Chain-of-Thought
Multi-step reasoning
To calculate churn for a portfolio, it understands it must extract usage data, apply recency/frequency formula, cross-check with NPS. It finds its own path.
The first wave of AI was about generating content. But executives don't pay salaries for pretty text — they pay for solving complex problems. The MDA LLM's reasoning engine pauses, analyzes the context, breaks it into logical steps, and validates its own conclusion before responding.
Why not 8B (too shallow) or 70B+ (expensive and slow)? Qwen 3.6 32B is the Goldilocks zone — perfect balance between reasoning depth, speed, and cost.
To calculate churn for a portfolio, it understands it must extract usage data, apply recency/frequency formula, cross-check with NPS. It finds its own path.
Forget the 8k or 32k limits that lose context. Hundreds of pages, an entire ledger, a customer's entire ticket history — all at once, with coherence from start to finish.
It has 32 billion parameters but activates only ~3.3B per token. Result: heavyweight reasoning with lightweight speed and drastically lower GPU cost.
256k tokens = ~500 pages of context processed simultaneously — equivalent to reading an entire ledger, a complete legacy codebase, or a customer's entire account history before responding.
It's not enough to take an open-source model and host it. What makes Qwen 3.6 32B an MDA LLM is the engineering around it — so it reasons like your business requires, not like a generic student.
Reasoning without real data is expensive hallucination. The model only draws conclusions from your Data Lake, CRMs, ERPs ingested via RAG and MCP connectors. If it doesn't know, it says so — zero fabrications.
The MDA LLM doesn't just think — it acts. It decides which tool to use: "to answer about customer X's billing, I need to run a SQL query on Snowflake via the BI Agent". Reasoning triggers the right tool.
For 256K + complex reasoning to be financially viable, we run with FP8 quantization via vLLM. Your CTO pays for real computation, not wasted VRAM. Throughput above 30 TPS even under high concurrency.
The 256K window solves immediate context. But the MDA ecosystem adds long-term memory via vector databases — the agent remembers what it discussed with the customer in prior interactions, maintaining logical continuity of business reasoning.
In all three, the difference isn't "prettier text" — it's actionable executive summary ready for the board.
Analyze the performance of 300 products cross-referencing NPS, engagement, and historical financial data.
The LLM ingests the entire database (256K), applies statistical formulas, and returns: "Product Y has a retention gap in phase Z. Suggested action: cross-sell with product W."
Understand a codebase of thousands of lines to refactor or find a systemic bug.
Dev uploads Python files, DB schema, and error logs. The 256K model maps all function dependencies, reasons about data flow, and generates the fix with surgical precision.
Approach a high-value lead by reading tenders, annual reports, and sales history before the call.
The SDR agent processes the entire lead context in 256K, reasons about approach, and conducts negotiation via voice or WhatsApp with senior consultant depth — not like a bot.
Turning a powerful LLM (Qwen 32B) into an enterprise reasoning engine requires more than an API — it requires data architecture, advanced prompt engineering, RAG, and GPU infra tuning.
MDA Consulting deploys your Private Reasoning Engine: we structure your data, calibrate the model to your business rules, and guarantee processing costs far lower than you'd pay in dollars to OpenAI.