AI Software Development Services

• Virtual customer support agents (including voice agents) and chatbots providing field-specific assistance (e.g., in doctor appointment scheduling, insurance claim filing, loan application submission).
• AI assistants for human agents (e.g., suggested replies, summaries, next-step recommendations).
• Intent detection and request routing models.
• Churn prediction and customer service analytics.

Specialized assistants (based on GenAI + domain rules + RAG) for various industries, including:

• Healthcare: AI scribes, appointment schedulers, patient intake assistants, staff copilots.
• BFSI: policy Q&A assistants, onboarding copilots, and loan or mortgage application assistants.
• Education: study planners, virtual tutors.
• Marketing: campaign copilots, content generators, social media managers.
• Gaming: dialog generation tools, moderation support.

​​​​​​AI assistance for EHR management (e.g., clinical notation, appointment summaries, smart data entry suggestions, coding support).

• Medical image analysis for MRI, CT, PET, SPECT, X-ray, ultrasound, and other modalities.
• AI-driven clinical decision support (risk stratification, diagnostic assistance, and treatment recommendations).
• Outcome prediction and cohort analytics (e.g., readmission risk, length-of-stay forecasting, treatment response analysis).
• Identification of hidden factors that influence health outcomes (history, medications, lifestyle, demographics) and longitudinal patient insights.
• 3D anatomy modeling and visualization.

• Finance copilots for reporting, commentary drafting, and variance explanations.
• Policy and procedure assistants for accountants and auditors.
• Agentic workflows for reconciliations and exception handling (with human approvals).
• Financial modeling and cash flow forecasting.
• Financial fraud detection and prevention.
• Financial risk management.
• Expense management to identify cost reduction opportunities and optimize spending.
• Tax optimization to minimize tax liabilities.
• Financial reporting and compliance monitoring.
• Asset and investment portfolio optimization.

• Procurement copilots (supplier comparison, RFQ drafting, contract Q&A).
• Exception-handling agents (for shipment delays, substitutions).
• Warehouse operations assistants for SOP guidance and incident logging.
• Real-time delivery route optimization and fleet monitoring engines.
• Predictive maintenance of warehouse equipment, trucks, and other assets.
• AI-assisted supplier selection and supplier performance assessment.
• Computer vision for automated product inspection.
• Warehouse operations automation with robots and drones.
• Supplier communication automation (e.g., payment reminders, invoice sharing).

• Inventory and warehouse management copilots (cycle count variance explanations, SOP-based guidance for receiving/put-away/picking, and incident & damage report drafting).
• Computer vision for inventory counting.
• Inventory demand forecasting based on data from all supply chain touchpoints, including customers, suppliers, manufacturers, and distributors.
• Real-time inventory optimization tools that dynamically adjust safety stock levels, reorder points, etc.
• Dynamic price optimization to reduce inventory levels by applying discounts.

• Technician copilots: SOP guidance, troubleshooting, work-order summaries, maintenance knowledge base search with RAG-based answers.
• Automatic “what changed” summaries from sensor or event streams.
• Intelligent insights and recommendations for OEE and asset utilization optimization.
• Predictive asset maintenance and early-failure detection (anomaly detection, remaining useful life).
• Asset lifecycle management (e.g., asset replacement or upgrade decisions, depreciation rates monitoring).
• Identification of asset risks related to regulatory compliance, environment, safety, etc.
• Real-time energy consumption optimization.

• Personalized content generation with brand book guardrails (outreach messages, proposals, product copy).
• Meeting and call summarization with action items captured in CRM.
• Campaign copilots for briefs, segmentation hypotheses, and A/B test variants.
• Automated email campaigns with personalized content and delivery schedules.
• Pipeline management with predictive lead scoring, deal prioritization, and smart recommendations on optimal actions.
• Multi-dimensional customer segmentation and sentiment analysis.
• Churn and upsell prediction.
• Dynamic price optimization based on the demand, stock levels, and the analysis of competitor prices.

• Copilots for HR teams (recruitment content generation, policy interpretation support, onboarding workflow assistance, case and ticket summaries, and draft communications).
• Employee assistants for everyday HR requests (onboarding guidance, benefits and leave questions, internal knowledge Q&A).
• AI-powered CV screening and candidate matching.
• Detection of recruitment bias.
• AI-assisted identification of factors contributing to employee turnover.
• Sentiment analysis to understand employee engagement.
• Personalized recommendations on employee performance optimization and learning opportunities.

• SOC copilots for alert triage summaries and investigation assistance.
• Natural-language querying over security logs and knowledge bases.
• Phishing simulation content generation.
• Automated detection of digital fraudulent activity (e.g., money-laundering transactions, phishing attempts, bots, malware, breach attempts).
• Detection of fraudulent and potentially hazardous activity in physical environments (via computer vision, access log analysis, etc.).
• Biometric-based authentication in security systems.

• LLM-assisted extraction of insights from unstructured and semi-structured content (tables, mixed layouts, inconsistent formatting).
• Topic-focused content aggregation with AI summaries (websites, social media, review portals, news sources).
• Taxonomy creation and normalization at scale (entities, categories, attributes) to standardize scraped data.
• Classical extraction plus classification, clustering, and deduplication to clean and structure large datasets.
• Sentiment analysis with quality checks and consistent evaluation controls.
• Image-based enrichment of scraped records: extracting insights from logos, product images, and in-picture text (computer vision, OCR).

• Natural language generation (NLG) with brand style constraints, tone tuning.
• Text, image, audio, and video generation pipelines.
• Subtitles, captioning, and translation at scale.
• Content summarization for articles, reports, calls, and knowledge bases.
• Content optimization for SEO (keyword enrichment, metadata, and on-page improvements).
• Content quality scoring and moderation (e.g., policy compliance, toxicity, brand-safety checks).
• SEO and topic modeling analytics to identify themes, gaps, and content opportunities.