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.
The AI Market in 2026: Massive Spending, Modest Returns
Global AI spending hit $2.52 trillion this year. Enterprise adoption is at 78%. But only 39% of organizations report meaningful impact on their bottom line — and just 5% have deeply embedded AI into core revenue workflows. The organizations seeing real ROI ($3.70-$10.30 per dollar invested) aren't spending more on API licenses. They're investing 70% of their AI budgets in process redesign and change management. That's exactly where we operate.
78%
Enterprise AI adoption
39%
Report significant ROI
~5%
Deeply embedded in revenue
What We Deliver
Multi-LLM Orchestration & Failover Architecture
Enterprise AI demands more than one model. With inference costs dropping 10x per year and new providers emerging quarterly, the right architecture routes each task to the optimal model — Claude for deep reasoning, Flash models for high-volume classification, GPT for embeddings — with automatic failover when any provider goes down. Our production platform does exactly this with cost-aware selection across Anthropic, OpenAI, and Google.
Live on databusiness.ai — multi-LLM orchestration with zero-downtime provider failover.
Token Optimization & Cost Engineering
Total inference spending grew 320% last year as AI moved from experiments to always-on agents. We invented the PTC (Programmatic Tool Calling) architecture — a pattern that reduces token consumption by 85-99% by replacing verbose MCP tool calls with compact CLI operations. Five production PTC systems run daily. In an era where enterprises are routing 60-80% of tasks to small language models for 10-30x savings, we've been engineering cost efficiency since before it was trendy.
97% token reduction on Wrike. 99% on Microsoft 365. Ahead of the hybrid routing curve.
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. Unlike MCP's stateless tool calls, RAG provides deep contextual grounding that prevents the hallucinations plaguing most enterprise deployments.
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 has exploded to 10,000+ community servers and 97 million SDK downloads — adopted by every major AI provider and now governed by the Linux Foundation. We were building production MCP servers before the ecosystem went mainstream. Our published 23-tool M365 server and 26-tool Excalidraw server on NPM aren't experiments — they're the infrastructure our daily operations run on.
M365 MCP Server: 23 tools, MSAL OAuth 2.0, multi-account support — running in production since 2024.
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
Claude Opus 4.6
Primary LLM — 1M context, agent orchestration, deep reasoning
Claude 3.7 Sonnet
Production workloads — hybrid reasoning at $3/$15 per 1M tokens
OpenAI GPT-4o / o3-mini
Failover LLM, embeddings, fast reasoning tasks
Google Gemini Pro
Large-context analysis (2M tokens), sub-agent delegation
Qdrant + pgvector
Vector storage for semantic search (1536-dim, cosine distance)
MCP Protocol
10,000+ ecosystem servers — we build custom ones for your systems
Claude Code
80.9% SWE-bench — our primary dev environment with 62 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
62 AI skills. 5 PTC systems. 3 MCP servers. 49 projects. This isn't our first deployment — it's our sixty-second.
Frequently Asked Questions
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.