KEY FEATURES / DEPLOYMENT

Run AI where your business needs it

Deploy AHEAD in public cloud, private cloud, or on‑prem.
Push to industrial edge or operate fully air‑gapped.

AI cannot be one‑size‑fits‑all. Some teams need low latency at the edge. Others need data sovereignty or no‑internet environments. Many want cloud scale, but with customer‑managed keys and private networking.

AHEAD meets you where you are: cloud, hybrid, on‑prem, edge, or air‑gapped. So your people get the same explainable workflows and governed deliverables everywhere.

Deployment Types

Choose what fits your constraints

Cloud

Managed

Fit: Fastest path to value; broad integrations; elastic scale.
Posture: Private networking options; IP allow-lists; VPC/VNet peering (where supported).
Keys & data: Customer-managed keys optional; data residency controls.
Note: Ideal for multi-team pilots and rapid iteration.

Hybrid

Split Planes

Fit: Keep sensitive data/actions local while using managed control plane.
Pattern: Control plane in cloud; data plane on-prem with local connectors and caches.
Benefit: Reduces egress; maintains policy-aware retrieval; central governance.
Note: Common for regulated domains and global orgs.

On-Prem

Private

Fit: Sovereignty, isolation, and integration with existing IT/security controls.
Runtime: Containers/Kubernetes (including private platforms).
Keys & storage: Customer-managed KMS/HSM; local secrets vaults; artifact registries.
Note: Same product capabilities—no re-architecture required.

Edge

Line

Fit: Low-latency inference; intermittent connectivity; site-level privacy.
Pattern: Local inference, offline-capable workflows; delayed sync for deliverables.
Hardware: CPU/GPU profiles; lightweight runtime; sensor/MES connectors.
Note: Ideal for quality inspection, predictive maintenance, safety.

Air-Gapped

No Internet

Fit: High-security environments with strict isolation.
Pattern: Local registries; offline model and package updates; local logs; physical media sync if allowed.
Governance: Full explainability and deliverables still apply—without external calls.

Security & Data Control Governance everywhere

Identity & Access: SSO/SAML/OIDC; RBAC with least-privilege scopes; ABAC for data and deliverables.
Keys & Secrets: Customer-managed keys (KMS/HSM); per-env secrets vault; rotation schedules.
Network Isolation: Private networking, IP allow-lists, mTLS; no egress where required.
Data Residency: Region pinning; content tags for residency and purpose limitation.
Privacy & Safety: PII detection/redaction at ingest; content checks pre-send.
Auditability: Signed/immutable logs (optional), lineage on data and decisions; exportable evidence.

Outcome: Same governance posture in cloud or on the shop floor.

Model Hosting & Routing Your models, your way

Provider mix: Use cloud APIs or self-host OSS models (e.g., Llama/Mistral class) under your control.
Routing: Cost/latency/quality routing per task; budget guardrails and alerts.
Private weights: Host sensitive models on-prem/edge; cache prompts and embeddings locally.
Vector options: Use your preferred vector store or built-in options with policy tags.
Observability: Per-model dashboards (latency, cost, acceptance, failure modes).
Fallbacks: Graceful degradation to local models if upstream providers are unavailable.

Outcome: Performance, sovereignty, and cost control—without vendor lock-in.

Performance, Scale & HA/DR From pilot to org-wide

Autoscaling: Horizontal scale for agents, retrieval, and inference pools.
Caching: Context and embedding caches tuned to latency/throughput goals.
Throughput: Queues with back-pressure; idempotent operations.
High Availability: Multi-zone clustering; rolling upgrades; health probes.
Disaster Recovery: Snapshots; offsite/second-site options; RPO/RTO targets defined per environment.
Capacity Profiles: CPU-only, mixed, GPU-optimized bundles.

Outcome: Consistent performance from pilot to organization-wide rollout.

Observability & AI FinOps Measured outcomes

Traces everywhere: Data → retrieval → policy → model/tool → agent → decision.
Dashboards: Latency, error rates, cost per task, acceptance/rework, evidence coverage.
Budgets & Alerts: Hard and soft limits by team/use case; anomaly detection.
Log handling: Local log stores; delayed or zero egress (air-gapped) with export jobs.
Change tracking: Model/version changes; configuration diffs; rollout/rollback history.

Outcome: Operate with confidence and measured outcomes, not guesswork.