Seclai

Seclai: build, run and govern AI agents over your own content Seclai is a platform for building AI agents, chat bots and MCP servers on top of knowledge bases that keep themselves current. Connect your content, design a multi-step workflow, pick from a wide catalog of models, and ship it to users, to your own code, or to other AI tools. Governance, prompt-injection scanning, evaluations and full run traces are built in, not added later. KNOWLEDGE BASES THAT STAY CURRENT Point Seclai at websites, RSS feeds, Google Drive or Dropbox folders, inbound email, or uploaded files. Content is pulled on a schedule, extracted, chunked and indexed for retrieval. Audio and video are transcribed, and images on web pages, in feeds and inside documents are indexed alongside the text, so retrieval works across media and not only on plain text. Metadata filters and reranking keep results focused on what a step needs. AGENT WORKFLOWS, NOT JUST PROMPTS Agents are workflows made of typed steps that you build in the editor or define as JSON. Steps cover retrieval, LLM prompt calls with tools, web search and fetch, data extraction (JSON, HTML, XML and structured data), branching (if/else and switch), loops over runtime lists, calls to other agents, calls to tools on external MCP servers, memory reads and writes, waits, and human-in-the-loop approval that pauses a run until reviewers decide. Outputs can go to webhooks, email, S3, back into a knowledge base, or stream straight to the caller. Image, audio and video generation steps are available too. An AI assistant can draft a workflow from a plain-language description to get you started. Agents run when new content arrives or changes, when a file is added to a connected cloud drive, when an email reaches the agent's own inbox, or on demand through the API. MODEL CHOICE WITHOUT LOCK-IN Choose models from Anthropic, OpenAI, Google and many other providers per step, with effort and reasoning controls where a model supports them. Seclai tracks model deprecations and sunset dates, warns accounts that use affected models, recommends successors, and can upgrade a step automatically with optional rollback. Requests can fail over across vendors that host the same model. GOVERNANCE AND SAFETY BUILT IN Content is screened where it enters: agent input, external data returned by a step, and new content arriving in a source. Define governance policies with configurable evaluation tiers, hold content for human review, and keep an audit trail of every policy change. A dedicated classifier scans inputs for jailbreak attempts. Inbound email is gated by sender allowlists backed by DMARC, not just a bare signature check. QUALITY YOU CAN MEASURE Agent evaluations score step outputs with an LLM judge, either on every run with automatic retry, as sampled monitoring in production, or as manual validation. Evaluations can run retroactively over past runs. Every run produces a step-by-step trace that streams live while the agent is working, including inputs, outputs, tokens and credit cost for each step. MEMORY AND CONVERSATIONS Named memory banks give agents durable facts and per-conversation chat history with vector search and automatic compaction, so long-running assistants keep context without blowing up prompt size. BUILT FOR DEVELOPERS Agents, knowledge bases and runs are available through a versioned REST API, with official SDKs for Python, JavaScript/TypeScript, Go and C#, plus a CLI. Seclai also runs as an MCP server, so tools such as Claude and ChatGPT can query your knowledge bases and run your agents directly, using OAuth or API keys. Agent definitions export as JSON for version control and import into another account or environment, and API versioning protects existing integrations as the platform evolves. FOR TEAMS AND LEADERS Organizations share agents, knowledge bases and connections across members. Usage is metered in credits with per-run and per-step cost visibility, low-credit alerts and automatic top-up, so engineering leaders and product managers can see what an agent costs before it scales. Alerts notify the right people when runs fail, evaluations miss, or a model is about to be retired. TYPICAL USES - Support and internal assistants grounded in your docs, wiki and policies - Monitoring of websites, feeds and inboxes that summarizes, classifies and routes what changed - Document intake pipelines that extract structured data from files and email attachments - Research and reporting agents that combine your content with live web search - MCP endpoints that give AI coding and chat tools governed access to company knowledge Start with a free trial, connect a source, and have a working agent with traces and evaluations in minutes.

Seclai provides production infrastructure for AI applications, combining LLM workflow orchestration with evaluation, governance, observability, and model selection. It helps teams manage the parts around an LLM call, including retries, content screening, cost tracking, retrieval infrastructure, and persistent memory. Teams can build and deploy agents, inspect and replay traces, and receive outputs, costs, traces, and governance verdicts from agent calls. Users add a content source by entering a URL or uploading a file; Seclai detects the type and sets up syncing. It indexes content into a vector database for retrieval. Users connect a knowledge base to a workflow in the visual builder, then deploy the agent. Each agent has an HTTP endpoint, and SDKs are available for Python, JavaScript, Go, and C#. Calls return an output, trace, cost, and governance verdict. Seclai is for teams building production AI applications, particularly teams that have to answer for what they ship. It brings workflow orchestration, evaluations, governance, observability, model choice, and deployment into one runtime. The page describes parity between local and production behavior, with agents that can be cloned while retaining the same workflows, models, policies, and evaluations.