Most sites are invisible to modern AI agents and have poor in-site discovery.
Customers expect instant answers. Traditional site search, thin content, and missing structured data make it hard for both humans and AI to find accurate information — leading to lost sales and frustrated users.
Unstructured content
Important product and help content is buried in HTML with no semantic signals for machines to understand.
Poor search UX
Site search is slow, returns irrelevant results, and cannot power conversational experiences or recommendations.
No embeddings or knowledge base
Without vectorized content or a knowledge graph, AI agents cannot surface context-aware answers or handle follow-ups.
Indexing gaps
Missing sitemaps, robots rules, or schema markup prevents search engines and crawlers from properly indexing crucial pages.
Build a semantic, indexed, and embedding-powered discovery stack.
We make sites AI-agent ready by structuring content, building vector search, adding schema and knowledge graphs, and exposing safe conversational endpoints for users and assistants.
Content engineering
We structure copy, FAQs, product data, and docs into machine-readable formats and canonical content nodes.
Schema & indexing
Implement structured data (JSON-LD), sitemaps, and robots to improve search engine and crawler visibility.
Embeddings & vector search
Generate embeddings for pages and assets, store them in a vector DB, and serve fast semantic search for AI and UI use.
Conversational agents
Design agent prompts, safety layers, and RAG (retrieval-augmented generation) flows for accurate, up-to-date answers.
Monitoring & analytics
Track query intents, agent handoffs, and search performance; use data to refine knowledge and UX continuously.
Search UI & recommenders
Polish UX with instant suggestions, facets, and hybrid ranking (semantic + lexical) to improve conversions.
What we deliver to make discovery reliable and fast.
From data audits to production agents, our work covers the full stack required for modern discovery.
Content audit & mapping
Identify canonical pages, consolidate duplicates, and map content to user intents.
Structured data implementation
JSON-LD, Product, FAQ, Article, Breadcrumbs — schema that helps search engines and agents.
Embeddings pipeline
Text preprocessing, chunking, embedding generation, and vector DB orchestration.
API & agent endpoints
Secure endpoints for agents and chat experiences with rate limits, caching, and audit logs.
Search UX
Instant search, fuzzy matching, faceted filters, and mobile-first interactions.
Privacy & compliance
Redaction, user-consent handling, and opt-out for sensitive data surfaced to agents.
From audit to production: pragmatic engineering and content ops.
We combine engineering, content strategy, and ML operations so your discovery stack stays accurate and scalable.
Discovery & audit
We crawl and audit content, analytics, and search logs to find coverage gaps and high-value intents.
Information architecture
Define canonical nodes, taxonomy, and schema that make content machine-readable and human-friendly.
Embedding & index build
Chunk content, generate embeddings, and build a vector index with hybrid ranking strategies.
Agent design & safety
Create prompt templates, candidate responses, guardrails, and fallback flows for uncertain queries.
Launch & iterate
Deploy, monitor query quality, and iterate on content and routing to continuously improve performance.
Modern tools for safe, performant discovery.
We pick technologies that align with scale, data sensitivity, and latency needs.
Embedding providers
OpenAI, Anthropic, or on-prem alternatives depending on privacy and budget.
Vector databases
Pinecone, Milvus, Weaviate, or managed vector stores for low-latency semantic search.
RAG & agents
Retrieval-augmented generation patterns, prompt templates, and multi-step reasoning flows.
Search infra
Hybrid search stacks combining Elasticsearch/Opensearch with vector layers for best relevance.
Observability
Logging, query analytics, and feedback loops to measure accuracy, drift, and user satisfaction.
Integrations
CMS connectors, e-commerce feeds, and API endpoints so agents always serve fresh data.
Packages built for discovery maturity.
We offer staged engagements from rapid audits to full production agent builds and long-term content ops.
Discovery Audit
₹80,000 - ₹1,40,000
Crawl, content map, search log analysis, and prioritized roadmap.
Search & Index Build
₹2,50,000 - ₹5,50,000
Embedding pipeline, vector DB, and search UI integration.
Agent Production
₹6,00,000+
Conversational agent, safety, monitoring, and SLA-driven support.
Practical artifacts that power discovery and agents.
Audit report & roadmap
Detailed findings, prioritized fixes, and success metrics.
Content map & canonicalization
Clean structure and canonical content units that reduce duplication and improve recall.
Schema & JSON-LD
Structured data implemented site-wide for SEO and agent consumption.
Embeddings & vector index
Precomputed embeddings and a production vector store for semantic retrieval.
Agent endpoints
Secure APIs that power chat, voice, and internal assistant use cases.
Monitoring & feedback tooling
Query analytics, relevance metrics, and tools for continuous improvements.
Common questions about AI discovery and agent integration.
How long does it take to go live?
Small audits and pilot search integrations can launch in 2–4 weeks. Full agent production typically takes 8–16 weeks depending on data complexity and approvals.
Will this affect SEO?
Yes — when done correctly, structured data and canonicalization improve SEO. We avoid risky changes and follow best practices to preserve existing rankings.
Do you store user data?
We follow privacy-first designs. Sensitive user data is redacted before indexing and we support on-prem or private cloud options when required.
Which platforms do you integrate with?
We integrate with most CMS, e-commerce platforms, and CRMs via APIs or connectors, and can adapt to custom platforms as needed.
How do you measure success?
We track search relevance, click-through rates, agent resolution rate, conversion uplift, and qualitative user feedback to measure impact.