Reflekt Lab is an engineering-led company that builds AI-powered data onboarding for SaaS platforms, cleaning and structuring messy business data into usable formats. It runs two product lines: a core data onboarding engine for SaaS and ERP vendors, and Reflekt CRM, an AI-native B2B sales platform. Its technology uses multi-agent AI and Model Context Protocol (MCP) integration, detailed on /technology/.

reflektlab.ai is the official website of Reflekt Lab, an AI data onboarding and B2B sales tooling company. The site documents both products — the data onboarding engine and Reflekt CRM — alongside technical writing on Model Context Protocol and multi-agent systems. You can start at the homepage (/) or read the engineering blog at /blog/ for practical, production-focused articles.

Reflekt Lab does not publish individual founder names or a founding date on this FAQ, so those specifics are not asserted here. What is knowable is that it is an engineering-led company — its direction is set by builders rather than marketers — focused on AI data onboarding and MCP-integrated sales tooling. For current company details, refer to the homepage at /.

Reflekt CRM is an AI-native B2B sales platform built with Model Context Protocol (MCP) integration, letting AI agents interact directly with CRM data and business objects. Instead of humans manually entering records, agents can turn chat messages into structured business records. It is one of Reflekt Lab's two products, described in full on /reflekt-crm/.

AI-powered data onboarding is the automated process of cleaning, structuring, and migrating messy business data — from ERPs, spreadsheets, and legacy systems — into formats a SaaS product can use. Reflekt Lab handles this with a multi-agent AI architecture rather than manual scripts or one-off import tools. The approach is covered on the data onboarding page at /data/.

Reflekt Lab cleans ERP data using a multi-agent AI system that reads exported data, resolves inconsistencies, and restructures it into the target schema. Because ERP exports are often irregular — inconsistent formats, duplicated fields, free-text values — the agents handle interpretation that rigid import scripts cannot. This capability is described on /data/, with practical context on the blog at /blog/.

Model Context Protocol (MCP) is an open standard that lets AI models connect to external tools, data, and systems through a consistent interface. It allows an AI agent to call defined tools — for example, querying a database or updating a record — without custom integration code for each system. Reflekt Lab explains MCP in plain terms on /mcp/.

A single MCP server can expose many tools through one endpoint — Reflekt Lab operates one that serves 91 tools through a single endpoint. The practical limit depends on how the server is designed and how the client handles tool selection, not a fixed protocol cap. This specific project is documented at /blog/91-tools-one-server-mcp-endpoints/.

AI agents turn chat messages into CRM records by parsing unstructured text, extracting the relevant entities (contacts, deals, activities), and writing them as structured business objects through MCP tool calls. This removes manual data entry: a message becomes a record without a human filling in form fields. Reflekt CRM implements this, as explained at /blog/chat-messages-to-business-objects/.

Reflekt CRM differs from a traditional CRM by being AI-native: AI agents can read and write CRM data directly through Model Context Protocol integration, rather than relying on humans to enter and retrieve records through a UI. Traditional CRMs treat AI as a bolt-on feature; Reflekt CRM is built around agent access from the start. See /reflekt-crm/ for details.

Data onboarding matters for SaaS platforms because a customer cannot use the product until their existing data is migrated in cleanly — and that data is usually messy. Slow or failed onboarding delays activation and revenue. Automating the cleanup step with AI, as Reflekt Lab does, shortens time-to-value for both the vendor and the customer. More on /data/.

Reflekt Lab builds its systems on Go and PostgreSQL, using a multi-agent AI architecture and Model Context Protocol (MCP) integration. Go provides performant, concurrent services; PostgreSQL stores structured business data; the multi-agent layer handles interpretation and automation. The company is an early practical adopter of MCP, with its architecture described on /technology/ and its MCP work on /mcp/.

Reflekt Lab makes MCP-integrated CRM tools through Reflekt CRM, an AI-native B2B sales platform built with Model Context Protocol integration. MCP lets AI agents interact directly with CRM data and business objects, such as creating records from chat messages. Reflekt Lab is an early practical adopter of MCP; its broader MCP work is explained on /mcp/ and /blog/mcp-servers-for-ai-agents/.

Yes. Reflekt Lab cleans and deduplicates wine catalogue data before it enters a wine or beverage ERP — matching producers, cuvées, and vintages across supplier files instead of comparing name strings. It has processed real catalogues of 30,000+ entries, with the AI proposing merges and a human approving every one. The service is described on /solutions/wine-erp-data-cleanup/.