AI Product Design & Development Company

We design, build, and ship production-grade AI products end-to-end.

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What We Do

We design and implement AI-driven products with end-to-end UX, RAG, MLOps, and secure orchestration to deliver impactful, reliable solutions.

Design Philosophy

Our AI design makes powerful capabilities accessible through natural conversational UX, prompt-aware workflows, and guardrail-first designs that clearly communicate boundaries.

Our conversational UX design methodology addresses the unique challenges of LLM-powered interfaces. We develop conversation flows that maintain context across multi-turn interactions, implementing sophisticated state management that tracks user intent, conversation history, and task progress. Our designs include visual indicators for context windows, showing users how much conversation history the AI considers. We create prompt scaffolding interfaces that help users structure complex requests, with templates for common patterns and interactive builders for advanced queries.

We specialize in designing for multimodal AI interactions, creating unified experiences that seamlessly blend text, voice, image, and document inputs. Our interfaces adapt dynamically to input modality, adjusting UI elements and feedback mechanisms accordingly. We design modal switching interfaces that allow users to transition between input types mid-conversation, maintaining context across modalities. Our multimodal output designs handle mixed media responses, properly formatting and presenting text, code, tables, and generated images within coherent layouts.

Our latency-tolerant UI patterns address the inherent delays in LLM processing while maintaining user engagement. We implement progressive response streaming that displays partial results as they generate, with smooth animations that indicate ongoing processing. Our designs include productive wait states that surface relevant information or suggest related actions while primary requests process. We create tiered response strategies that deliver quick preliminary answers followed by more comprehensive results, managing user expectations through clear progress indicators.

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RAG Architecture & Best Practices

We implement sophisticated RAG systems that enhance LLM capabilities with your proprietary knowledge. Our approach combines optimal chunking strategies, hybrid retrieval methods, and intelligent caching for production-grade performance.

Our RAG implementation expertise begins with sophisticated document processing pipelines that handle diverse content types including PDFs, Office documents, HTML, and proprietary formats. We implement intelligent chunking strategies that preserve semantic coherence while optimizing for retrieval effectiveness. Our sliding window approaches with configurable overlap ensure important context isn’t lost at chunk boundaries. We develop hierarchical chunking systems that maintain document structure, enabling both fine-grained and document-level retrieval.

We excel at embedding generation and management, selecting optimal models based on your domain and languages. Our implementations support multiple embedding models simultaneously, allowing different strategies for different content types. We handle embedding versioning and migration, ensuring system continuity as better models become available. Our dimension reduction techniques optimize storage and computation without sacrificing retrieval quality.

Our hybrid retrieval strategies combine dense vector search with traditional keyword methods, leveraging the strengths of both approaches. We implement BM25 scoring for exact match requirements alongside semantic similarity search. Our Maximum Marginal Relevance (MMR) implementations ensure diverse, non-redundant retrieval results. We develop custom re-ranking pipelines using cross-encoders and learned ranking models that significantly improve result relevance.

We have deep expertise across vector database platforms, selecting and optimizing based on your specific requirements. For FAISS deployments, we implement efficient indexing strategies including IVF and HNSW configurations tuned for your data scale. Our PGVector implementations leverage PostgreSQL’s robustness with vector capabilities, ideal for transactional consistency requirements. We optimize Pinecone deployments for serverless scale with pod configuration and index management strategies.

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Orchestration Patterns & Frameworks

We leverage leading orchestration frameworks to build sophisticated AI applications that coordinate multiple models, tools, and data sources seamlessly.

Our LangChain expertise encompasses the full spectrum of chain architectures and patterns for LLM applications. We build sequential chains that process information through multiple transformation stages, with each step refining and enriching the output. Our parallel chain implementations enable concurrent processing of multiple data streams, significantly reducing latency for complex operations.

We implement sophisticated memory systems using LangChain’s memory abstractions, going beyond simple conversation buffers. Our sliding window memories maintain recent context while managing token consumption. We build summary memories that distill conversation history into concise representations, preserving key information across extended interactions.

Our LangGraph implementations create stateful, multi-agent systems with complex interaction patterns. We design graph topologies that represent business workflows, with nodes corresponding to specialized agents or processing steps. Our state management strategies ensure consistency across distributed graph executions, implementing checkpointing and recovery mechanisms.

We leverage LangFlow for rapid prototyping and visual workflow development, creating intuitive drag-and-drop interfaces for non-technical stakeholders. Our LangFlow customizations include specialized components for your domain-specific operations, custom nodes that encapsulate complex business logic, and integration adapters for your existing systems.

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MCP Architecture & Governance

We implement Model Context Protocol integrations that enable secure, governed access to enterprise tools and data sources while maintaining comprehensive observability.

Our Model Context Protocol implementations provide standardized, secure interfaces between LLMs and enterprise systems. We design MCP architectures that abstract complexity while maintaining flexibility, creating tool servers that encapsulate business logic and enforce governance policies.

We implement comprehensive permission models within MCP deployments, ensuring fine-grained access control over tool capabilities. Our role-based access control (RBAC) systems map organizational roles to permitted tool operations, with inheritance hierarchies that simplify administration.

Our sandboxing strategies ensure safe execution of MCP tools, protecting against both intentional and accidental misuse. We implement resource isolation using container technologies, limiting CPU, memory, and network access for tool executions.

We build comprehensive observability into MCP implementations, providing detailed visibility into tool usage and performance. Our distributed tracing captures full execution paths across multiple tool invocations, correlating requests across system boundaries.

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Evaluation & Safety Systems

We implement robust evaluation, monitoring, and safety systems that ensure your AI products maintain quality, security, and compliance in production.

Our evaluation framework implementations provide comprehensive quality assurance for LLM applications throughout their lifecycle. We develop offline evaluation suites that test model performance against carefully curated golden datasets, measuring accuracy, relevance, coherence, and task-specific metrics.

We build online evaluation systems that continuously assess production performance using real user interactions. Our A/B testing frameworks randomly assign users to different model variants, prompts, or retrieval strategies, measuring impact on business metrics.

Our observability platforms provide deep visibility into LLM application behavior and performance. We implement distributed tracing that captures complete request flows across model calls, retrieval operations, tool executions, and post-processing steps.

Our guardrail systems protect against various risks inherent in LLM applications. We implement prompt injection detection using multiple strategies including perplexity analysis, template matching, and learned classifiers.

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Data Engineering & Operations

We implement comprehensive data and MLOps practices specifically designed for LLM applications, from ingestion to deployment.

Our data engineering for LLM applications begins with robust ingestion pipelines that handle diverse data sources and formats. We implement streaming ingestion for real-time data sources, with exactly-once processing guarantees and automatic retry mechanisms.

We excel at data cleaning and preparation specifically for LLM consumption. Our text normalization pipelines handle encoding issues, special characters, and formatting inconsistencies that could impact model performance.

Our synthetic data generation capabilities address common challenges in LLM application development. We create diverse training examples for fine-tuning and few-shot learning using advanced prompting techniques and model cascades.

We implement specialized CI/CD pipelines for LLM applications that go beyond traditional software deployment. Our prompt version control systems track changes to system prompts, few-shot examples, and templates with full revision history.

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Security & Compliance Framework

We implement comprehensive security and privacy measures that protect sensitive data while ensuring regulatory compliance.

Our security implementations for AI systems address unique challenges posed by LLM applications and their access to sensitive data. We develop multi-layered security architectures that protect against both traditional and AI-specific threats.

We excel at PII detection and redaction within AI pipelines, protecting sensitive information throughout the processing lifecycle. Our pattern-based detection identifies common PII types including names, addresses, phone numbers, and financial information.

Our prompt sanitization systems prevent various forms of injection attacks and data exfiltration attempts. We implement input validation that detects and blocks malicious prompt patterns before they reach models.

We build comprehensive audit logging systems that maintain detailed records for compliance and forensics. Our immutable audit trails capture all AI system interactions including inputs, outputs, and metadata.

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Architectures & Reference Patterns

Proven architectural patterns for common AI use cases with detailed implementation guidance.

RAG-Powered Help Center

Self-service support system powered by RAG over documentation, tickets, and knowledge bases.

Our RAG-powered help center architecture delivers an intelligent, self-service support experience that empowers users to find answers instantly and accurately. By combining your documentation, historical support tickets, FAQs, and internal knowledge bases into a unified retrieval ecosystem, it ensures that valuable information from every corner of your organization is accessible when needed.

At its core, the retrieval layer employs a hybrid search mechanism that combines semantic similarity and keyword-based matching. Semantic search helps interpret conceptual or natural language queries, understanding user intent even when phrased differently, while keyword matching ensures precision for technical phrases, error codes, and specific product identifiers.

To further enhance accuracy, we implement query expansion and synonym mapping, enabling the system to recognize related terminology, alternate spellings, and variations commonly used in your support ecosystem. The result is a highly efficient, adaptive, and scalable help center that reduces ticket load, improves response quality, and provides users with faster, more personalized resolutions — all powered by cutting-edge Retrieval-Augmented Generation (RAG) technology.

Agentic Ops Copilot

Autonomous agent system for operations automation with human oversight and tool orchestration.

Our agentic ops copilot architecture creates intelligent automation for operational tasks while maintaining human oversight and control. The system uses ReAct (Reasoning and Acting) agents that break down complex operational procedures into executable steps.

The tool integration layer provides secure access to operational systems through standardized interfaces. We implement comprehensive permission models ensuring agents only perform authorized actions.

Multimodal Intake Assistant

Unified intake system processing voice, text, images, and documents for automated workflows.

Our multimodal intake assistant architecture processes diverse input types through a unified AI-powered pipeline. The system accepts voice inputs with real-time transcription and speaker diarization, text through various channels including email and chat, images for visual information extraction, and documents in multiple formats.

The multimodal LLM integration combines inputs intelligently, understanding relationships between different modalities. We implement context fusion that correlates information across voice transcripts, uploaded images, and attached documents.

Case Studies

Real-world AI implementations delivering measurable business impact.

Brand Identity, Social Media, Microsites

branchX

NEO based banking app that makes your life easy

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Website

HelpAG

Strengthening Digital Fortification for the Middle East's Cybersecurity Leader

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Web App, Mobile App

Queryloom

The Platform Powering Insightful Conversations through Polls and Questions

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Website, Brand Identities

Kinara Capital

Empowering Small Businesses with Accessible Capital

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Website

Aditya Birla

Asset Taxonomy Management Tool

Logo Design, UIUX Design

ninZa

Empowering Global Traders with Professional-Grade Tools

eCommerce

ARANTZA

eCommerce platform for Empowering Women's Health and Confidence

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Mobile App, Web App

Xride

Revolutionize Your Travel Experience with the Ultimate Taxi App Innovation

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UIUX Design

Spectraforce

Transforming Job Discovery Through Strategic Design

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UIUX Design

Volkswagne

Refined UI/UX experience for smarter automotive interactions.

UIUX Design, No Code Development

My Navro

Empowering Students with a Clean and Purpose-driven Digital Experience

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Web App

EXCELFORE

Webredesign for Silicon Valley Automakers for Software-Driven Vehicles

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Process: From Brief to Launch

Our structured approach ensures successful AI product delivery with clear milestones and deliverables.

Discovery (2-3 weeks)

Technical assessment, opportunity identification, and strategic planning.
Deliverables: AI readiness report, opportunity matrix, preliminary architecture.

Design (3-4 weeks)

Deliverables: AI readiness report, opportunity matrix, preliminary architecture.
Deliverables: Design systems, interaction flows, technical specifications.

Prototype (4-6 weeks)

MVP development, initial integrations, and proof of concept.
Deliverables: Working prototype, evaluation results, refinement recommendations.

Build (8-12 weeks)

Full implementation, integrations, and comprehensive testing.
Deliverables: Production-ready system, documentation, test suites.

Deploy (2-3 weeks)

Production deployment, monitoring setup, and initial optimization.
Deliverables: Deployed system, monitoring dashboards, runbooks.

Scale (Ongoing)

Performance optimization, feature expansion, and continuous improvement. Deliverables: Monthly reports, optimization recommendations, feature updates.

Tooling & Stack

Vendor-agnostic expertise across leading AI platforms and technologies.

Models

Open-weight and frontier models selected based on performance, cost, and requirements. Expertise across GPT, Claude, Gemini, Llama, Mistral, and specialized models.

Vector Databases

FAISS, PGVector, Pinecone, Weaviate, Milvus, Qdrant, ChromaDB. Selection based on scale, features, and operational requirements.

Orchestration

LangChain, LangGraph, LangFlow, Semantic Kernel, Haystack. Custom frameworks for specialized requirements.

Observability

LangSmith, Datadog, New Relic, Grafana, custom telemetry solutions. Comprehensive monitoring across all system components.

Pricing & Engagement Models

Flexible engagement options aligned with your project scope and business objectives.

$40,000 / per 2-week sprint

Sprint-Based Development

Defined deliverables with clear timelines and milestones.

Best for:

$75,000 starting price

Specific features.

Defined deliverables with clear timelines and milestones.

Best for:

$25,000+ / per month

Strategic Retainers

Ongoing partnership with dedicated team allocation

Best for:

Frequently Asked Questions

Transform, grow, and thrive with our experts.

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info@wilsonwings.com

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SoulSis - Together, Safe Everywhere

Salaah - Connecting Community

AI Product Design & Development Company

What We Do

Design Philosophy

Architectures & Reference Patterns

RAG-Powered Help Center

Agentic Ops Copilot

Multimodal Intake Assistant

Case Studies

Process: From Brief to Launch

Discovery (2-3 weeks)

Design (3-4 weeks)

Prototype (4-6 weeks)

Build (8-12 weeks)

Deploy (2-3 weeks)

Scale (Ongoing)

Tooling & Stack

Models

Vector Databases

Orchestration

Observability

Pricing & Engagement Models

$40,000 / per 2-week sprint

Sprint-Based Development

Best for:

$75,000 starting price

Specific features.

Best for:

$25,000+ / per month

Strategic Retainers

Best for:

Frequently Asked Questions

Transform, grow, and thrive with our experts.

Check Our Pricing

Explore plans that fit your design needs and budget.

Let our experts help you transform, grow and thrive your business.

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