01 — Knowledge Load
Retrieval & Grounding
How much data must the system know? Indexing 50 PDFs requires vastly different vector database infrastructure and chunking strategies than indexing a live SQL database.
Grounded AI &
Intelligent Support Pricing
We engineer intelligence, we do not train magic. This pricing framework is forhallucination-free systemsanchored to your business data—designed to assist, route, and reason with strict engineering constraints.
Next.js 16+ Route HandlersRetrieval Augmented Generation (RAG)Server-Side Security
The Physics of Intelligence Cost
The price of a grounded AI system is not determined by its personality, but by its architectural load. We calculate cost based on three immutable engineering vectors: Knowledge, Capability, and Control.
02 — Action Capability
Tool Use & Routing
What can the system do? A read-only bot is low-risk. An agent that can call APIs, book meetings, or route support tickets requires complex function-calling architecture.
03 — Safety Engineering
Guardrails & Compliance
How strict is the supervision? High-stakes domains (legal, medical) require adversarial testing and output validation layers to mathematically guarantee hallucination control.
Grounded AI & Intelligent Support
Knowledge Base & Grounding
Define the volume of proprietary data the AI must reason over. Higher volumes require more sophisticated vector indexing and retrieval strategies.
Document Volume
In blocks of 1001001 Block Indexed
025005,000
Agent Capability & Actions
Determine how the AI interacts with your business systems. Move from passive information retrieval to active task execution.
Action Power
Safety & Risk Mitigation
Configure the guardrail architecture for your AI agent. High-stakes or public-facing systems require rigorous adversarial testing to prevent hallucinations and misuse.
Guardrail Level
Where the Budget Goes
We price strictly on engineering load. Your budget funds the construction of three specific architectural layers required to make AI safe, accurate, and functional.
Infrastructure Layer
Vector Database Engineering
Documents are not just "uploaded." They are parsed, chunked, embedded into high-dimensional vectors, and indexed in Supabase or Pinecone. This ensures the AI retrieves exact paragraphs, not vague summaries.
- PDF/SQL Parsing
- Embedding Pipeline Setup
Capability Layer
Function Calling & Routing
If the AI takes action (booking meetings, updating CRMs), we engineer deterministic "Tools" (API schemas) that the model can invoke. This includes error handling, rate limiting, and permission checks.
- Secure API Schemas
- Transaction Rollbacks
Control Layer
Adversarial Guardrails
We write code that validates the AI's output before the user sees it. This includes PII stripping, competitor name blocking, and "jailbreak" prevention to ensure brand safety.
- Output Filtering
- Red-Team Testing
The Invisible Floor
Every AI interface we ship includes these non-negotiable architectural standards. We do not dilute security to lower the price.
- Zero-Retention Logging
- We configure LLM providers (OpenAI/Anthropic) to zero-retention mode by default. Your business data is processed for inference only and never used to train public models.
- Streaming Latency
- We implement React Server Components (RSC) streaming to deliver the first token in under 200ms. Users never stare at a spinning loader while the model "thinks."
- Type-Safe Prompts
- We treat prompts as code. All system instructions are version-controlled, typed with TypeScript, and tested against regression suites to prevent silent degradation.
- Server-Side Isolation
- API keys and vector logic live exclusively on the server (Next.js Route Handlers). Client-side code never touches your credentials or raw index data.
Pillar IV: Grounded AI Pricing FAQs
Questions about AI integration, RAG systems, grounding, security, and costs.
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