Our Services
Vigilant Voices is the product offshoot of Model Signal, built as a complete Generative AI orchestration layer. From intelligent multi-model routing and private infrastructure deployment to connecting your own data sources, every service is designed to make your AI stack smarter, cheaper, and entirely under your control.
Dynamic orchestration across GPT-4o, Claude, Gemini, Mistral, LLaMA, and more using intent, risk, latency, and policy signals.
Get StartedOne OpenAI-compatible endpoint for all approved models with enterprise policy controls and zero-rewrite app migration.
Get StartedLog, replay, and policy-map every AI response with evidence trails for HIPAA, GDPR, SOC 2, and internal governance controls.
Get StartedGranular per-model spend intelligence tied to quality and outcome metrics, with optimization recommendations before overrun happens.
Get StartedDefine ordered fallback sequences so if your primary model is down or over-budget, traffic re-routes automatically without user impact.
Get StartedPython, Node.js, and Go SDKs with first-class support for streaming, function calling, RAG pipelines, and agent frameworks.
Get StartedEvery model call is instrumented with latency, token count, cost, and quality score. Exportable to Datadog, Grafana, and OpenTelemetry.
Get StartedOn-prem, VPC, or managed cloud — we support all deployment topologies with dedicated support, SLAs, and custom integrations.
Get StartedThese concepts are designed to give Vigilant Voices unique product differentiation beyond typical model access and prompt tooling.
Re-simulate historical AI decisions under new policy rules to predict risk before production rollout.
View service Try in demoConvert legal, compliance, and security policy docs into executable guardrails automatically.
View service Try in demoRun multi-model consensus checks and publish only responses that meet confidence and evidence thresholds.
View service Try in demoWhen policy conflicts occur, route exceptions to the right approver with machine-generated risk context.
View service Try in demoChoose focused implementation tracks for retrieval quality, reliability engineering, policy guardrails, and private model adaptation.
Optimize chunking, indexing, and reranking to maximize grounded answer quality.
View ServiceEngineer resilient AI runtime operations with SLOs, failovers, and runbooks.
View ServiceTranslate governance rules into enforceable, auditable runtime controls.
View ServiceRun secure model adaptation pipelines with governance and release gates.
View ServiceModel Signal is a Generative AI-first company built for organizations that can't afford to hand their data to a public cloud and hope for the best. Vigilant Voices, its product offshoot, deploys entirely within your own infrastructure — whether that's bare-metal servers you manage, an IaaS environment like AWS, Azure, or GCP, or a private VPC tenant we provision and manage on your behalf.
Nothing leaves your perimeter. Your prompts, your responses, and your data stay inside your environment. We bring the intelligence layer to you — not the other way around.
Run the full Vigilant Voices stack on your own servers or VMs. We provide container images, Helm charts, and Terraform modules — you control the hardware, the network, and the keys.
Don't want to manage the ops? We provision and maintain a dedicated Vigilant Voices environment inside a private VPC in your cloud account. Fully isolated — no shared resources with other customers, ever.
All inter-service communication is mTLS-encrypted. Role-based access control, audit logging, and secrets management are configured out of the box — no bolted-on security after the fact.
Out-of-the-box LLMs know everything about the world — and nothing about your company. Vigilant Voices bridges that gap by letting you connect your own internal data sources directly to the AI layer, so every response is grounded in your knowledge, not generic training data.
Connect databases, document stores, wikis, CRMs, SharePoint, or any structured or unstructured data source to Vigilant Voices. At inference time, the platform retrieves the most relevant content from your data and injects it as context into the model's prompt — so the AI answers with your facts, not hallucinated ones. Your source data never gets baked into the model itself; it stays in your systems and is queried live.
For teams that need the model to internalize your terminology, tone, or decision patterns — not just reference your documents at runtime — Vigilant Voices supports supervised fine-tuning workflows. You provide labeled examples from your domain; we manage the training pipeline against compatible open-weight models running inside your infrastructure. The resulting model is yours and stays in your environment.
Vigilant Voices includes a built-in vector store that indexes your documents as high-dimensional embeddings. When a user submits a query, the platform finds the semantically closest chunks of your content — not just keyword matches — and surfaces them to the model. Works with PDF, HTML, Markdown, SQL, and API-based data sources out of the box.
Your data isn't static — and neither is your AI's knowledge of it. Vigilant Voices watches your connected data sources for changes and automatically re-indexes updated content so the model always has access to your latest information. Configure sync schedules or trigger re-indexing on commit, publish, or any webhook event.
From your source systems to a grounded AI response — all inside your infrastructure
Whether you need a quick SaaS evaluation or a full private deployment scoped to your infrastructure, our team will walk you through every step.
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