A Profile for Every
AI Application
Wag-Tail is an AI Application–Centric gateway. Instead of forcing one global policy on every use case, you compose a dedicated profile for each AI application from three building blocks — security rules, MCP components, and token-optimization rules — then connect it to any LLM. Every application gets exactly the controls, tools, and cost profile it needs.
Why One Policy for All Falls Short
Enterprises don't run "AI" — they run many distinct AI applications. A customer-support assistant, a developer copilot, a finance analytics bot, and a public-facing chatbot each have completely different security needs, data access, tools, and cost profiles. Applying a single global gateway policy to all of them means every application is either over-restricted, under-protected, or paying for capabilities it never uses.
1. Different Security Needs
Each application handles different data and threats:
- A finance bot must mask PII and financial records
- A public chatbot needs strict jailbreak and output moderation
- A dev copilot must redact secrets and source code
- One blanket rule set fits none of them well
2. Different Tools & Context
Applications need different MCP tools and data sources:
- Support needs the knowledge base and ticketing
- Analytics needs database and spreadsheet access
- A public bot should reach almost nothing
- Granting every app the same tools is a risk
3. Different Cost Profiles
Token economics vary widely by workload:
- Repetitive support queries benefit from caching
- Long prompts benefit from compression
- Some apps should route to the cheapest model
- One optimization setting leaves savings on the table
The shift is from gateway-centric to application-centric. Instead of one policy in front of everything, Wag-Tail lets you package the exact combination of controls each AI application needs — and change it independently as that application evolves.
How It Works: One Profile per Application
Each AI application connects to the LLM through its own profile — a package that combines security rules, MCP components, and token-optimization rules. Wag-Tail applies that profile to every request from the application, then routes it to the provider you choose.
Your AI Applications
Support bot, dev copilot, analytics, chatbot — each with its own profile
Wag-Tail Application Profile
- PII masking, injection & jailbreak defense
- Content filtering & policy per application
- Only the MCP tools & data this app may use
- Knowledge base, database, ticketing, search
- Semantic caching & prompt compression
- Smart model routing tuned per application
- Per-application usage, spend & audit trail
- Consistent oversight across every profile
Any LLM Provider
OpenAI, Azure OpenAI, Anthropic, Google & more
The Three Building Blocks
Every application profile is composed from the same three building blocks. Mix and match them to fit each use case — turn rules on or off, swap MCP tools, and dial cost controls up or down, independently per application.
1. Security Rules
Decide exactly how each application is protected. Compose the security posture per app instead of applying one global rule set.
- PII detection & masking — emails, phone numbers, IDs, customer and financial data
- Threat protection — prompt injection, jailbreak attempts, unsafe outputs
- Content & policy filtering — enforce what each application may send and receive
- Enterprise integration — works alongside F5 AI Gateway and existing controls
A public chatbot and an internal finance assistant can run wildly different rules — from the same platform.
2. MCP Components
Grant each application only the tools and context it should have. Attach the Model Context Protocol (MCP) components an app is allowed to use — and nothing more.
- Scoped tool access — knowledge base, databases, ticketing, web search, internal APIs
- Least-privilege by design — an app can only reach the MCP servers in its profile
- Reusable components — define an MCP tool once, attach it to any profile
- Governed context — every tool call runs under the same audit trail
Give the support assistant its knowledge base and ticketing, while the public bot gets almost nothing.
3. Token-Optimization Rules
Tune cost and latency for each workload. Apply the optimization strategy that matches how the application actually uses tokens.
- Semantic caching — repeated or similar queries answered instantly from cache
- Prompt compression — reduce token usage before requests are sent
- Smart routing — send each app to the most cost-effective model for its needs
- Budget controls — set per-application spend limits and alerts
Repetitive workloads commonly cut AI API costs by 60–80% — without touching apps that don't need it.
Example Application Profiles
The same three building blocks combine into very different packages. Here's how four common AI applications each get a profile tuned to their needs.
Customer Support Assistant
Security: strict PII masking + content filtering
MCP: knowledge base + ticketing
Tokens: aggressive semantic caching for repetitive questions
Developer Copilot
Security: secret & source-code redaction + injection defense
MCP: repository & docs search
Tokens: prompt compression + routing to a coding model
Finance & Analytics
Security: financial-data masking + full audit trail
MCP: database + spreadsheet access
Tokens: smart routing to a cost-effective model
Public-Facing Chatbot
Security: jailbreak defense + output moderation
MCP: limited FAQ tools only
Tokens: semantic cache + cheapest capable model
Same platform, four profiles. Each application connects to its LLM with the exact combination of security, tools, and cost controls it needs — and you can adjust any profile without affecting the others.
Business Impact
The difference between one blunt policy and a profile tuned to every application.
One Policy for Every App
Organizations typically face:
- Over-restricted apps that can't do their job
- Under-protected apps that leak sensitive data
- Every app granted the same broad tool access
- Cost savings left on the table for some workloads
- Changing one policy risks breaking every app
- Vendor lock-in to a single model or provider
A Profile per Application
Organizations gain:
- Security tuned to each application's real risk
- Least-privilege MCP tool access per app
- Optimization matched to each workload's tokens
- Independent changes — no cross-app breakage
- Per-application usage, spend, and audit visibility
- Freedom to route each profile to any LLM
Every AI application becomes a governed, right-sized package — connected to the best LLM for the job.
ROI Illustration
Token-optimization rules are tuned per application, so savings come from the workloads that benefit most. For an organization spending approximately USD $50,000 / month on AI APIs, typical savings break down as follows:
Semantic Caching
Prompt Compression
Smart Routing
Total Potential Savings
Illustrative estimate based on repetitive enterprise workloads. Actual savings vary by application, usage patterns, and profile configuration.
Deployment
Wag-Tail deploys as a lightweight layer within your existing infrastructure. No major application redesign is required — point each application at its Wag-Tail profile endpoint, and its security, MCP, and token-optimization rules become active immediately.
On-Premise
Run entirely within your own data center for maximum control over data and infrastructure.
Private Cloud
Deploy in your private cloud environment with full isolation and governance.
Hybrid
Combine on-premise and cloud deployments to match your operational and compliance needs.
Docker-Based
Ship as a containerized layer with Docker for fast, repeatable deployment anywhere.
Fast adoption. Multi-provider routing and automatic failover underpin every profile, so applications stay available even when a single provider degrades.
Executive Summary
Wag-Tail is an AI Application–Centric gateway. Rather than one policy stretched across every use case, it lets organizations build a dedicated profile for each AI application from three building blocks — security rules, MCP components, and token-optimization rules — and connect each one to the best LLM for the job.
The result: every AI application is protected, equipped, and optimized for exactly what it does — governed centrally, tuned individually.
Ready to give every AI application its own profile?