AI Software Development Services
Use SoftTeco’s artificial intelligence development services to automate your workflows, cut costs, and turn data into faster, smarter decisions.
17
Years in IT
30
AI Experts
300
Happy Customers
450
Successful Projects
Proudly Serving Industry Leaders
SoftTeco’s AI Software Development Services
AI Software Solutions We Deliver
Natural Language Processing (NLP)
SoftTeco designs NLP-powered solutions to help you enhance customer service, data analysis, and personalization. Whether you need to automate text processing, understand your customers better, or quickly process massive amounts of text, we can help.
NLP solutions we develop:
- AI chatbots & virtual assistants
- Sentiment analysis
- Text classification & summarization
- Machine translation
Computer Vision
Bring automation, speed, and efficiency to your processes with SoftTeco’s computer vision development services. We offer organizations innovative and modern solutions that utilize computer vision technology to help you work faster and smarter.
Computer vision solutions we develop:
- Image recognition & classification
- Image generation & modification
- Image analysis
- Optical character recognition (OCR)
- Object detection
- Face recognition
Speech & Audio Processing
Our speech recognition and audio processing solutions help organizations process their documents in a quick, automated manner, thus saving time and money on manual transcription or manual data entry.
Audio processing solutions we develop:
- Speech recognition
- Text-to-speech & speech-to-text
- Voice assistants
- Voice biometrics
Generative AI
Speed up the content generation process and enhance customer personalization through Gen AI development services. SoftTeco’s ML experts work with a variety of tools, including RNNs, transformers, and diffusion models to deliver your perfect solution.
Gen AI solutions we develop:
- Text generation
- Image & video generation
- Custom LLMs
Predictive Analytics
Eliminate guesswork from strategic decision-making and build your processes around real-life insights with our predictive analytics solutions. SoftTeco can help not only design the ML model but also prepare the dataset and fine-tune the model in accordance with your specific needs.
Predictive analytics solutions we develop:
- Customer behavior prediction
- Sales & demand forecasting
- Risk & fraud detection
- Financial forecasting
- Predictive maintenance
Business Process Automation
SoftTeco provides a variety of automation solutions, designed to handle tasks of any level of complexity. Whether you need help with manual document processing or want to automate the entire workflow, we can assist in setup and implementation of the selected solution.
Automation solutions we develop:
- RPA
- Intelligent document processing
- Workflow orchestration
- Customer service automation
- Fraud detection & compliance checks
AI Business Intelligence
Make your data work for you with SoftTeco’s AI business intelligence services. We take data processing to the next level by using advanced technologies and combining innovation with test-and-tried approaches.
BI solutions we develop:
- Automated data analysis
- Advanced data visualization
- Anomaly detection
- Data cleansing & enrichment
Agentic AI
Equip your product with autonomous systems that plan, decide, and act toward clear goals. Agentic AI goes beyond scripts and simple prompts: it reasons over context, breaks work into steps, calls tools and APIs, and reports back with results.
Agentic AI solutions we deliver:
- Autonomous workflow agents
- AI “digital employees”
- Operational optimization agents
- Multi-agent systems
- Proactive customer agents
01 / 08
SoftTeco Services Industry Recognitions
AI Use Cases Across Industries
Fintech
Financial institutions run on decisions measured in seconds. We develop financial software and deploy AI to help them make those calls with speed and precision.
- Fraud detection. Deep-learning systems scan transactions in real time, flagging behavior that doesn’t match normal patterns, and stopping fraud before losses occur.
- Risk scoring. AI models analyze multiple data streams like payments, online behavior, and geolocation to assess customer and portfolio risk more accurately than traditional scoring.
- Credit decisioning. Predictive models evaluate thin-file clients using alternative data, improving inclusion, and reducing defaults.
- Market forecasting and trading. Reinforcement learning agents process market signals, forecast trends, and execute trades automatically when target conditions are met.
- Portfolio optimization. ML algorithms simulate thousands of “what-if” scenarios to maximize returns within a defined risk profile.
- AI customer support. NLP-driven chatbots respond instantly, escalate complex requests, and reduce operational overhead.
Insurance
Our AI solutions help modernize insurance operations, which results in faster claims, smarter pricing, and stronger risk control.
- Claims processing. AI extracts data from documents, validates policies, and automates settlements.
- Fraud detection. Pattern recognition identifies outliers across claims and transactions.
- Risk assessment. Predictive models analyze customer profiles to fine-tune premiums.
- Customer experience. Chatbots deliver instant policy updates and assist with renewals.
- Underwriting automation. AI evaluates submissions against historical benchmarks for faster approval.
- Predictive analytics. AI forecasts loss ratios and detects emerging risk categories.
Healthtech
Healthcare depends on accuracy, but speed saves lives, that’s where AI makes a difference
- AI diagnostics. Computer vision models identify anomalies in X-rays, MRIs, or CT scans with precision comparable to radiologists.
- Remote monitoring. Wearables stream vital data; AI agents detect irregularities and alert clinicians in real time.
- Personalized care. Predictive models combine lifestyle, genetic, and clinical data to suggest treatment plans with fewer side effects.
- Hospital efficiency. AI forecasts patient flow, optimizes staff schedules, and automates documentation.
- Drug development. Generative AI predicts molecule behavior and shortens R&D cycles.
- Virtual health assistants. NLP chatbots guide patients through follow-ups, prescriptions, and recovery plans.
Real Estate
Real estate runs on documents, deadlines, and decisions, while AI brings order and foresight to all three.
- Smart property search. AI analyzes listings, status reports, pricing history, and buyer intent to deliver personalized property matches.
- Virtual tours. Computer vision can help create virtual tours of real estate properties by enriching the footage with virtual staging and AI-guided narration.
- Contract automation. OCR and NLP verify clauses, check signatures, and flag missing data points in seconds.
- Market forecasting. Predictive analytics track regional trends, new construction, and demand shifts to guide investment strategy.
- Customer service bots. AI assistants schedule viewings, answer queries, and manage maintenance requests 24/7.
Retail and Ecommerce
In ecommerce, every click generates data. AI turns that data into sales.
- Personalized shopping. Recommendation engines analyze browsing behavior and purchase history to tailor product suggestions.
- Demand prediction. Predictive models adjust inventory levels and reduce overstocking or shortages.
- Dynamic pricing. AI monitors competitor prices and demand signals to update pricing in real time.
- Virtual try-ons. AR and Computer Vision combine to show customers how products look or fit without a physical test.
- AI-Driven support. Chatbots resolve delivery and payment queries instantly, improving conversion and retention.
- Customer sentiment tracking. NLP tools analyze feedback and reviews to improve merchandising decisions.
Edtech
AI personalizes education, which helps institutions scale quality teaching without losing human touch.
- Adaptive learning systems. Models adjust difficulty, pacing, and content based on learner performance.
- Automated assessment. NLP systems evaluate open-ended work (essays, short answers, code explanations), and check key points, logic, structure, and writing quality. They add instant comments and send uncertain cases to a teacher for review.
- Predictive retention models. AI flags students at risk of dropping out and suggests interventions early.
- Assistive tech. Speech-to-text and text-to-speech tools make learning accessible to all.
- Curriculum planning. AI analyzes course data and enrollment patterns to optimize class scheduling and resource allocation.
- Student support bots. Virtual assistants handle FAQs, deadlines, and enrollment queries 24/7.
Manufacturing
AI lets factories monitor equipment, forecast failures, and optimize process parameters automatically without waiting for manual input.
- Predictive maintenance. ML models interpret vibration and temperature data to prevent machine failures.
- Computer vision inspection. Cameras detect micro-defects during production, ensuring consistent quality.
- Production forecasting. AI predicts demand and aligns procurement, labor, and logistics accordingly.
- Energy optimization. Smart models adjust machine usage to lower power consumption.
- Worker safety. Vision systems detect unsafe behavior or missing safety gear in real time.
- Process simulation. Digital twins replicate factory operations to test improvements before deployment.
Logistics and Transportation
Speed, cost, and reliability define logistics, and AI balances all three simultaneously.
- Route optimization. Algorithms calculate the most efficient paths based on weather, fuel, and traffic data.
- Predictive fleet maintenance. AI forecasts component wear and schedules service before breakdowns.
- Warehouse automation. Computer vision and robotics coordinate picking, packing, and dispatching operations.
- Supply chain forecasting. Predictive analytics model delivery times and material availability to prevent bottlenecks.
- Autonomous navigation. AI systems guide drones and delivery vehicles in closed environments safely and precisely.
- Customer tracking. Real-time status updates and estimated delivery adjustments increase transparency.
Energy and Utilities
In energy, stability depends on prediction, and AI keeps systems efficient and sustainable.
- Smart grid optimization. AI balances generation and consumption, reducing downtime and waste.
- Demand forecasting. Models predict consumption spikes and guide storage use.
- Predictive maintenance. Sensors and AI detect anomalies in turbines, transformers, and pipelines.
- Renewable energy integration. ML predicts short-term solar irradiance and wind speed, then adjusts setpoints and dispatch (battery charge/discharge, curtailment, and reserve scheduling), so variable generation stays in balance with demand.
- Emission monitoring. AI measures and reports environmental metrics to meet compliance standards.
- Resource planning. Predictive models optimize distribution to reduce operational costs.
AI Software Development Process at SoftTeco
Learn how our AI development company designs and delivers your next-gen AI solutions to match your data environment, compliance rules, and scalability demands.
Definition of Business Objectives
SoftTeco’s business analysts and ML engineers will help you define the existing pain points and goals and determine how exactly AI can help. We then determine the most suitable AI implementation strategy, which includes the selection of the AI model, evaluation of available data, and definition of KPIs.
Expected deliverables: a project brief with outlined goals, a feasibility study, and a high-level architecture of the proposed solution.
Data Collection & Preparation
SoftTeco’s data engineers and data analysts will identify available data sources and set up data pipelines for further collection. In case you do not have enough training data yet, we can start with synthetic data or public datasets. Our experts use tools like Roboflow and CVAT for dataset annotation and then split it into training and validation sets for further work.
Expected deliverables: data documentation (including its format, structure), initial EDA.
Model Development & Training
Once the data is ready, we will design and train the AI model on the annotated data. To achieve the most accurate results, we will set up preliminary evaluation metrics such as model precision and recall and will continuously tune hyperparameters.
Expected deliverables: a model prototype, training logs, documentation with algorithm choices and their justification.
Model Testing
Together with ML experts, SoftTeco’s QA engineers will test the designed model on real-world scenarios to see how its performance measures against the defined metrics. This might include A/B testing for performance comparison and validation loop for uncovering model drift.
Expected deliverables: validation and testing reports, approval for deployment.
Deployment & Integration
This step involves full integration of the developed model with your business logic and the configuration of the model’s monitoring. During this stage, our ML engineers work together with frontend and backend developers to ensure seamless integration with all parts of the system.
Expected deliverables: a detailed deployment plan, API implementation, a rollback plan.
Post-Launch Support
After the model is launched, we will closely monitor it for any possible issues as well as for the need for retraining. We will also update the model based on the new data (upon the request) and will perform scheduled model evaluations and revalidations.
Expected deliverables: an incident response guide, version history and change logs, a monitoring dashboard.
Don’t Rush with AI, Plan Your Moves Wisely
Many organizations are rushing to implement AI features simply to gain a competitive edge. While early adoption can indeed bring advantages, the key question is not whether to use AI but where to use it. Randomly inserting AI into products or processes may create short-term buzz, but true value emerges only when AI addresses the right problems. Strategic adoption matters more than speed.
The businesses that thrive will be those that move beyond the race to simply “add AI”. Instead, they will ask deeper questions: Which processes in our organization truly benefit from AI? Where does automation free people to do more valuable work? How can AI enhance—not replace—human creativity and judgment?
Have an idea?
We’ll help you turn it into AI
AI Projects That Make Us Proud
An Intelligent Business Chatbot
Our company designed and implemented a GPT-4-powered chatbot that helps us enhance and personalize customer service via 24/7 responsiveness and availability. We used a powerful RAG system for bot training and LLMs for data processing, achieving the needed accuracy and response style. We also implemented langdetect and FastText libraries for multi-language support.
An AI-Based Solution for Banana Leaf Health Monitoring
SoftTeco delivered an AI-powered solution for automated monitoring of banana trees in greenhouses and timely detection of disease symptoms via object detection and classification. After collecting the required data, our experts designed a classified model that works in conjunction with a specialized software module for image extraction.
A Smart Beehive Tracking System
SoftTeco used the computer vision technology to design an innovative mobile app that would help beekeepers automatically count the number of bees in a beehive. When a beekeeper takes a photo of the hive from the app, a smart ML model recognizes the bees in the photo and counts them, thus providing real-time beehive monitoring.
An AI-Powered Analyzer of Golf Players
Golf Club is a smart solution that uses computer vision to analyze golf players’ movements and posture and transforms the collected insights into technical requirements for further golf club manufacturing. SoftTeco was responsible for preliminary data collection and labeling and for the development of two AI models (one for detection and the other for segmentation).
Why Choose SoftTeco As Your Artificial Intelligence Development Company?
- 1 17+ years of experience in custom software development and IT consulting
- 2 300+ happy customers, including unicorn startups, from 75+ countries
- 3 Awarded by TechBehemoths, TopDevelopers, TechReviewer
- 4 30+ skilled ML engineers and data scientists
- 5 4.8 stars on Clutch with consistently positive reviews
- 6 Certified partners of Google Cloud and Digital Ocean
What our clients say
Our Tech Stack
Infrastructure and Cloud
Cloud: AWS, Google Cloud, Azure
Compute: NVIDIA GPUs, TPUs
Storage: Data Lakes, Warehouses, Object Storage
Data Engineering
Pipelines & ETL: APIs, Airflow, dbt
Processing: Pandas, NumPy, PySpark, Dask, polars
Labeling & Versioning: Roboflow, CVAT, Labelbox, DVC
ML and Deep Learning
DL: TensorFlow, PyTorch, Keras
Classical ML: Scikit-learn, XGBoost, LightGBM, prophet, R
Computer Vision & NLP: OpenCV, YOLO/Detectron2, SpaCy, NLTK, HF Transformers
MLOps and Deployment
Containers & Orchestration: Docker, Kubernetes
CI/CD & Tracking: GitHub Actions, GitLab CI, MLflow, W&B, Databricks
Monitoring: Prometheus, Grafana, ELK, LangSmith
Databases and Vector Search
DBs: PostgreSQL, MongoDB, MySQL, ClickHouse
Vector: Qdrant, FAISS, ChromaDB, LanceDB, pg_vector, Milvus, Wevieate, pinecone
Caching: Redis
APIs and Integration
Model Serving & APIs: FastAPI, Flask, GraphQL
Backend: Django, Node.js
BI & Apps: Streamlit, Dash, Plotly, Power BI, Tableau, Reflex
Security and Governance
Identity & Secrets: Keycloak, Vault, AWS/GCP IAM
Compliance: GDPR, ISO 27001, SOC 2, HIPAA (where required))
Responsible AI: AIF360, Fairlearn
Generative AI
LLMs & Tooling: GPT-4, Claude, Mistral, HF Transformers, LMstudio, vllm, ollama, llama.cpp
RAG & Orchestration / Agentic AI: LangChain, LlamaIndex, LangGraph, Langfuse, n8n, Langflow
Fine-tuning & Hosting: LoRA/PEFT, DeepSpeed, Vertex AI, SageMaker, HF Hub, Azure Machine Learning
AI Software Development Costs
The costs for developing an AI software solution may vary from $10,000 to $500,000, depending on the specific requirements of each project. Below we list the approximate costs for different types of projects that our clients might request:
POC Development
MVP Development
Full-Fledged Product Development
Additional Costs
POC Development
The Proof of Concept development involves the creation of the product’s prototype with an aim to study the feasibility of the idea. It normally includes a small-scale preparation of the dataset, basic algorithm selection, design of the prototype model and the initial evaluation of its performance.
Average cost: $10,000 – $50,000
Duration: 4-8 weeks
MVP Development
The minimum viable product development is a step up from the POC development and offers a more robust dataset and a more complex AI model. The MVP helps collect feedback from real users and understand what further improvements should be made to the product and why.
Average cost: $40,000 – $150,000
Duration: 2-5 months
Full-Fledged Product Development
With its event-driven, non-blocking I/O model, Node.js enables developers to create highly scalable applications that can accommodate a growing user base without compromising performance.
Average cost: $100,000 – $500,000
Duration: 6-12+ months
Additional Costs
Below we list the most common additional costs that are associated with artificial intelligence software development:
Data labeling: $5,000 – $40,000 (depending on the size and complexity of the dataset)
Cloud infrastructure: $500 – $10,000/month (depending on the usage)
Model maintenance: $20,000 – $80,000/year
Security & compliance audits: $5,000 – $20,000/audit
There are also several hidden costs of AI development that are usually associated with maintenance and employee training and are often overlooked.
Frequently Asked Questions
How long does it take to develop an AI-based solution?
What kind of ROI can I expect from AI implementation?
However, if we look at average statistics, most companies adopting AI report the following:
1. 15-30% improvement in productivity and employee efficiency due to task automation
2. 10-40% reduction in costs due to optimization of resource allocation, automation of workflows and elimination of the human error factor
3. 5-15% sales increase due to data-based decision making, better customer analysis and enhanced personalization.
Do you support multi-language AI solutions?
How do you address bias and fairness in AI models?
1. Use diverse and representative datasets
2. Use specific tools for bias analysis (IBM AI Fairness 360, Microsoft Fairlearn)
3. Use specialized techniques like adversarial debiasing or reweighting for bias reduction
4. Perform testing across different demographic slices
5. Align AI development with all needed regulations and standards
What kind of data do I need to provide for AI development?
We might also request annotated data and contextual data for extra context. As we proceed with the AI model development and training, we will also need validation and testing datasets that often include historical data and are used for model testing.
Learn more about AI
What we do
What we think
+ Show more
