Conversational AI Consulting & Development Services
SoftTeco provides conversational AI consulting and conversational AI development services, delivering products used in customer service, support, operations, and internal knowledge workflows.
75
client locations
18
years in the IT market
500
employees
650
delivered projects
Conversational AI value in numbers
Conversational AI services SoftTeco provides for businesses
Conversational AI consulting
Our conversational AI consulting helps you understand whether such a system is worth building, where it can bring value, and how it should fit into your existing operations. We turn a broad idea into a clear project direction with realistic scope, priorities, and rollout logic.
The service includes:
- Use case discovery and prioritization
- Workflow and content assessment
- Architecture and platform selection
- Integration planning
- Risk and ROI analysis
- Rollout roadmap
Conversational AI development services
We develop conversational systems that help users get answers, complete tasks, and move through digital workflows with less friction. Depending on the use case, the solution can work across websites, mobile apps, portals, kiosks, internal tools, or product interfaces.
What we build:
- Customer-facing AI chatbots
- Support assistants
- Internal knowledge assistants
- Voice assistants
- AI agents for action-based workflows
- NLP modules inside larger products
Conversational AI integration services
A conversational system becomes more useful when it can work with real business data instead of handling queries in isolation. We connect it to the tools your teams already use, so the assistant can support requests with context and pass information between systems correctly.
Common integrations include:
- CRM platforms
- Help desk and ticketing systems
- Knowledge bases
- ERPs
- HR tools
- Web portals
- Booking and payment systems
Conversational AI modernization services
If your current chatbot depends on rigid scripts, limited intents, or manual updates, our engineers can turn it into a more flexible conversational system. We do that to keep what already works, remove weak points, and add AI capabilities only where they improve answer quality, routing, or user experience.
What we improve:
- Outdated chatbot flows and conversation logic
- Intent recognition, entity extraction, and fallback handling
- Knowledge retrieval and response grounding
- LLM integration and conversation orchestration
- Analytics, performance tracking, and optimization points
Conversational AI security and compliance
Conversational AI often works with sensitive information, from customer records and support tickets to internal documents and legal content. That is why we define security requirements early and build the solution under ISO 9001 and ISO 27001 certified processes.
We build with:
- Role-based access
- Audit trails
- Secure integrations
- Logging and observability
- Environment controls
- Deployment patterns that fit compliance needs
Conversational AI support and optimization
After launch, we review how the assistant performs in real conversations and improve the parts that affect accuracy, usability, and business value. This keeps the system useful as products, policies, user behavior, and workflows change.
What we optimize after launch:
- Prompt behavior and response quality
- Retrieval accuracy and knowledge base updates
- Intent recognition and entity extraction
- Fallback responses and escalation rules
- Analytics, conversation reports, and performance insights
AI solutions we build
01
AI chatbots for customer service and support
Our engineers build AI chatbots that handle repeat questions, route requests, and reduce first-line workload. These systems work best when request volume is high and escalation rules are clear. By answering common questions and guiding users to the next step, AI chatbots can support lead conversion at the early stages of the sales funnel.
02
Virtual assistants for web portals
The assistants help users search content, complete routine tasks, and move through workflows with less friction. They work well in client portals, employee portals, partner platforms, and internal knowledge hubs.
03
Voice assistants and speech-enabled systems
Voice-based systems fit cases where users need hands-free interaction, faster guidance, or kiosk-based support. These solutions rely on speech recognition, natural language understanding, and response generation.
04
AI agents
Our team develops conversational systems that do more than reply to users. They can understand intent, read context from connected systems, use tools, create tickets, update records, route requests, or complete parts of a workflow. This makes AI agents useful for support, operations, and internal processes where the assistant has to move the request forward.
05
NLP modules inside larger products
Not every project needs a full assistant. We also develop standalone NLP modules that can be built into existing products, portals, and internal systems. These modules help recognize user intent, extract key details from text, classify requests, detect sentiment, summarize content, and improve search across large knowledge bases.
Need a conversational AI system that fits your workflow?
Talk to our team about your use case, data sources, and integration needs. You will get a realistic starting point.
Our conversational AI development roadmap
Step 1. Discovery and business alignment
Our team defines the use case, users, and business results the assistant needs to support. We map workflows, review source systems, and agree on success criteria, ticket types, escalation rules, knowledge sources, access rules, and workflow ownership.
Step 2. Conversation and workflow design
Next, we design how the assistant should behave in real interactions. This includes intents, entities, retrieval logic, fallback rules, handoff points, and approval points, so the system knows when to answer, when to ask for more context, and when to pass the task to a person. The goal here is not only to make the flow work on paper, but to make it reliable in real use.
Step 3. Architecture and integration planning
Once the logic is clear, we define how the assistant will work with your existing systems. We decide where it will read data, where it can write or update records, how it will route requests, and what access boundaries it needs. This step matters because most conversational AI projects fail not at the interface level, but at the integration level.
Step 4. Development and validation
Then we build the interface, orchestration layer, and integrations that make the assistant ready for production use. We validate retrieval quality, tool execution, workflow reliability, grounding, hallucination risks, latency, and edge cases against evaluation datasets and real request scenarios. This is where we identify what needs tuning before launch.
Step 5. Release and continuous improvement
We launch with monitoring, access controls, observability, analytics, and a support plan for ongoing tuning. After release, we track how the assistant performs, where users drop off, which answers need improvement, and how the workflow changes over time. That helps keep the system accurate, useful, and aligned with real business needs.
SoftTeco’s awards and recognitions
Technologies we use for conversational AI development
AI-related technologies
Core AI technologies: large language models, generative AI, machine learning, natural language processing, natural language understanding, retrieval-augmented generation, vector databases, AI agents
Conversation intelligence: intent recognition, entity extraction, sentiment analysis, semantic search, text classification, summarization
LLMs (proprietary / API-based): OpenAI GPT models, Anthropic Claude, Google Gemini, Mistral AI, AI21 Jamba
LLMs (open-source / self-hosted): Meta Llama, Mistral, Microsoft Phi, Qwen, DeepSeek, Google Gemma, Falcon
NLP models: BERT, FLAN-T5, SBERT
Speech and audio: speech recognition, OpenAI Whisper, OpenAI Realtime API, ElevenLabs, Deepgram, AssemblyAI
Embedding models: OpenAI Embeddings, SBERT
Delivery stack
Python: AI logic, model integration, backend services, and data processing
Java: enterprise backend systems and complex business logic
Node.js: fast API development and real-time conversational features
React: web chat interfaces, admin panels, and analytics dashboards
Angular: enterprise-grade web applications and portal interfaces
AWS: cloud hosting, storage, AI services, and scalable infrastructure
Microsoft Azure: enterprise cloud deployment, security, and AI integrations
Google Cloud: cloud infrastructure, data processing, and AI service integration
Docker: containerized deployment and stable environments across stages
REST API: connecting conversational AI with CRMs, help desk tools, portals, and internal systems
PostgreSQL: structured data storage, user records, logs, and application data
Elasticsearch: fast search, document lookup, and knowledge base search across large content sets
What our clients value most
Why choose SoftTeco for conversational AI consulting and development
18+ years in software development and IT consulting and 8+ years in AI development
30+ ML engineers and data scientists, in total 500+ employees across international offices
300+ happy customers
650+ successful projects
55+ awards from B2B tech marketplaces, including TechBehemoths, DesignRush, TopDevelopers, and TechReviewer
ISO 9001 and ISO 27001 certifications that support quality and security management
Clients from 75+ countries
4.8 stars on Clutch
Dedicated Data Science and Machine Learning Department
Conversational AI projects delivered by our team
AI chatbot development for customer service
Our engineers built Elgie AI Chat as an internal AI chatbot to support customer service and lead generation in one flow. The solution helped speed up customer service 2x, improved reply accuracy and relevance by 25%, and increased website leads by 14%. It uses GPT-4 together with retrieval-augmented generation, so the chatbot can respond based on company-specific knowledge rather than generic output. We also added multilingual support, interaction logging, and reporting dashboards, which gave the team better visibility into user behavior and response quality.
Conversational search and chatbot support for automotive diagnostics
Our team modernized Blindspot, an auto dealer information system, and added an AI chatbot assistant for technicians and resellers who need fast access to complex technical information. The assistant helps users find repair procedures, wiring diagrams, and diagnostic codes without searching through the platform manually. It is powered by NLP models and works together with semantic search, which makes the system more effective in understanding intent and surfacing the right content.
Voice and avatar assistant for hospitality
Our developers built an AI-powered hotel assistant for a high-end collaboration device used in hospitality and customer experience scenarios. The product combines voice interaction, avatar-based communication, RAG, and LLM-based response generation to create a more natural guest experience. We also used ReAct-style agent logic, which helps the assistant handle requests in a more structured way instead of only returning static answers.
Proudly serving industry leaders
Use cases where conversational AI works best
Customer service
Conversational AI works best in customer service when your team handles high volumes of similar requests and needs to respond faster without adding headcount. It helps reduce first-line workload, shorten wait times, and route cases more accurately, so human agents can focus on issues that need judgment, context, or direct interaction.
Good fit for:
- FAQ handling
- Account and order questions
- Customer information retrieval and processing
- Support intake
- Escalation to human agents
Sales and lead qualification
Conversational AI can support the early stages of the sales funnel when request volume is too high for teams to process every inquiry manually. An assistant can answer product questions, collect basic requirements, qualify leads, suggest relevant next steps, and route high-intent prospects to sales. This helps reduce missed inquiries and keeps response quality consistent during traffic spikes.
Good fit for:
- Product and service selection
- Lead qualification
- Pricing and availability questions
- Demo or consultation booking
- Routing high-intent leads to sales
Retail and ecommerce shopping assistance
Retail and ecommerce businesses often use conversational AI to create a smoother shopping experience across every stage of the customer journey. It can reply to product questions, suggest relevant items, check availability, support cart and checkout flows, and handle delivery or return questions.
Good fit for:
- Product discovery and recommendations
- Inventory and availability checks
- Cart and checkout assistance
- Abandoned cart conversations
- Order tracking and return support
Data and analytics support
Conversational AI can help teams access information faster without digging through dashboards or writing queries manually. An AI assistant can retrieve data from connected systems, summarize reports, explain trends, and answer questions about customers, sales, operations, or support performance.
Good fit for:
- Natural-language data queries
- Report summaries
- KPI explanations
- Customer and sales insights
- Analytics support for non-technical users
Business process automation
Conversational AI can also support routine administrative workflows where users need to submit requests, check statuses, update records, or trigger actions in connected systems. Instead of switching between tools, employees can use a conversational interface to complete simple operational tasks faster and with fewer manual steps.
Good fit for:
- Meeting and appointment scheduling
- Billing and invoicing requests
- Payroll or finance questions
- Task creation and reminders
- Workflow updates in CRM, ERP, or help desk systems
Travel guest assistance
Conversational AI helps hotels, resorts, travel platforms, and service teams handle repeat requests faster and provide support at any hour. It works well when guests need clear guidance, quick updates, or help with routine questions, while staff can spend more time on requests that need personal attention.
Good fit for:
- Concierge flows
- Guest support
- Booking and wayfinding
- Kiosk interactions
Technical product and documentation support
AI-powered assistants make it easier to navigate complex technical products, service manuals, repair guides, and diagnostic information. They can quickly point users to the correct procedure, walk them through troubleshooting, clarify technical concepts, and streamline documentation-heavy tasks without relying on manual searches. This is useful when support teams handle recurring technical inquiries or maintain extensive knowledge bases.
Good fit for:
- Semantic search across manuals
- Guided troubleshooting
- Product setup guidance
- Workflow support inside complex systems
Internal employee support
Employees often need quick answers from policies, portals, shared folders, and help desk systems, but this information is rarely stored in one place. A conversational assistant gives them one entry point for common internal requests. It can help employees find policies, create tickets, check request status, and move through HR, IT, or operations workflows with less friction.
Good fit for:
- HR and IT questions
- Policy lookup
- Access requests
- Internal knowledge search
- Onboarding support
Voice and contact center automation
Conversational AI is useful in contact centers when companies need to manage large call volumes, collect information before human handoff, or replace rigid IVR flows with more natural conversations. Voice assistants can identify the reason for contact, capture key details, route calls, and provide agents with context before they join the conversation.
Good fit for:
- Conversational IVR
- Call routing and information capture
- Appointment scheduling by phone
- Voice-based order or account status checks
- Agent handoff with conversation context
Regulated service workflows
Regulated industries benefit from conversational AI when the process is structured, the content is approved, and the assistant works inside clear limits. In these cases, the system helps users navigate workflows faster, surface the right information, and reduce manual effort without losing control over access, review, or escalation.
Good fit for:
- Controlled access to internal knowledge
- Guided workflows
- Document-heavy support flows
- Assistants with clear review and escalation rules
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FAQ
How much does conversational AI development cost?
Can conversational AI connect to our CRM, help desk, or internal systems?
How do you keep conversational AI answers accurate?
When should we choose custom conversational AI instead of a ready-made platform?
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