Everything you need to build, run and scale enterprise AI, in one platform.
The intelligence layer your enterprise runs on.
Grounding, guardrails, evaluation, access, cost and observability are the difference between a prototype and production AI. 4S CoreAstrum provides all of it as one foundation, so your teams ship trusted AI instead of rebuilding the plumbing for every project.
Every stage builds on the one before it, so an assistant is grounded, guarded and observable by default, not as an afterthought.
Connect documents, databases and APIs; chunk and embed into vector search.
RAG fetches the most relevant context for each question.
Agents and copilots plan, call tools and draft the answer.
Guardrails check grounding, safety and policy before it ships.
Trace, evaluate and monitor every response.
Feedback and evaluation tune quality over time.
Six production-hardened AI modules, configurable per deployment and shared by every assistant, agent and copilot you build.
Answers grounded in your own data
Documents are parsed, chunked and embedded; hybrid retrieval with reciprocal rank fusion and LLM re-ranking finds the right context; every answer cites its sources. An ontology and knowledge graph capture your domain's entities and relationships.
Agents that plan, act and collaborate
AI agents decompose tasks, call tools and take action, alone or as a multi-agent team, with memory, their own knowledge base and a full execution history. Human approval steps keep people in control of the decisions that matter.
One gateway to every model
An AI gateway routes across providers and models from one registry, with managed credentials and a versioned prompt library. Guardrails run on every call, and resilience is built in: circuit breakers, retry with backoff and rate-limit handling.
Know how good every answer is
Eval datasets and suites, LLM-as-judge with judge drift detection and human evaluation score quality before and after release. Regression gating blocks a worse version, and OpenTelemetry traces show every step of every agent run.
From scans and PDFs to validated data
OCR and layout intake read scanned documents and PDFs; schema-driven extraction pulls out fields with a confidence score, validation rules check them, and batch processing sends clean data straight to your master records.
Start past the plumbing
Reusable templates and GenAI copilots for common jobs: text analysis, generation and summarization, and conversions between text, images and video, packaged with industry use cases and best practices for a faster ROI.
See exactly how each answer was produced: prompt and session, knowledge base, pipeline, agent and every model call, with tokens, latency and cost at each step, across models run with different parameters.
Full observability, not guesswork. Compare models and parameters side by side, watch tokens, latency and cost per call, and replay any session end to end.
Security, tenancy, error handling and alerting are part of the platform, not a project. Every AI product on 4S CoreAstrum inherits them.
Enterprise Application Core
Roles and permissions per workspace, single sign-on, organization hierarchies and a full audit trail. Access is always decided centrally, so a misconfigured tenant can never lock a valid user out.
One platform, every tenant kept apart
Each tenant's data, vectors, prompts, keys and audit are separated by default, in a shared database or one per tenant. Connection pooling, caching and a job scheduler keep it fast as usage grows.
Problems found before users report them
Errors are logged, classified and routed. An error-alert engine and digest alerts notify the right people, AI provider errors are classified automatically, and AI health is monitored continuously.
The right message, on the right channel
One engine sends templated messages by email, WhatsApp, SMS and in-app, on triggers or schedules, with approval actions, delivery tracking and a full history.
A standard delivery lifecycle for every customer project, so each new AI product starts from proven templates and ships through the same gates.
Standard templates, project and backend structure, environment configuration.
Reusable accelerators and best practices for each domain solution.
Eval suites and regression gating before anything ships.
CI/CD pipelines, release versioning, deployment checklists, Docker.
Health checks, monitoring, application logging, automated rollbacks.
Choose where AI runs, which models it uses, and how it's governed, per deployment, by configuration.
Run the whole pipeline beside your data; nothing has to leave the building.
Bring your own LLM and embeddings; swap or route models without rewrites.
Retrieval and grounding run where your data lives, not in someone else's cloud.
Prompts, retrievals, tools and responses are logged for review and evaluation.
Access is always checked centrally, so a misconfigured tenant never blocks a valid user.
A consistent brand layer and packaging built for distribution, including Google Marketplace.
Teams build a wide range of AI on the same foundation, each grounded in your data, guarded, and observable from day one.
Grounded question-answering over your own documents and data, with citations back to the source.
Assistants that plan, call tools and take action across your systems, with a human in the loop.
Docs-to-data processing that turns unstructured files into clean, structured data.
User chat with saved sessions, history and memory across conversations.
Multi-step automations that reason across your tools and systems.
AI that watches streams and sources, and surfaces what matters in real time.
Built for production AI
Bring retrieval, models, guardrails, observability and access into one AI foundation, and give every assistant a head start.