Automate work end-to-end with AI in the loop.
Trigger-based automations that combine deterministic logic with AI reasoning — for the messy work that used to require a human. Ship customer-facing AI features on a governed runtime with retrieval, tool use, evaluations and human oversight built in — so your team moves from prototype to production in days instead of quarters.
Overview
A single home for ai automation.
AI Automation is Flyps' production-grade runtime for shipping AI that customers rely on. It replaces a fragile stack of prompt tools, orchestrators, vector stores and observability tools with one system where teams design, evaluate and roll out AI features on the same discipline as software.
Under the hood, AI Automation grounds every response in your own data, calls typed tools across 300+ connectors, streams tokens with backpressure, and captures every step in an auditable trace. Engineering, product and compliance work from the same source of truth — with policy, safety and cost caps enforced by the platform rather than tribal knowledge.
50%
fewer tools
4×
faster time to value
99.99%
uptime
SOC
2 · HIPAA
Problems it solves
What breaks without AI Automation.
The pain points core ai teams tell us they hit before adopting AI Automation — and how the platform removes each one.
Prototypes stuck in demo mode
AI proofs of concept impress on a Friday demo but never reach production because ai automation needs evaluations, guardrails and observability the demo skipped.
Fragmented AI stack
Teams stitch together prompt tools, vector stores, orchestrators and observability tools that don't share identity, data or policy.
Hallucinations at customer touchpoints
Ungrounded models make up answers, breaking trust with customers and forcing support to clean up the mess.
No audit trail
Regulated teams can't ship AI without step-by-step traces, model versions and human approvals recorded for every action.
Runaway inference cost
Without semantic caching, routing and quotas, per-call inference costs quietly break the business case.
Vendor lock-in
One-model architectures make it impossible to switch providers when pricing, policy or capability shifts.
Capabilities
Everything you need to ship.
Event triggers
Webhooks, schedules, forms and change-data-capture from any source.
AI branches
Let a model decide the next step when the rules aren't obvious.
Observability
Per-run traces, replays, and diffs across environments.
Technology
Built on production-grade primitives.
AI Automation is engineered for teams that need to run AI in production — with the reliability, observability and portability that regulated, high-volume workloads demand.
- Retrieval-augmented generation (RAG) with hybrid vector and keyword search
- Typed function calling across 300+ connectors with JSON Schema validation
- Model routing across OpenAI, Anthropic, Google and self-hosted models
- Semantic caching, per-workspace quotas and cost caps
- Event-driven runtime with SSE and websocket streaming
- OpenTelemetry-native traces, evals and replay
- SOC 2, ISO 27001, HIPAA-eligible; bring-your-own-key and VPC options
Why teams love AI Automation
Outcomes, not outputs.
The measurable benefits core ai leaders report after standardizing on AI Automation.
Ship AI features in days, not quarters
AI Automation collapses the timeline from prototype to production with reusable retrievers, tools, prompts and evaluations your whole team can compose.
Grounded, cited responses
Every answer is tied to source documents, tickets or CRM records — so customers and auditors can trust the output.
Human-in-the-loop by default
Route sensitive actions to Slack, email or an approval queue before execution, with full context attached.
Portable across model providers
Route to OpenAI, Anthropic, Google or self-hosted models per workspace, per environment or per request.
Predictable unit economics
Semantic caching, quotas and cost caps keep per-run inference spend inside the business case.
Governed by policy, not exceptions
Central policy engine enforces PII, brand and compliance rules across every prompt and tool call.
Enterprise-grade observability
Full-fidelity traces, replays and diffs — exported to your existing APM through OpenTelemetry.
How we deliver
From setup to signal in weeks.
Every AI Automation rollout follows the same repeatable process — designed with your engineering, product and compliance teams in the loop from day one.
1
Discovery
We map the workflows where AI Automation will run — sources of truth, tools it must call, and the humans in the loop.
2
Design & grounding
Auto-suggested schemas, retrievers and prompts you can review, edit and version in the same repo.
3
Build & evaluate
Compose typed workflows, wire up evaluations and run offline suites before promoting anything to production.
4
Rollout & observe
Staged rollouts, canary traffic and full-fidelity traces exported to your existing observability stack.
Expected results
Business impact you can measure.
Typical outcomes teams report within the first two quarters on AI Automation — instrumented against the metrics your leadership already tracks.
4×
faster time from prototype to production
60%
reduction in customer response times
99.99%
uptime across every production workspace
70%
less manual toil on repetitive AI work
Use cases
Where teams use AI Automation.
Renewals & churn saves
Provisioning
Content moderation
Data enrichment
Integrations
Plays well with your stack.
First-party connectors — plus 300+ more across the Flyps platform.
Recommended solutions
See AI Automation in your context.
FAQ
Answers about AI Automation.
The questions engineering, product and compliance leaders ask most often before rolling AI Automation out — with detailed answers on implementation, technology and security.