→ Policy-enforced completions → Immutable audit trail → JIT credential issuance → 46 registered models → VPC-native deployment → PII redaction at rest → mTLS service mesh → SIEM-exportable logs → Policy-enforced completions → Immutable audit trail → JIT credential issuance → 46 registered models → VPC-native deployment → PII redaction at rest → mTLS service mesh → SIEM-exportable logs
Beta — July 1, 2026

Frontier models.
Full control.
Zero exposure.

PromptliAI is the enterprise grade AI operating system that sits between your organization and every frontier model — enforcing policy, routing intelligently, and logging everything. Chat, agents, code assist, audit, and governance live in one tab.
Tool sprawl ends here.

526 spots remaining Beta waitlist
250+Customer interviews
46Registered models
5-layerAdmissibility gate
One tabAll AI tools, self-contained
Who it's for
For Developers

Shadow AI
ends here.

Personal accounts mean personal liability. PromptliAI gives your engineers a governed, repo-aware AI endpoint — policy-enforced, fully auditable, zero shadow usage.

→
  • Policy-enforced completions — every model call routed through your org's guardrails, not around them
  • Repo-aware code assist — connected to your branch, your ticket, your context — not a generic model snapshot
  • Full audit trail per request — every suggestion logged, queryable, and exportable by your security team
  • Zero shadow usage — one governed endpoint replaces every personal account across your engineering team
For Enterprise

You decide what runs.
You can turn it all off.

Role-based access, configurable learning modes, SIEM-exportable audit trails, and compliance dashboards built for the CISO buying cycle.

→
  • Five-layer admissibility gate — runs before any model is touched; fail any invariant, execution stops
  • Role-based access control — Admin / Operator / Developer / Auditor with cascading least-privilege
  • Configurable learning modes — Off / Suggest / Teach / Admin; lockable at org or team level
  • Hash-chained audit trail — tamper-evident, SIEM-exportable, compliance-ready from day one
See Enterprise Details →
Platform capabilities
Five buckets. One control plane.

Every enterprise grade AI interaction flows through a governed stack — from memory retrieval to action execution.

01 / 05
Storage & Memory
Persistent, retrieval-ready context across every session and tenant.
Expand
  • Session storage — DynamoDB-backed history with ownership metadata per session
  • Cross-session memory — MemMachine episodic recall, cosine-similarity filtered at query time
  • RAG pipeline — Bedrock Titan embeddings, Redis HNSW retrieval, 1,200-token budget
  • Artifact store — S3-backed with signed URLs and tenant-scoped lifecycle management
  • Cache layer — Intent-tagged Redis TTL per class; cache savings tracked as a cost metric
02 / 05
Chat & Routing
Multi-model conversations with intelligence at the prompt layer.
Expand
  • Prompt normalization — locale detection, synonym expansion, semantic normalization before any model call
  • Role inference — infers stakeholder role (CIO, CISO, developer, DevOps) to shape response framing
  • Intent classification — routes to code, security, accounting, or deployment strategy with confidence scoring
  • Model Council — panel from 46 models assembled on intent × criticality × accuracy; runs candidates in parallel
  • Memory injection — MemMachine recall enriches every prompt automatically
03 / 05
Agents
Autonomous workflows with scoped, audited access to external systems.
Expand
  • Agent runner — sequential step execution with incremental status (queued → running → success/failed)
  • JIT credentials — scoped OAuth tokens per step; expire immediately after use
  • System connectors — Salesforce, GitLab, Jira, and more; refresh tokens KMS-encrypted
  • Lifecycle management — draft → active → paused → archived; success rate tracked per agent
  • Prebuilt templates — escalate tickets, sync GitLab MRs, generate status reports, alert on SLA breaches
04 / 05
Governance & Security
Verifiable safety controls before, during, and after every LLM call.
Expand
  • Admissibility gate (A1–A5) — identity binding, session ownership, boundary constraints, prompt drift detection, malicious prompt correction
  • Boundary engine — per-user and per-tenant rate limits, accuracy floors, criticality tiers (medium / high / critical)
  • Tamper-evident audit trail — hash-chained ledger with cryptographic integrity verification; SNS alert on chain break
  • PII redaction — structured audit logs hash-mask user IDs and payloads before writing
  • mTLS — mutual TLS enforced across all service-to-service calls
05 / 05
Workflow Learning
The system continuously learns what works and routes accordingly.
Expand
  • Variant grading — grader Lambda scores and ranks candidate responses; winner informs future routing
  • Calibration engine — IRR tracking detects model drift from expected alignment
  • Cost-aware execution — pre-request budget check, per-job cost deduction, CloudWatch metrics by tenant/intent/model
  • Learning mode controls — Off / Suggest / Teach / Admin — configurable per org and team
  • StepFunctions orchestration — version, merge, and manage multi-step pipelines via API
Request lifecycle
What happens to every prompt.
01
Memory retrieval
Relevant past context fetched from MemMachine and injected
02
Admissibility gate
Five invariants evaluated before any model is touched
03
Intent & routing
Classified, normalized, council assembled, parallel execution
04
Grading & calibration
Responses scored, ranked, logged, routing updated
05
Action execution
JIT credentials issued, steps run, full audit written
Why now
The market already knows the problem.
78%
of knowledge workers bring their own AI tools to work without employer knowledge — every prompt ungoverned, untracked, and off-policy.
Microsoft & LinkedIn Work Trend Index, 2024 →
82%
of organizations lack governance councils with the authority to manage what AI agents are actually doing across their enterprise stack.
PromptFluent Research, March 2026 →
$0
in audit trail, policy enforcement, or model routing governance ships with any frontier model by default. Compliance is the customer's problem — until now.
TechRadar Pro, April 2026 →

Beta opens July 1st.
Governed AI from day one.

No credit card  ·  VPC-native  ·  Audit-ready