PixelGrid designs and builds reliable AI capabilities for ecommerce platforms, mobile apps, and web applications—from intelligent search and recommendations to grounded copilots, agentic systems, and multimodal experiences.
We focus on capabilities that improve discovery, understanding, and decision-making inside the digital experience you already want to build.
Semantic and conversational search that understands intent, context, and the language your customers actually use.
Relevant product and content recommendations designed around real catalogue data and measurable user journeys.
In-product assistants that work from approved data and help users understand, decide, or create with confidence.
Useful visual, voice, and natural-language interactions designed for the context of web and mobile products.
Goal-driven systems that coordinate steps, use approved tools, and pause for human judgment when the consequence matters.
Model routing, retrieval, APIs, permissions, fallbacks, and observability engineered into your existing product stack.
We define the user problem and success measure before choosing a model. AI earns its place by making the product more useful.
We design retrieval, permissions, and integrations so features work from the information they are allowed to use.
Realistic test cases, clear failure states, privacy-aware architecture, and quality thresholds turn a prototype into a product.
Quality, latency, cost, and adoption are observable after launch, giving the team evidence for each improvement.
We identify the user journey, available data, product constraints, and the outcome worth improving.
We test the hardest assumptions with representative examples before committing to a full production build.
We build the experience, model layer, data connections, safeguards, and observability as one coherent system.
We monitor real usage and quality, then refine the feature as users, data, and models change.
AI engineering is the work required to turn a model into a dependable product capability. PixelGrid combines product design, software architecture, data integration, model selection, retrieval, evaluation, security, and production monitoring. We focus on features that improve the experience of an ecommerce platform, mobile app, website, or web application—such as semantic search, relevant recommendations, an in-product copilot, or multimodal interactions. The model is only one component. The surrounding software determines whether the feature is fast, grounded in approved data, safe to use, measurable, and maintainable as models and product requirements change.
We build intelligent search and product discovery, recommendation systems, grounded copilots, agentic systems, natural-language interfaces, content assistance, and visual or voice features. The right feature depends on the user journey rather than the novelty of a model. For ecommerce, that might mean understanding conversational product searches and surfacing genuinely relevant items. For a web or mobile application, it might mean helping a user explore complex information, use approved tools across several steps, or complete a specialist task with less friction. We start by defining the user outcome and the evidence that would show the feature is useful before choosing the model or technical approach.
Agentic automation uses an AI system to work toward a defined goal across multiple steps, choosing from approved tools and responding to what happens along the way. In a product, an agent might gather relevant information, call an API, compare options, prepare an action, and ask a person to approve the consequential step. We design these systems with explicit permissions, limited tool access, durable state, traceable runs, and clear hand-off points. Before launch, the agent is tested against realistic success cases, edge cases, and situations where it should stop rather than guess. The goal is controlled, observable execution—not an unsupervised system with unlimited access.
We work with leading commercial and open-source models and select the right option for each product based on task quality, latency, privacy, cost, and deployment requirements. Where possible, we design the application so that the model provider is not tightly coupled to the rest of the product. That makes it easier to evaluate new models, control costs, and respond to changing requirements without rebuilding the entire feature. For products with data-residency or infrastructure constraints, we can assess EU-hosted services or self-hosted open-source models as part of the architecture.
We define expected behaviour, build evaluation datasets from realistic product scenarios, and test quality before launch. When the feature needs knowledge from your business, retrieval can ground responses in approved product data or documents instead of relying on model memory. We design clear failure states, permission boundaries, and user controls, then instrument the production feature so quality, latency, cost, and adoption can be monitored over time. The exact controls depend on the consequence of an incorrect result: a product suggestion and a decision-support feature should not be treated as if they carry the same risk.
Yes. We can assess an existing product, identify where AI could improve a specific user journey, and design the feature around the current architecture. The first step is usually a focused technical and product review covering data quality, APIs, privacy, user experience, and the expected volume and cost of model calls. From there, we can prototype the highest-risk interaction, evaluate it against real examples, and integrate it incrementally. If the existing system needs foundational work first, we will make that visible in the plan instead of hiding it inside the build.
Cost and timeline depend on the product scope, data readiness, integrations, model requirements, and the level of evaluation needed before release. A focused prototype for one well-defined user journey is different from launching a production feature across a large catalogue or regulated product. After an initial consultation, we define the outcome, technical approach, delivery stages, and major risks in a written proposal. We usually recommend proving the highest-risk assumption early, then expanding only when the experience and quality meet agreed measures.
Bring us the user problem. We'll help you find the clearest path from idea to a reliable product capability.