Introduction

Seclai is a production LLM platform that handles everything around the model call — model portability, retries, evaluation, observability, RAG, memory, governance, prompt safety, and cost management — so you can ship AI features with confidence.

What is Seclai?

Seclai lets you build multi-step LLM workflows that go beyond a single API call. Connect content sources, build retrieval-augmented pipelines, give agents persistent memory, and deploy workflows with built-in safety, evaluation, and full observability. Every real-world LLM feature involves multiple model calls — Seclai provides the production infrastructure to make them reliable, observable, and cost-effective.

Key Features

• 90+ AI Models — Access models from OpenAI, Anthropic, Google, Amazon, DeepSeek, xAI, Meta, Mistral, and more with a single integration. Swap providers without changing your workflow. Browse and test them side-by-side in the Models & Playground.
• Multi-Step Agent Pipelines — Chain 24 step types — prompt calls, gates, transforms, retrieval, memory, webhooks, sub-agent calls, and more — into visual workflows with conditional branching, fan-out/fan-in, and self-improving retry loops.
• RAG Infrastructure — Connect RSS feeds, websites, custom APIs, and file uploads as content sources. Auto-chunking, embedding, reranking, and semantic retrieval built in. Chat directly with your knowledge bases or use them as context in agent pipelines.
• Memory Banks — Give agents persistent memory that spans conversations and sessions. Conversation and general bank types with automatic compaction and retention policies.
• Triggers & Scheduling — Run agents on user input, content changes, or cron schedules. Stream responses token-by-token via SSE.
• Observability & Evaluation — Trace every LLM call with step-level input, output, latency, cost, and quality scores. Evaluate outputs against quality criteria with eval-and-retry and sampled monitoring modes.
• Safety & Governance — Three layers of protection: prompt injection detection, policy-based content screening with flag-or-block verdicts, and quality evaluations. See Safety & Quality below.
• Cost & Operations — Per-model, per-agent credit tracking with budget alerts, model lifecycle management with auto-rollback, team collaboration, and resource transfers and exports.

What Can You Build?

• Knowledge Base Assistants — Build AI assistants that answer questions using your indexed content with built-in RAG, memory, and streaming. Customer support bots, internal documentation assistants, and research tools.

• Content Monitoring & Processing — Track RSS feeds and websites for new content. Automatically summarize, classify, extract entities, or generate insights when new items arrive. Trigger agents on content changes in knowledge bases.

• Multi-Step LLM Pipelines — Chain retrieval, generation, evaluation, governance checks, and actions into reliable workflows. Use gates for conditional branching, combinators for fan-out/fan-in, and retries for self-improving loops.

• Personalised Chatbots — Combine conversation memory banks with general preference memory to build agents that remember user context across sessions and improve over time.

• Scheduled Automation — Deploy agents that run on cron schedules — daily digests, weekly reports, hourly monitoring. Template triggers pre-fill inputs for recurring tasks with no user interaction required.

• Data Extraction & Integration — Fetch web pages, call external APIs via webhooks, search the web, write results to S3, send email reports, and chain sub-agents for complex orchestration.

Integration Options

Seclai is designed to fit into your existing stack:

• REST API — Full CRUD for all resources. Trigger agents, manage sources, query knowledge bases, and more.
• MCP Server — Model Context Protocol integration for AI coding tools like Claude, Cursor, and VS Code Copilot. Manage your entire Seclai account from your IDE.
• SDKs — Official clients for Python, JavaScript, Go, and C#.
• CLI — Command-line interface for scripting and CI/CD integration. Includes built-in skills — lightweight, pre-packaged workflows that run common tasks (e.g. "create a monitoring agent for this RSS feed") in a single command, without needing a full MCP setup.
• API Keys — Dual authentication: API keys or OAuth tokens. Scoped access for different integrations.

Safety & Quality

Seclai provides three layers of protection that work together:

1. Prompt Scanner — An always-on ML classifier that detects prompt injection and jailbreaking attacks at every ingress point and on outputs from external sources. Zero configuration, zero LLM cost, sub-second latency.
2. Governance — LLM-based policy screening for safety, PII, bias, legal, and brand compliance. Configurable per-agent, per-step, and per-source with flag-or-block verdicts and a review queue.
3. Agent Evaluations — Score outputs against quality criteria to catch regressions. Eval-and-retry mode automatically re-runs low-scoring steps; sampled monitoring mode flags quality drift over time.

Why Seclai?

Building custom AI automation is hard. Every real-world LLM feature involves multiple model calls, each requiring retries, evaluation, governance, and observability. Teams spend months rebuilding this infrastructure instead of shipping product.

We built Seclai so you don't have to rebuild that infrastructure.

Skip Months of Infrastructure Work — Building production LLM infrastructure from scratch requires 3–6 months of development time. With Seclai, all production pillars — model portability, retries, eval, observability, RAG, memory, streaming, governance, prompt safety, and cost management — are built in from day one.

Purpose-Built for LLM Production — Unlike generic automation tools, Seclai is designed specifically for the challenges of running LLMs in production. Native support for multi-model pipelines, structured output validation, systematic quality evaluation, and three-layer safety.

No Vendor Lock-In — Use any of 90+ AI models. Switch between Claude, GPT, Gemini, Llama, and others with a dropdown. Your workflows, your choice. Export all your data at any time in standard formats. Transfer resources between accounts.

Complete Observability — Trace every LLM call across multi-step pipelines. See input, output, latency, token usage, cost, and quality scores at each step. Inline pseudo-steps show prompt scans, governance evaluations, and agent evaluations directly in the trace.

Focus on Your Product — We handle the infrastructure complexity. You focus on building features that drive value for your users.

Getting Started

Ready to build your first AI workflow? Check out our Getting Started guide to deploy your first agent in minutes.