DokBot vs Maximem Synap: Features, Pricing & Which Is Better (2026)

A side-by-side comparison of DokBot and Maximem Synap — features, pricing, and ideal use cases — to help you decide which AI tool fits your workflow.

DokBot

Freemium

DokBot turns your PDFs and documents into an embeddable RAG support chatbot for your website in minutes, using your own API key or built-in credits.

Multi-Format RAG Knowledge Bases: Native ingestion of PDF, DOCX, XLSX, MD and TXT files with automatic processing and chunking — no copy-pasting text.
Bring Your Own API Key: Connect OpenAI, Anthropic, Google AI Studio, Groq, OpenRouter, CheaperInference or Ollama Cloud directly without a middleman.
Managed AI Credits: Optionally buy monthly AI credits so DokBot runs the model on its own provider key and bots need no API key.
Human Handoff: When the bot doesn't know an answer it collects the visitor's email and surfaces the full conversation in the Conversations inbox.
Shadow DOM Embeddable Widget: Install with one script tag before </body>, fully isolated so it never breaks your site's CSS.
Live Preview Testing: Iterate on prompts and verify answer accuracy in a live preview before going public.
MCP Access: Pro plan exposes MCP so your own AI assistant can manage bots and knowledge bases.

Website Customer Support: Answer common visitor questions 24/7 from product manuals and help docs.
Documentation Assistant: Let users query long technical PDFs or guides conversationally instead of searching manually.
Lead Capture from Unanswered Questions: Collect emails when the bot can't answer and follow up with full context.
Low-Cost Support Automation: Small businesses run a support bot on free-tier or cheap model providers via BYOK.
Multilingual SMB Support: Spanish-speaking businesses deploy a docs-grounded assistant on their site in minutes.
Agency Client Bots: Build and manage multiple bots with separate knowledge bases for different client sites.

Maximem

Freemium

Maximem Synap is an agentic context management platform that gives AI agents persistent, scoped memory with sub-15ms retrieval.

Two-Call Memory API: Record messages asynchronously and fetch ranked, prompt-ready context before each reply — writes never block the agent.
Three-Layer Memory Model: Separate organizational, long-term per-user and short-term session memory with customer and client scoping for multi-tenant isolation.
Entity Resolution & Structured Extraction: Converts raw dialogue into structured facts and merges aliases of the same person or company automatically.
Anticipatory Retrieval: Pre-fetches context during the conversation for under 15ms P75 retrieval, fast enough for voice agents.
Agentic Compaction & Consolidation: Background cycles compact and consciously forget stale information to keep context lean and cut token costs.
Hybrid Vector, Graph & File Storage: Retrieval nets across semantic vectors, entity relationship graphs and raw documents, with FAST/ACCURATE/HYBRID modes.
Broad Framework Support: Python and TypeScript SDKs, REST and WebSocket APIs, a hosted MCP endpoint and native adapters for 23 frameworks including LangChain, CrewAI, OpenAI Agents and Claude Agent SDK.
Enterprise Controls: BYOK model providers, audit trails, webhooks, custom memory schemas, and VPC deployment, SSO/SAML and RBAC on enterprise plans.

Personalized Support Agents: Customer service bots remember prior issues and preferences across sessions instead of re-asking users.
Voice Agents: Real-time voice assistants pull user context in milliseconds without conversational pauses.
Multi-Tenant SaaS Assistants: B2B products keep each customer's memory isolated while sharing organization-wide policies and product facts.
Token Cost Reduction: Teams replace full-history prompt stuffing with compact ranked context to lower LLM spend.
Long-Running Copilots: Coding or productivity assistants recall what a user worked on days or weeks earlier.
Replacing Homegrown RAG Memory: Engineering teams avoid building and maintaining their own extraction, vector DB and ranking pipeline.