Introduction
Seclai offers production LLM infrastructure.
What is Seclai?
Seclai is production LLM infrastructure for teams building AI applications. It brings model access, workflow orchestration, output validation, evaluation, observability, RAG, memory, governance, and cost management into one runtime. Instead of assembling many separate tools around each LLM call, teams can build and operate AI agents in a more unified environment.
The platform is designed for teams that need to answer for what they ship. It supports visual building and code-based workflows, and it lets users switch between 200+ models from major providers. Seclai is suitable for AI engineers, platform teams, product teams, and organizations that need stronger control over LLM observability, model portability, AI governance, and LLM cost management. It matters because production AI systems need more than a model API; they need validation, traces, evals, safety screening, and deployment controls that work together.
Key Features of Seclai
Model Portability Across 200+ Models
Switch between 200+ models from providers such as Anthropic, OpenAI, Google, and Meta without rewriting workflows. Keep prompts, data, and agent steps in place while changing the model at a specific step.
Step-Level Observability
Track every step in every run with status, latency, credits, and governance signals. Replay traces from the dashboard to debug multi-step AI agents and improve production LLM infrastructure reliability.
Output Validation and Retries
Validate structured outputs and automatically retry bad responses with fallback models. This helps teams maintain response quality without building custom retry logic for every workflow.
Evaluation Sets for Tests and Production
Define eval sets once and run them locally, in CI, and in production with the same semantics. Use LLM evaluation to catch quality regressions before and after deployment.
RAG and Knowledge Bases
Connect websites, file uploads, RSS feeds, and REST APIs, then index content into a vector database for retrieval. Seclai handles chunking, embedding, and syncing as part of its RAG infrastructure.
Persistent Memory Banks
Add conversation memory and general memory banks so AI agents remember context across sessions. This supports more consistent user experiences in long-running assistants and support workflows.
AI Governance and Prompt Injection Protection
Write natural-language policies for content safety, PII protection, and forbidden topics. A fast ML-based prompt scanner screens inputs, source content, and webhook responses to block prompt injection before it reaches an LLM.
Cost Management and Budget Controls
Track cost by model, agent, and step with credit-based usage reporting. Set budget controls and alerts to support LLM cost management across teams and applications.
Visual Builder and Code Support
Build agents visually or work in code, depending on the team’s workflow. The platform includes SDKs for Python, JavaScript, Go, and C# for application integration.
HTTP Endpoints for Deployment
Deploy each agent with an HTTP endpoint that returns output, trace, cost, and governance verdict in one call. This makes production LLM infrastructure easier to connect to existing applications.
Use Cases for Seclai
Customer Support Triage
Build support agents that retrieve knowledge base content, classify requests, and route conversations. Use LLM observability and evals to monitor quality and catch regressions.
Compliance and Security Reviews
Screen inputs and outputs against governance policies for PII, forbidden topics, and content safety. Step-level traces help teams audit what happened in each run.
Internal Knowledge Assistants
Connect documents, websites, and APIs to create RAG-powered assistants with persistent memory. Teams can switch models as needs change without rebuilding the entire workflow.
Lead Enrichment and Research Agents
Orchestrate multi-step agents that gather, summarize, and validate information from connected sources. Cost tracking and output validation help keep quality and spend under control.
Multi-Model AI Agents
Use model portability to choose different models for different steps, such as fast classification and stronger reasoning. This supports flexible AI agents while keeping governance and evaluation consistent.
How to Use Seclai
- Add a content source by entering a URL or uploading a file. Seclai detects the source type and sets up syncing.
- Create a knowledge base so content is chunked, embedded, and indexed for retrieval.
- Build an agent with the visual builder, then wire in knowledge bases, memory, and workflow steps.
- Configure governance policies, output validation, retries, and evaluation sets for the agent.
- Deploy the agent through its HTTP endpoint or SDK and monitor runs with production LLM infrastructure traces, cost data, and governance verdicts.
Target Audience for Seclai
- AI and machine learning engineers building production LLM applications
- Platform teams that need LLM observability, governance, and cost controls
- Product teams creating AI agents, assistants, and RAG workflows
- Security and compliance teams reviewing AI input and output risks
- Startups and enterprises evaluating production LLM infrastructure for scale
Is Seclai Free?
Seclai offers a free trial with full feature access and credits to get started. The trial is limited to one per person, and most accounts that sign up with a social login can start without a credit card. Paid plan details are not listed in the provided content, so users should check the official pricing page for current information.
| Plan | Price | Features |
|---|---|---|
| Free Trial | $0 during trial | Full feature access, starter credits, one trial per person, most social logins can start without a credit card |
| Paid Plans | Not listed in provided content | Visit the official Seclai pricing page for current details |
Seclai's Pros and Cons
| Aspect | Pros | Cons |
|---|---|---|
| Pricing | Free trial with full feature access and credits | Paid plan details are not listed in the provided content |
| Features | Covers observability, evals, RAG, governance, memory, and cost management | Broad platform may require learning new concepts |
| Model support | 200+ models with model portability | Advanced provider-native features may need provider-specific syntax |
| Deployment | HTTP endpoints and SDKs for common languages | Production setup still requires engineering planning |
| Governance | Prompt injection scanning and natural-language policies | Governance settings need thoughtful configuration |
Frequently Asked Questions about Seclai
What is Seclai?
Seclai is production LLM infrastructure for building, deploying, and monitoring AI applications. It combines model access, workflows, evals, observability, RAG, memory, governance, and cost tracking.
How does model portability work in Seclai?
Users can select from 200+ models and switch models at an agent step. Workflows, prompts, and data can stay the same, while advanced users can still use provider-native syntax when needed.
Does Seclai support RAG?
Yes. Seclai can connect RSS feeds, websites, file uploads, and REST APIs. Content is automatically chunked, embedded, and kept in sync for retrieval through knowledge bases.
How does Seclai handle prompt injection and governance?
Seclai uses an ML-based prompt scanner to screen inputs, source content, and webhook responses before they reach an LLM. Governance policies then screen outputs for safety, privacy, and compliance rules.
Is there a free trial for Seclai?
Yes. New accounts receive a free trial with full feature access and credits. The trial is limited to one per person, and most social logins can start without a credit card.
Can Seclai agents be deployed through an API?
Yes. Every agent has an HTTP endpoint, and Seclai provides SDKs for Python, JavaScript, Go, and C#. A run can return output, trace, cost, and governance verdict in the same call.
Seclai Tags
production LLM infrastructure, LLM infrastructure, LLM observability, LLM evaluation, model portability, RAG infrastructure, AI governance, LLM cost management, AI agents, prompt injection protection, LLM orchestration, Seclai
