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| Match Graph | 0 | 0 |
| Pricing | Free | Paid |
| Capabilities | 13 decomposed | 14 decomposed |
| Times Matched | 0 | 0 |
Queries a proprietary 450M+ contact database using a filter-based search interface supporting 15+ dimensions (company size, industry, location, job title, seniority, job changes, VC funding, revenue, founding year, recruiting status, department, keywords). The search executes server-side queries against indexed contact records and returns results as in-platform lists or CSV exports, with export limits enforced per tier (100 leads/export on free tier, unlimited on paid).
Unique: Combines 40+ data providers via waterfall enrichment into a single queryable 450M contact index with multi-dimensional filtering (job changes, VC funding, revenue, recruiting status) rather than simple keyword search like LinkedIn Sales Navigator. Enforces tier-based export limits (100 vs unlimited) to drive monetization.
vs alternatives: Cheaper than LinkedIn Sales Navigator ($59/month vs $99/month) with more structured company data (revenue, VC funding, founding year) but smaller user base means fewer integrations and less market validation than Apollo or ZoomInfo.
Enriches partial contact records (email or phone) by querying a waterfall of 40+ third-party data providers in sequence, returning the first available match for each field (email, phone, company, title, etc.). Enrichment is credit-based (1 credit per email validation, 10 credits per phone number) and available via UI, bulk Enrichment Hub (up to 10,000 contacts/batch), Chrome Extension, or API. The system validates email deliverability and appends phone numbers with lower confidence (higher credit cost).
Unique: Uses waterfall aggregation across 40+ providers (specific providers undisclosed) rather than single-source enrichment, increasing coverage but obscuring data freshness and quality. Credit-based pricing (1 credit/email, 10 credits/phone) reflects confidence levels and provider availability. Bulk enrichment capped at 10K/batch suggests batch-queue architecture rather than real-time streaming.
vs alternatives: Cheaper per-contact than RocketReach or Clearbit ($0.08/email on Starter plan vs $0.50+ per contact) but lacks transparency on data sources and accuracy guarantees, making it riskier for teams requiring high-confidence contact data.
Integrates with Aircall and Ringover VoIP dialers to enable click-to-call from Zeliq platform and automatic call logging to HubSpot. Users can initiate calls directly from prospect records or sequences, with call duration and outcome tracked in Zeliq and synced to CRM. Phone calls consume credits (1 credit per call on Starter plan = 750 calls/month). Call recording and transcription appear to be handled by dialer (Aircall/Ringover), not Zeliq.
Unique: Integrates click-to-call with Aircall/Ringover and automatic HubSpot logging, reducing context-switching between dialer and CRM. Phone calls consume credits (1 credit/call), creating unified cost model with email and SMS. No call recording/transcription or advanced dialer features (voicemail drop, IVR) mentioned.
vs alternatives: Cheaper than separate Outreach ($99+/month) + Aircall ($50+/month) = $150+/month, but limited to Aircall/Ringover only; competitors support broader dialer ecosystem.
Exports prospect lists from Zeliq search or enrichment as CSV files for use in external tools (CRM, email marketing, spreadsheets). Free tier limited to 100 leads per export; paid tiers (Starter+) allow unlimited exports. Export includes enriched fields (email, phone, company, title, LinkedIn URL, etc.) and can be filtered before export. Export mechanism (immediate download vs queued/emailed) not specified.
Unique: Enforces tier-based export limits (100 leads on free, unlimited on paid) to drive monetization. CSV-only export format limits flexibility vs competitors offering JSON, Excel, and API-based exports. No scheduled exports or field mapping mentioned.
vs alternatives: Similar to Apollo and ZoomInfo export, but free tier limit (100 leads) is more restrictive than competitors offering 500+ free exports, creating stronger paywall pressure.
Zeliq claims 'real-time data' and 'prospect information stays fresher than static database competitors,' but provides no specifics on: data refresh frequency, update latency, coverage of data sources, or freshness guarantees. The 450M contact database is sourced from 40+ providers via waterfall enrichment, but update frequency per provider is undisclosed. This capability appears to be a marketing claim rather than a documented technical feature.
Unique: Zeliq claims 'real-time data' and 'fresher than static database competitors' but provides zero technical transparency on refresh frequency, update latency, or freshness guarantees. This is a marketing claim without documented SLA or methodology.
vs alternatives: Unknown — insufficient data on how Zeliq's data freshness compares to Apollo, ZoomInfo, or other competitors. Lack of SLA makes it impossible to assess whether 'real-time' claim is accurate or marketing hyperbole.
Automates multi-step outreach campaigns across email, SMS, social messages, and phone calls by executing pre-defined sequences against recipient lists. Sequences are template-based (mechanism for personalization unspecified) and can include delays, conditional branching (inferred), and integration with dialers (Aircall/Ringover) for phone calls. Free tier limited to 2 active email-only sequences; paid tiers support unlimited sequences with multi-channel capabilities. Delivery mechanism (real-time vs batched) and personalization depth (template variables vs dynamic content) are undisclosed.
Unique: Combines lead search, enrichment, and multi-channel sequencing in single platform (vs separate tools like Apollo + Outreach), reducing tool sprawl. Credit-based phone call pricing (750 credits/month on Starter = 75 calls) integrates calling cost into single subscription rather than separate dialer fees. Sequence limits enforced per tier (2 on free, unlimited on paid) to drive monetization.
vs alternatives: All-in-one cheaper than Outreach ($99+/month) + Apollo ($49+/month) + dialer ($50+/month) = $200+/month, but lacks advanced features like AI-powered subject line testing, predictive send times, and conditional logic that Outreach provides.
Syncs Zeliq-generated leads and outreach activities (emails sent, calls made, replies received) bidirectionally with HubSpot CRM, automatically creating/updating contact records and logging activities without manual data entry. The sync mechanism (webhook-based, scheduled batch, real-time API polling) is undisclosed. Two-way sync implies HubSpot updates (e.g., deal stage changes) may flow back to Zeliq, but specifics are unconfirmed. Sync is included in Starter plan and higher; free tier status unclear.
Unique: Integrates lead sourcing, enrichment, and outreach sequencing with HubSpot in single platform, eliminating manual CRM data entry. Two-way sync (inferred) suggests bidirectional data flow, but sync mechanism (webhook vs batch vs polling) and latency are undisclosed. Sync included in Starter plan ($59/month) vs standalone CRM integrations that charge per-sync or per-record.
vs alternatives: Cheaper than Outreach + HubSpot integration ($99+ + $50+ = $150+/month) but limited to HubSpot only; competitors like Apollo support Salesforce, Pipedrive, and other CRMs, making Zeliq less flexible for multi-CRM enterprises.
Provides team-level lead assignment and performance tracking via a manager dashboard showing individual rep metrics (leads assigned, emails sent, calls made, replies received, conversion rates) and team aggregates. Lead distribution mechanism (manual assignment, round-robin, AI-based routing) is undisclosed. Dashboard displays real-time or near-real-time metrics (refresh frequency unknown) and integrates with sequence execution to track outreach outcomes per rep.
Unique: Combines lead distribution, sequence execution, and performance tracking in single platform vs separate tools (Apollo for sourcing + Outreach for sequencing + Salesforce for reporting). Lead assignment mechanism (manual vs round-robin vs AI) undisclosed, suggesting either simple manual assignment or proprietary routing algorithm.
vs alternatives: Cheaper than Outreach ($99+/month) + Salesforce ($165+/month) for team visibility, but lacks advanced forecasting and predictive analytics that Salesforce Einstein provides.
+5 more capabilities
Automatically loads and parses documents from diverse sources (PDFs, Word docs, HTML, Markdown, code files, databases) into a unified in-memory representation using format-specific loaders and node-based document abstractions. Each document is decomposed into Document objects containing metadata, content, and relationships, enabling downstream processing without format-specific handling in application code.
Unique: Provides a unified loader abstraction (BaseReader interface) that normalizes 100+ data source connectors into a single Document/Node API, eliminating format-specific branching logic in application code. Loaders are composable and chainable, allowing sequential transformations (e.g., load → split → extract metadata → embed).
vs alternatives: Broader out-of-the-box loader coverage than LangChain's document loaders and more structured node-based decomposition than raw text splitting, reducing boilerplate for multi-source RAG pipelines.
Splits documents into semantically coherent chunks using multiple strategies (character-based, token-aware, recursive, semantic) with configurable overlap and chunk size. Preserves document hierarchy and metadata through a node tree structure, enabling retrieval systems to maintain context relationships and enable hierarchical re-ranking or parent-document retrieval patterns.
Unique: Implements a node-tree abstraction that preserves document hierarchy and enables parent-document retrieval patterns. Supports multiple splitting strategies (recursive, semantic, code-aware) with pluggable custom splitters, and automatically propagates metadata through the node tree.
vs alternatives: More sophisticated than LangChain's text splitters because it preserves hierarchical relationships and supports semantic splitting; better for complex document structures than simple character-based splitting.
LlamaIndex scores higher at 40/100 vs Zeliq at 39/100. However, Zeliq offers a free tier which may be better for getting started.
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Processes documents containing mixed content (text, images, tables, code) by extracting and understanding each modality separately, then synthesizing information across modalities. Uses vision models for image understanding, specialized parsers for tables and code, and integrates results into a unified document representation for retrieval and generation.
Unique: Integrates vision models, table parsers, and code extractors into a unified multi-modal document processing pipeline that synthesizes information across modalities. Preserves modality-specific structure (table schemas, code formatting) while enabling cross-modal retrieval and generation.
vs alternatives: More comprehensive multi-modal support than text-only RAG; built-in vision integration reduces boilerplate for document understanding compared to manual vision API calls.
Enables streaming of LLM responses token-by-token and real-time retrieval updates, allowing applications to display partial results as they become available. Supports streaming from retrieval (progressive document discovery) and generation (token-by-token output) with backpressure handling and cancellation support for responsive user experiences.
Unique: Provides first-class streaming support for both retrieval and generation with automatic backpressure handling and cancellation. Enables progressive result display without custom async/streaming code in application layer.
vs alternatives: More integrated streaming support than manual LLM API streaming; built-in retrieval streaming and backpressure handling reduce complexity compared to custom streaming implementations.
Tracks API costs for LLM calls, embeddings, and other operations with per-query and per-session cost attribution. Provides cost optimization recommendations (e.g., batch processing, model selection, caching) and enables cost-aware query planning to balance quality and expense. Integrates with multiple LLM providers to normalize cost tracking across models.
Unique: Provides automatic cost tracking across multiple LLM providers with per-query attribution and cost optimization recommendations. Integrates with query execution to enable cost-aware planning without manual cost calculation.
vs alternatives: More integrated cost tracking than manual API billing review; built-in optimization recommendations reduce guesswork for cost reduction.
Enables building custom RAG pipelines by composing modular components (retrievers, synthesizers, agents, tools) through a declarative or programmatic API. Supports complex workflows with branching, loops, and conditional logic, with automatic dependency resolution and execution optimization. Pipelines are reusable, testable, and can be deployed as APIs or batch jobs.
Unique: Provides a flexible pipeline composition API supporting both declarative and programmatic definitions, with automatic dependency resolution and execution optimization. Enables complex workflows with branching and conditional logic without custom orchestration code.
vs alternatives: More flexible pipeline composition than fixed RAG architectures; better workflow support than manual component chaining.
Generates embeddings for documents/nodes using pluggable embedding providers (OpenAI, Hugging Face, local models) and stores them in a unified vector store interface that abstracts over multiple backends (Pinecone, Weaviate, Milvus, FAISS, Chroma, etc.). The abstraction layer enables switching vector stores without changing application code, and handles batching, retry logic, and metadata indexing.
Unique: Provides a unified VectorStore interface that abstracts 10+ vector database backends, enabling zero-code switching between providers. Handles embedding batching, retry logic, and metadata propagation automatically. Supports both cloud and local embedding models through a pluggable EmbedModel interface.
vs alternatives: Broader vector store coverage and more seamless provider switching than LangChain's vectorstore integrations; better abstraction consistency across backends than using raw vector store SDKs directly.
Retrieves semantically similar documents from vector stores using embedding-based similarity search, with optional re-ranking, filtering, and fusion strategies (hybrid search combining dense and sparse retrieval). Supports multiple retrieval modes (similarity, MMR, fusion) and enables custom retrieval logic through a pluggable Retriever interface that can combine multiple strategies.
Unique: Implements a pluggable Retriever abstraction supporting multiple retrieval strategies (similarity, MMR, fusion, custom) that can be composed and chained. Built-in support for re-ranking via LLM or cross-encoder, and hybrid search combining dense and sparse retrieval without custom integration code.
vs alternatives: More flexible retrieval composition than LangChain's retrievers; built-in re-ranking and fusion strategies reduce boilerplate for advanced retrieval pipelines.
+6 more capabilities