SVC—007 ● LIVE DATA ENGINE MAR 2026

DML
Brain
Engine

Central Database Intelligence

The core reasoning layer that fuels AI agents with real-time operational data. Routes every agent query through a hybrid decision layer — semantic vector search for conceptual retrieval, dynamically generated SQL for exact record lookup. Bypasses the shallow wrapper-agent pattern entirely by giving agents direct database intelligence.

Hybrid

VECTOR SEARCH

Live

SQL QUERIES

Real-time

DATA INJECTION

Dynamic SQLHybrid SearchVector DBAgentic AIPython

// UI PREVIEW — QUERY CONSOLE

DML BRAIN ENGINE Query Console

Engine online

QUERY INPUT

"Find all candidates with Python experience hired in Q1 2026 with salary above 80K"

ROUTEHYBRID

VECTORPython skills

SQLdate + salary filter

LATENCY38ms

RECORDS12 matched

INJECTED✓ to agent ctx

RETRIEVAL RESULTS

VECTOR SEARCH — semantic match

candidate_id: C-2041 · "5 years Python, FastAPI, AWS" candidate_id: C-1887 · "Python ML engineer, scikit-learn" candidate_id: C-2103 · "Python backend, Django, PostgreSQL"

GENERATED SQL — exact filter

SELECT * FROM candidates WHERE hire_date BETWEEN '2026-01-01' AND '2026-03-31' AND salary > 80000 AND id IN (C-2041, C-1887, C-2103, ...)

AGENT CONTEXT INJECTION

INJECTED_RECORDS

12 candidates matched hybrid criteria

TOP_MATCH

C-2041: Priya R. · 92% semantic score · salary 95K

SQL_VERIFIED

All 12 records confirmed via exact SQL match

CONTEXT_SIZE

2.1K tokens injected into agent prompt

AGENT_READY

✓ Context loaded — agent proceeding

02 // ARCHITECTURE

The Pipeline

AI
AGENT

→

DML Brain
Router

→

Vector
Search

SQL
Generator

→

Operational
DB

→

AGENT
CONTEXT

STEP 01

Query Ingestion

Agent submits a natural language query to the DML Brain. Query intent classified: semantic, exact, or hybrid retrieval mode.

STEP 02

Hybrid Routing

Router determines the retrieval strategy. Conceptual queries go to vector search; filter/aggregate queries go to SQL generator; complex queries use both.

STEP 03

Vector Retrieval

Natural language query embedded and searched against the operational vector index. Top-k semantically relevant records surfaced.

STEP 04

SQL Generation

LLM dynamically generates a SQL query from natural language. Query validated, sanitized, and executed against the live operational database.

STEP 05

Context Injection

Results from both layers merged and deduplicated. Structured context injected directly into the agent's prompt — grounding it in live data.

03 // STACK

Built with

ROUTING

Hybrid Search Layer

Custom intent classifier decides whether to invoke vector search, SQL generation, or both — based on query structure and keyword signals.

SEMANTIC

Vector DB

Operational data embedded and indexed for semantic similarity search. Enables conceptual queries that exact SQL cannot express.

EXACT RETRIEVAL

Dynamic SQL Engine

LLM translates natural language filters into validated SQL. Parameterized queries prevent injection. Handles joins, aggregations, and date ranges.

GROUNDING

Context Injection

Merged results formatted as structured context and injected into agent system prompt. Agents answer from live data — not stale training knowledge.

RUNTIME

Agentic AI Framework

Integrates with any LangChain or custom agent as a tool. Agents call the DML Brain like a function — query in, enriched context out.

BACKEND

Python

Core engine implemented in Python. Async query execution for parallel vector + SQL retrieval. Results merged in under 50ms for most queries.

DMLBrainEngine

The Pipeline

Built with

DML
Brain
Engine