Conversational AI for every surface.
Deploy branded chat experiences across web, mobile and messaging channels — grounded in your company's knowledge and governed by a single policy layer. 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 chat.
AI Chat 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 Chat 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 Chat.
The pain points core ai teams tell us they hit before adopting AI Chat — 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 chat 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.
Grounded answers
Retrieval over your docs, tickets and CRM with citations on every response.
Multi-channel
Ship one assistant to web, iOS, Android, WhatsApp and Slack from a single build.
Handoff to humans
Seamless escalation to live agents with full transcript and confidence scores.
Technology
Built on production-grade primitives.
AI Chat 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 Chat
Outcomes, not outputs.
The measurable benefits core ai leaders report after standardizing on AI Chat.
Ship AI features in days, not quarters
AI Chat 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 Chat 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 Chat 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 Chat — 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 Chat.
Support deflection
In-product assistance
Sales chat
Community Q&A
Integrations
Plays well with your stack.
First-party connectors — plus 300+ more across the Flyps platform.
Recommended solutions
See AI Chat in your context.
By Team
Sales
Prospect, close and forecast with an AI copilot on every rep's shoulder.
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Customer Support
Deflect, resolve and delight — with humans in the loop where it counts.
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HR
Modern people ops: onboarding, help desk, and employee experience.
ExploreBy Business Size
Startups
Punch above your weight with an AI-native stack.
ExploreFAQ
Answers about AI Chat.
The questions engineering, product and compliance leaders ask most often before rolling AI Chat out — with detailed answers on implementation, technology and security.