Services
Engineering of AI-powered smart inventory management, dynamic pricing engines, and hyper-personalized customer experiences that adapt in real time based on probabilistic consumer behavioral logic to drive top-line revenue. Sub-services include AI product recommendation engine development, predictive inventory management AI, generative AI for e-commerce catalog creation, and visual search AI for retail mobile applications.
Engineering of AI systems that analyze competitor pricing, inventory levels, and individual customer willingness-to-pay to adjust prices dynamically, maximizing margin and conversion without manual intervention.
Development of predictive models that forecast demand at a hyper-local SKU level, automating replenishment and allocation to minimize stockouts and overstock while accounting for seasonality and trends.
Architecture of next-best-action engines using collaborative filtering, content-based filtering, and real-time session data to personalize product discovery feeds and dramatically increase average order value.
Implementation of multimodal models (like GPT-4V, DALL-E) to automatically generate high-converting, SEO-optimized product descriptions, marketing copy, and lifestyle imagery at scale.
Integration of computer vision models (e.g., CLIP, ResNet) to enable shoppers to search with images, find similar products, and receive personalized visual recommendations based on style preferences.
Building of time-series and causal inference models that synthesize internal sales data, external market signals, and promotional calendars to predict future demand with high accuracy for strategic planning.
Development of sophisticated chatbots and voice assistants that guide customers through complex purchases, answer product questions using RAG, and process orders within messaging platforms.
Engineering of real-time intervention systems that identify at-risk shopping sessions and trigger personalized incentives, reminder messages, or support offers to recover lost revenue.
Implementation of probabilistic graph models and deterministic matching to unify anonymous and known customer data across web, mobile, and in-store touchpoints into a single, actionable profile.
Creation of systems that evaluate customer context, past behavior, and business rules in milliseconds to serve the most effective promotion, discount, or bundle at the precise moment of consideration.
Development of models that predict customer lifetime value, optimize reward structures, and personalize engagement to increase retention and maximize the ROI of loyalty initiatives.
Engineering of machine learning models that infer unstated customer goals and purchase stage from browsing patterns, enabling hyper-personalized experiences before explicit signals are given.
Building of systems that dynamically generate email content, subject lines, and send times for each individual recipient based on predicted engagement, driving significantly higher open and click-through rates.
Integration of AI to streamline the checkout flow with features like dynamic field validation, personalized payment method suggestions, and fraud risk assessment to reduce friction and increase completion rates.
Development of natural language understanding systems for smart speakers and in-car interfaces, allowing for hands-free product search, reordering, and status updates using models like Whisper and custom SLMs.
Integration of AR frameworks and 3D computer vision to allow customers to visualize products in their own space (e.g., furniture, apparel), reducing returns and increasing confidence in purchase decisions.
Development of models that use customer-provided measurements, past purchase history, and product attributes to accurately predict the best-fitting size, drastically reducing apparel return rates.
Creation of real-time algorithms that identify complementary products and construct personalized bundles or upsell offers at the point of cart addition or checkout to increase average order value.
Engineering of predictive models that forecast the long-term revenue potential of individual customers, enabling prioritized marketing spend, personalized retention strategies, and improved CAC efficiency.
Architecture of a central decisioning engine that coordinates consistent, context-aware personalization across web, mobile app, email, SMS, and in-store digital signage from a unified customer profile.
Building of systems that analyze giver-recipient relationships, occasion, past preferences, and social signals to suggest highly relevant gift options, solving a major e-commerce discovery problem.
Implementation of agentic AI systems that autonomously trigger purchase orders and transfer requests by simulating supply chain constraints and predicting lead times, moving beyond basic forecasting.
Integration of web scraping and NLP models to continuously monitor competitor prices and promotional strategies, providing actionable insights and automated pricing rule adjustments.
Engineering of unsupervised and supervised learning models to dynamically create and update high-fidelity customer segments based on behavior, enabling precise targeting for marketing and merchandising.
Development of systems that connect social media engagement (likes, shares, comments) directly to product catalogs and shopping experiences, enabling seamless purchasing within social platforms.
Creation of AI-driven systems that tailor landing page content, imagery, and CTAs in real-time based on the visitor's acquisition source, intent, and profile to maximize conversion rates.
Implementation of sentiment analysis and aspect-based opinion mining on customer reviews and Q&A to surface product insights, identify quality issues, and generate actionable summaries for merchandisers.
Engineering of systems that generate or assemble personalized video content showcasing products relevant to a specific user, integrating shoppable elements for a highly engaging discovery experience.
Development of systems that provide accurate, up-to-the-second inventory counts across all sales channels and fulfillment nodes, preventing overselling and enabling reliable promises like "buy online, pick up in-store."
Building of models that predict the most cost-effective and customer-preferred delivery option for each order, optimizing carrier selection, routing, and promise dates to balance cost and satisfaction.
Engineering of simulation and forecasting models that predict the impact of new product introductions and promotional campaigns, optimizing launch timing, inventory allocation, and marketing spend.
Creation of systems that determine the optimal message, timing, and frequency for mobile push notifications for each user, maximizing engagement while minimizing opt-outs.
Implementation of models that analyze social media, search trends, and early sales data to identify emerging product trends, enabling faster and more data-driven merchandising decisions.
Integration of advanced computer vision and generative AI (like Stable Diffusion) to create realistic virtual try-on experiences for apparel, eyewear, and cosmetics, enhancing online confidence.
Development of systems using fine-tuned LLMs to automatically create unique, compelling, and SEO-friendly product descriptions for thousands of SKUs, tailored to different customer segments.
Engineering of systems that ethically solicit post-purchase reviews using personalized prompts and employ NLP to automatically moderate incoming reviews for authenticity and policy compliance.
Building of systems that track detailed product page views without an add-to-cart event, then trigger personalized retargeting ads or emails showcasing the viewed items with relevant incentives.
Integration of generative AI interfaces that allow customers to describe or modify product designs (e.g., "make this t-shirt more rustic"), creating a unique, co-creation shopping experience.
Development of RAG-based systems that provide instant, accurate answers to customer support questions by querying internal knowledge bases, reducing ticket volume and improving self-service.
Implementation of multi-armed bandit algorithms and predictive models to autonomously test and optimize website elements (like CTAs, layouts, offers) in real-time, moving beyond traditional A/B testing.
Engineering of systems that continuously analyze customer sentiment from support chats, reviews, and surveys, alerting teams to emerging issues and automatically routing feedback to relevant departments.
Development of a centralized personalization layer for marketplaces that tailors search results, promotions, and discovery feeds for each shopper while balancing the goals of multiple sellers.
Building of models that learn individual subscriber preferences over time to dynamically curate the contents of each subscription shipment, maximizing surprise, delight, and retention.
Implementation of machine learning models that stitch together disparate touchpoint data to map and predict individual customer journeys, identifying key drop-off points and opportunities for intervention.
Development of systems that automatically generate and serve thousands of unique ad creative variants, tailored to the interests and demographics of individual users across programmatic platforms.
Engineering of systems that leverage social graph data and engagement history to personalize the shopping experience within social media apps, from discovery to checkout.
Creation of systems that deliver personalized tutorials, how-to guides, and product tips based on a customer's purchase history and observed usage patterns, increasing product adoption and satisfaction.
Development of adaptive checkout flows that change based on user device, location, purchase history, and perceived risk level, removing unnecessary steps and friction points in real-time.
Engineering of AI systems that trigger personalized post-purchase communication sequences (e.g., delivery updates, usage tips, review requests) based on the specific product bought and customer profile.
Building of models that predict customer satisfaction scores (like NPS or CSAT) from behavioral data, allowing businesses to proactively address issues before they impact loyalty and survey results.
How We Work
One-fit-all AI don't work for modern businesses. At Inferensys, we aim to understand your business & custom requirements; which we use to define most efficient agentic workflows, the data, and the tools for your business.
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We understand the task, the users, and where AI can actually help.
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We define what needs search, automation, or product integration.
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We implement the part that proves the value first.
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We add the checks and visibility needed to keep it useful.
Read moreThe first call is a practical review of your use case and the right next step.
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