| Ecosystem | 0 | 1 |
| Match Graph | 0 | 0 |
| Pricing | Paid | Free |
| Capabilities | 13 decomposed | 12 decomposed |
| Times Matched | 0 | 0 |
Tekst ingests customer messages from multiple communication channels (email, SMS, chat, social media) and normalizes them into a unified message format before routing to workflows. The platform uses channel-specific adapters that translate protocol-specific metadata (sender IDs, timestamps, attachments) into a common schema, enabling downstream workflow logic to operate channel-agnostically without reimplementation per channel.
Unique: Uses channel-specific adapter pattern with unified schema translation rather than a single message format, preserving channel-native metadata while enabling cross-channel workflow logic without reimplementation
vs alternatives: More flexible than Zendesk's channel routing because adapters are composable and extensible, vs Intercom's tighter channel coupling that requires channel-specific workflow branches
Tekst encrypts all customer messages at rest and in transit using TLS 1.3 for network transport and AES-256-GCM for storage encryption. The platform implements key management with per-tenant encryption keys, ensuring that even Tekst infrastructure cannot decrypt customer data without explicit key access. Encryption is applied at the message ingestion point before any processing, and decryption occurs only at the point of display or workflow execution.
Unique: Implements per-tenant encryption keys with customer-managed key option (BYOK), enabling organizations to retain full cryptographic control rather than relying on provider-managed keys
vs alternatives: Stronger security posture than Zendesk or Intercom, which offer encryption but retain key management; comparable to enterprise Slack or Teams but with tighter integration into support workflows
Tekst provides a library of pre-written response templates that agents can use to quickly reply to common customer inquiries. Templates support variable substitution (e.g., {{customer_name}}, {{ticket_id}}) and conditional sections (e.g., show billing info only if category is 'billing'). Agents can search templates by keyword, create custom templates, and track template usage. Templates can be organized by category and shared across teams. The system suggests relevant templates based on message category or customer history.
Unique: Supports conditional template sections and variable substitution with team-wide sharing and usage tracking, rather than simple copy-paste snippets
vs alternatives: More structured than manual snippets, but less intelligent than AI-powered response suggestions (e.g., Intercom's AI-suggested replies using LLMs)
Tekst maintains a complete conversation history for each customer across all channels and time periods, enabling agents to view full context when responding to new messages. The system automatically retrieves relevant past conversations (e.g., previous issues, purchases, complaints) and displays them alongside the current message. Context includes message text, attachments, resolution status, and associated tickets. Agents can manually search for specific past conversations or use AI-powered context suggestions (if enabled).
Unique: Maintains unified conversation history across all channels and time periods, enabling agents to see full customer context without manual channel switching
vs alternatives: More comprehensive than single-channel history (e.g., email-only), but less intelligent than AI-powered context summarization (e.g., Intercom's AI summaries)
Tekst provides dashboards and reports showing key support metrics: message volume, response time, resolution time, customer satisfaction (CSAT), agent utilization, and SLA compliance. Metrics are aggregated by time period (daily, weekly, monthly), team, agent, and category. Reports can be scheduled and emailed automatically. The system supports custom metrics and KPIs via formula-based calculations. Data is visualized in charts (line, bar, pie) and tables for easy analysis.
Unique: Provides pre-built dashboards for common support metrics (response time, resolution time, CSAT, SLA compliance) with customizable time periods and aggregations
vs alternatives: More integrated than external BI tools (Tableau, Looker) but less flexible; comparable to Zendesk or Freshdesk's native analytics
Tekst uses rule-based and machine-learning-based categorization to automatically classify incoming messages by intent, urgency, or topic, then routes them to appropriate teams or workflows. The system learns from historical message labels and routing decisions, building a classifier that improves over time. Routing rules are expressed as a declarative workflow language that supports conditional logic (if-then-else), team assignment, priority escalation, and SLA-based queuing.
Unique: Combines rule-based routing with incremental ML learning from historical decisions, allowing teams to start with explicit rules and gradually transition to learned patterns without manual retraining
vs alternatives: More transparent than Zendesk's black-box routing (rules are visible and debuggable), but less sophisticated than Intercom's AI-driven intent detection which uses deep learning on large corpora
Tekst provides a workflow engine that executes multi-step automation sequences triggered by message events (arrival, categorization, customer response). Workflows are defined declaratively using a state machine pattern, supporting branching (if-then-else), loops, delays, and external action invocations (API calls, CRM updates, email sends). The engine maintains workflow state across message interactions, enabling context-aware responses and multi-turn automation.
Unique: Uses explicit state machine pattern for workflows, making execution flow visible and debuggable, rather than implicit callback chains; supports long-running workflows with delays and human handoff points
vs alternatives: More transparent than Zapier's black-box automation (workflows are inspectable), but less feature-rich than enterprise workflow engines like Temporal or Airflow which support distributed execution and complex retry logic
Tekst provides pre-built connectors for popular CRM (Salesforce, HubSpot) and helpdesk (Jira Service Desk, Freshdesk) systems, enabling bidirectional data sync without custom API development. Integrations use webhook-based event streaming for real-time updates: when a message arrives in Tekst, customer data is fetched from the CRM; when a ticket is resolved in Tekst, the status is pushed back to the helpdesk. Integrations are configured through a UI with field mapping and transformation rules.
Unique: Provides pre-built connectors with UI-based field mapping and webhook-driven real-time sync, reducing integration friction compared to building custom API clients
vs alternatives: Faster to implement than custom REST API integrations, but less flexible than Zapier or MuleSoft for complex transformations; comparable to Intercom's native Salesforce integration but with broader platform support
+5 more capabilities
Sends text messages, media files, and formatted content to Telegram chats and channels through the Telegram Bot API. Implements message routing logic that resolves chat identifiers (numeric IDs, usernames, or channel handles) to API endpoints, handles message formatting (Markdown/HTML), and manages delivery confirmation through API response parsing. Supports batch message operations and message editing after delivery.
Unique: Wraps Telegram Bot API message endpoints as MCP tools, enabling LLM agents to send messages through a standardized tool-calling interface rather than direct API calls. Abstracts chat identifier resolution and message formatting into a single composable capability.
vs alternatives: Simpler integration than raw Telegram Bot API for MCP-based agents because it handles authentication and endpoint routing transparently, while maintaining full API feature support.
Retrieves message history from Telegram chats and channels by querying the Telegram Bot API for recent messages, with filtering by date range, sender, or message type. Implements pagination logic to handle large message sets and parses API responses into structured message objects containing sender info, timestamps, content, and media metadata. Supports reading from both private chats and public channels.
Unique: Exposes Telegram message retrieval as MCP tools with built-in pagination and filtering, allowing LLM agents to fetch and reason over chat history without managing API pagination or response parsing themselves. Structures raw API responses into agent-friendly formats.
vs alternatives: More accessible than direct Telegram Bot API calls for agents because it abstracts pagination and response normalization; simpler than building a custom Telegram client library for basic history needs.
Telegram MCP Server scores higher at 60/100 vs Tekst at 44/100. Telegram MCP Server also has a free tier, making it more accessible.
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Integrates with Telegram's webhook system to receive real-time updates (messages, callbacks, edits) via HTTP POST requests. The MCP server can be configured to work with webhook-based bots (alternative to polling), receiving updates from Telegram's servers and routing them to connected LLM clients. Supports update filtering and acknowledgment.
Unique: Bridges Telegram's webhook system into MCP, enabling event-driven bot architectures. Handles webhook registration and update routing without requiring polling loops.
vs alternatives: Lower latency than polling because updates arrive immediately; more scalable than getUpdates polling because it eliminates constant API calls and reduces rate-limit pressure.
Translates Telegram Bot API errors and responses into structured MCP-compatible formats. The MCP server catches API failures (rate limits, invalid parameters, permission errors) and maps them to descriptive error objects that LLMs can reason about. Implements retry logic for transient failures and provides actionable error messages.
Unique: Implements error mapping layer that translates raw Telegram API errors into LLM-friendly error objects. Provides structured error information that LLMs can use for decision-making and recovery.
vs alternatives: More actionable than raw API errors because it provides context and recovery suggestions; more reliable than ignoring errors because it enables LLM agents to handle failures intelligently.
Registers custom bot commands (e.g., /start, /help, /custom) and routes incoming Telegram messages containing those commands to handler functions. Implements command parsing logic that extracts command names and arguments from message text, matches them against registered handlers, and invokes the appropriate handler with parsed parameters. Supports command help text generation and command discovery via /help.
Unique: Provides MCP-compatible command registration and dispatch, allowing agents to define Telegram bot commands as MCP tools rather than managing raw message parsing. Decouples command definition from message handling logic.
vs alternatives: Cleaner than raw message event handling because it abstracts command parsing and routing; more flexible than hardcoded command lists because handlers can be registered dynamically at runtime.
Fetches metadata about Telegram chats and channels including member counts, titles, descriptions, pinned messages, and permissions. Queries the Telegram Bot API for chat information and parses responses into structured objects. Supports both private chats and public channels, with different metadata availability depending on bot permissions and chat type.
Unique: Exposes Telegram chat metadata as queryable MCP tools, allowing agents to inspect chat state and permissions without direct API calls. Structures metadata into agent-friendly formats with permission flags.
vs alternatives: More convenient than raw API calls for agents because it abstracts permission checking and response normalization; enables agents to make permission-aware decisions before attempting actions.
Retrieves information about Telegram users and chat members including usernames, first/last names, profile pictures, and member status (admin, restricted, etc.). Queries the Telegram Bot API for user objects and member information, with support for looking up users by ID or username. Returns structured user profiles with permission and status flags.
Unique: Provides user and member lookup as MCP tools with structured output, enabling agents to make permission-aware and user-aware decisions. Abstracts API response parsing and permission flag interpretation.
vs alternatives: Simpler than raw API calls for agents because it returns normalized user objects with permission flags; enables agents to check user status without managing API response structure.
Edits or deletes previously sent messages in Telegram chats by message ID. Implements message lifecycle management through Telegram Bot API endpoints, supporting text content updates, media replacement, and inline keyboard modifications. Handles permission checks and error cases (e.g., message too old to edit, insufficient permissions).
Unique: Exposes message editing and deletion as MCP tools with built-in permission and time-window validation, allowing agents to manage message state without directly handling API constraints. Abstracts 48-hour edit window checks.
vs alternatives: More agent-friendly than raw API calls because it validates edit eligibility before attempting operations; enables agents to implement message lifecycle patterns without manual constraint checking.
+4 more capabilities