AI Strategy from Engineers Who Ship, Not Consultants Who Theorize
Most AI consultancies hand you a strategy deck and wish you luck. We architect the systems, optimize the token economics, and stay in the codebase until it's running in production. Twenty-five years of enterprise development means we know where AI transforms a business — and where it's an expensive distraction.
What We Deliver
Multi-LLM Orchestration & Failover Architecture
We design systems that use the right model for the right task — and fail over gracefully when one provider goes down. Our production DataBusiness.ai platform routes between Claude, GPT-4, and Gemini with automatic failover, model-specific prompt optimization, and cost-aware selection.
Live on databusiness.ai — multi-LLM orchestration with zero-downtime provider failover.
Token Optimization & Cost Engineering
AI API costs can spiral fast. We invented the PTC (Programmatic Tool Calling) architecture — a pattern that reduces token consumption by 85-99% across API interactions. Five production PTC systems run daily against Wrike, Microsoft 365, Notion, Azure DevOps, and Pingdom.
97% token reduction achieved on Wrike API operations. 99% on Microsoft 365.
RAG Systems & Vector Search
Retrieval-Augmented Generation turns your business data into an AI knowledge base. We build RAG pipelines with OpenAI embeddings, Qdrant and pgvector for storage, and semantic search that actually understands context — not just keywords.
AI-Brain platform: vector-based semantic search across multi-provider email with 1536-dim OpenAI embeddings.
Custom MCP Server & AI Tooling Development
The Model Context Protocol is how modern AI assistants connect to your business systems. We build custom MCP servers — including a published 23-tool Microsoft 365 MCP server and a 26-tool Excalidraw MCP server on NPM.
M365 MCP Server: 23 tools, MSAL OAuth 2.0, multi-account support — running in production.
How We Work
Discovery & Audit
We start by understanding your current systems, data assets, and business goals. If you already have AI in play, we audit what's working, what's burning tokens, and what's producing hallucinations instead of value.
1-2 weeksArchitecture & Proof of Concept
We design the system architecture — model selection, prompt engineering patterns, data pipelines, integration points — and build a working proof of concept on real data. Not a demo with cherry-picked examples.
2-4 weeksProduction Build & Optimization
We build the production system with enterprise rigor: clean architecture, comprehensive error handling, structured logging, and monitoring. Then we optimize — token costs, latency, accuracy — until the numbers make sense.
4-12 weeksTools & Platforms We Use
Anthropic Claude
Primary LLM — code generation, analysis, agent orchestration
OpenAI GPT-4
Backup LLM, embeddings (text-embedding-3-small), failover
Google Gemini
Large-context analysis (1M+ tokens), sub-agent delegation
Qdrant
Vector storage for semantic search (1536-dim, cosine distance)
pgvector
Embedded vector search in PostgreSQL applications
MCP Protocol
Custom tool servers connecting AI agents to business systems
Claude Code
AI-augmented development environment (52+ custom skills)
Vercel AI SDK
Streaming response handling in Next.js applications
Typical Engagement
Timeline
8-16 weeks
Discovery through production
Team
Senior AI Architect
Your primary contact + managed dev team
Investment
Scoped after discovery
We don't quote without understanding the problem
52 AI skills. 5 PTC systems. 3 MCP servers. 49 projects. This isn't our first deployment — it's our fifty-second.
Let's Find Where AI Actually Fits in Your Business
One conversation. No pitch deck. We'll discuss your current systems, your goals, and whether AI is the right investment — or whether you'd be better served by solid engineering without the AI label.
Free 30-minute consultation. We'll tell you if AI is the wrong answer.