Tech Stack

Composable and AI-first Architecture

We blend hybrid search, personalization, and automation with a composable architecture so teams can deliver measurable outcomes fast.

How the stack comes together

Plug-and-play by design. We handle the full stack behind the scenes, so you can focus on outcomes.
Modular services integrate with your current search, PIM, CMS, and analytics.
Hybrid retrieval merges lexical + semantic signals for stronger intent capture.
Personalization layers adapt results with customer, context, and merchandising signals.
Automation workflows orchestrate enrichment, indexing, and activation.

Core building blocks

Our composable technology pillars

Proven scalable and reusable building blocks for search, recommendations, enrichment, and automation—configured per client and measurable in impact.

Relational Databases

Reliable source of truth for product, pricing, and inventory data—governed, auditable, and easy to integrate with existing commerce stacks.

Vector Databases

Enables semantic retrieval (meaning/intent), similarity matching, and fast 'related/complementary' discovery for search, navigation, and recommendations.

AI & LLMs (Generative AI)

Turns messy inputs into usable outputs—intent understanding, enrichment, classification, summarization, and smarter experiences without rebuilding core platforms.

Embedding Models

Converts products and queries into comparable “meaning vectors” so you can move beyond keyword matching and improve relevance at scale.

Data Connectors & Feeds

Practical integration with real-world sources—product feeds, sitemaps, PIM/CMS, search platforms, and analytics signals.

Orchestration & Automation

Keeps solutions fast-to-market and maintainable—pipelines for ingestion, enrichment, syncing, monitoring, and experimentation without heavy engineering overhead.

API-First & Composable Architecture

Plug-in building blocks that augment (not replace) existing platforms—easy to integrate across channels and teams, and easy to iterate per client.

Cloud-Based Deployment

Secure, scalable, and production-ready deployment patterns with clear environments (dev/stage/prod), cost control, and operational reliability.

Observability (Logging, Monitoring, Analytics)

Shows whether it’s working—performance, errors, relevance KPIs, and usage insights to prove impact and continuously improve.

Python Services

Rapid prototyping to production-grade services—strong ecosystem for data pipelines, AI integration, and backend APIs.

Delivery Practices

Enables team collaboration, governance, auditability, and safe releases through structured change control.

Semantic search

How semantic search elevates commerce discovery

Semantic search helps shoppers find what they mean, not just what they type, so discovery stays strong even with vague, natural, or unfamiliar queries.

How we make search work better

Each product gets a 'meaning fingerprint' (embedding). A vector database can instantly find the closest matches to a shopper’s intent, then we refine with business rules and signals.

Handles messy queries: Natural language, misspellings, and unfamiliar terms still return good results.
Improves relevance: More “right first page,” less scrolling, less reformulating.
Expands exploration: Suggests related and complementary items to keep shoppers moving.
Adapts over time: Can learn from engagement signals to continuously improve.