways. This is decision support in a wrapper that feels like a colleague, not an app. And it works because it speaks the language of protocols. It was built to reduce the cognitive drag of hunting through documents mid-shift.
WellSpan’s “Ana” voice assistant is a patient-connector. This thing calls you. It talks you through preparation. It orders your test kit. It explains your colonoscopy like it’s done a thousand times – because it has. Most bots wait to be poked. Ana acts like a nurse with a call list. And patients actually like her. She’s doing follow-ups that no one else had time for, and doing them in Spanish if needed.
Then there’s Microsoft’s Dragon Copilot – the closest thing to a full-stack AI assistant for clinical documentation. It listens, summarizes, drafts. And unlike the first wave of ambient scribes, it doesn’t just transcribe – it generates. It answers questions in real time. Pulls up labs. Fills out referral letters. It’s a cross between a dictation engine and a pocket specialist.
They’re not trying to automate care. They’re trying to free up care teams to actually deliver it. It’s already on the floor, in the workflow, affecting staffing ratios and patient throughput.
Fintech (Financial Services)
In fintech, chatbots wear badges. In 2025, they’re not just answering customer FAQs. They’re handling compliance, writing code, prepping meetings, and ghostwriting your recap emails like they’ve been in the firm for ten years. These agents are being wired into the core workflows.
“Erica” used to be a customer-facing novelty. Now there’s an internal version handling IT, HR, and operational clutter for over 90% of their 213,000 employees. That’s not a chatbot – that’s a full-time digital workforce. They’ve slashed IT help desk traffic by half.
Goldman Sachs doesn’t talk about productivity metrics yet, but the signal’s clear: their “GS AI Assistant” launched to 10,000 employees in Q1 and is scaling firm-wide. This is generative AI trained on Goldman’s own content – not a generic model with financial jargon sprinkled in. A chatbot that doesn’t just pull up documents, but behaves like a veteran team member who knows where everything lives and how it all connects. You ask a question, you get an answer and a context. It’s search, synthesis, and decision support in one move. It’s a shift in how institutional knowledge is accessed – and who gets to access it without needing a two-year ramp-up.
Then there’s Advisor360°, quietly solving one of the most annoying bottlenecks in wealth management: after-meeting paperwork. Their “Parrot AI” doesn’t just transcribe meetings. It summarizes key points, drafts follow-ups, updates the CRM, and does it all with compliance in mind. Built with SEC and FINRA rules as a first-class citizen. The agent drafts – advisors approve. That approval loop isn’t a blocker. It’s freeing up hours of clerical drag. Advisors spend more time with clients, and less time acting as stenographers for their own meetings.
AI here isn’t framed as magic. It’s operationalized as leverage. The stakes are too high for hallucinations. So the models are trained tight. The outputs are routed through approval paths. And every deployment starts from a workflow.
AI doesn’t run wild. It runs on rails, with humans in the loop, and with compliance and client trust wired in by default. That’s what makes it stick.
In 2025, the question isn’t whether AI will transform fintech – it’s whether you’re deploying it with guardrails.
Manufacturing
In manufacturing, the chatbot wears a hard hat. In 2025, AI agents have moved from customer service scripts to the factory floor, where they interpret sensor data, troubleshoot equipment failures, and do something no dashboard ever managed: answer a question in plain English that actually helps someone fix the problem.
Microsoft’s Factory Operations Agent, already living in Schaeffler’s global plants, is less a chatbot and more a diagnostic translator. Engineers don’t need to query SQL or parse a SCADA feed – they ask, “Why did Line 3’s output drop 6% this week?” and the agent pulls telemetry from machines, historical failure patterns, even energy data, and gives them a root-cause hypothesis faster than an ops meeting ever could. It’s AI removing the hour-long dance of digging through fragmented dashboards and tribal knowledge.
Lenovo’s AI Knowledge Assistant leans even further in. Give it an instruction – “Generate a predictive maintenance schedule for robot arms” – and it pulls from time-series sensor data, digital twin models, and maintenance logs to produce a plan. No SQL, no dev time. Just a prompt and an answer that can be implemented. And because it supports multimodal input – not just text, but images and waveform data – it can respond to visual queries and context-heavy inputs that old rule-based bots would choke on. This is agentic AI built for people who wear steel-toes, not hoodies.
Target’s Store Companion bot helps front-line retail workers manage inventory and operational questions using generative AI trained on internal SOPs. Staff use it on handhelds to get answers fast – freeing time for customer interaction. For an organization as massive as Target, shaving minutes per employee scales to thousands of hours recovered – without asking anyone to “learn AI”.
These chatbots are embedded where the questions emerge: in production lines, on shop floors, in mobile devices during the shift. And they’re answering real-world queries with real-time data – not canned scripts or keyword matches.
These aren’t “smart factories” in PowerPoint decks. These are deployments with business value on the clock.
What we’re seeing in manufacturing is the rise of a new kind of co-worker.
No-Code/SaaS vs. Custom LLM-based AI Chatbot Solutions
Most companies start with a fire to put out: customer support’s overwhelmed, lead conversion too slow, or they just want to show their board they’re “doing something with AI.” So they reach for the fastest fix – usually a no-code platform – and only later realize what trade-offs they’ve baked in.
Off-the-shelf bots are built to generalize
That’s their strength and their ceiling. You can launch in days, wire up a few flows, maybe plug into Slack or Shopify, and call it done. But once you try to route a workflow through a legacy system or enforce logic that lives in your business rules, the abstraction cracks. A finance team may want the bot to verify a customer’s identity using a custom internal system – not available. A health organization may need the bot to anonymize data before saving it – also not available. It’s the business model: standardization over specificity.
Integration limits
Most platforms have plug-and-play support for popular SaaS tools. Beyond that, you have to build everything manually with webhooks. Try pulling data from a custom ERP or enforcing authentication logic tied to an old access control matrix – suddenly the “no-code” charm gives way to “ask vendor support.” Worse, you’re now dependent on uptime, API constraints, and whatever UX roadmap the vendor thinks will help their average customer, not you.
Scaling
SaaS pricing looks great for prototypes. But per-message or per-seat models add up once a bot is actually useful. One support team scaled up usage and found themselves rewriting flows just to avoid triggering expensive overages.
Custom LLM
But if SaaS platforms cap potential, custom LLM builds raise the floor and the workload. You get full control. You also get every problem SaaS vendors exist to hide. Model deployment, infra scaling, guardrails, testing – suddenly it’s your problem if the chatbot hallucinates a refund policy or leaks sensitive data from training logs.
Self-hosted LLMs demand engineering depth most companies don’t have on hand. Even teams that start with an open model like LLaMA quickly run into the dark arts of prompt tuning, memory control, inference latency, and GPU costs.
Even the big wins come with footnotes. Morgan Stanley’s custom OpenAI assistant for financial advisors is a milestone, but behind it is a multi-phase project involving curated corpuses, feedback loops, and active retraining. It works because they invested in internal capability – not just tech, but governance.
Enterprises in healthcare, finance, and public services don’t get to roll out a chatbot that “mostly works.” They need audits, explainability, and clear data governance. No off-the-shelf vendor is going to rewrite their storage layer because your legal team said so. And no custom build is going to be compliant out of the box. Someone has to do the translation work – usually you.
Then there’s the temptation to use an open-source base model, wrap it in a vendor’s tooling, add a few integrations – done, right? Sometimes. Verizon did it with Google’s LLMs, feeding it 15,000 internal docs and building an internal assistant. But that only worked because they had the staff and budget to support it. Feeding a model 15,000 PDFs isn’t the hard part. Cleaning them, indexing them, validating the outputs – that’s the grind. And if the first round isn’t accurate, guess what? Now you’re in the chatbot tuning business.
Some companies treat SaaS bots like scaffolding – fast to launch, disposable when real needs emerge. Others go custom from day one and publish retrospectives warning about scope creep and team fatigue.
The smart ones don’t pick a side. Use SaaS to validate use cases, gather conversation data, and buy time. Then, when the need for differentiation or control becomes undeniable, build or buy the pieces that matter most – and phase out the rest. But either way, they stop pretending there’s a silver bullet. They budget for iteration. And they bake in oversight – not because they don’t trust the tech, but because they know it’s not done after version one.
About the Author:
Dmitry Baraishuk is a partner or Chief Innovation Officer at software development company Belitsoft (a Noventiq company). He has been leading department specializing custom software development for 20 years. Department has hundreds of successful projects such services as healthcare and finance IT consulting, Software development, application modernization, cloud migration, data analytics implementation, more for US-based startups and enterprises.
