Fewer input tokens
You don't pay for AI to read unnecessary JSON integration schemas for that task. The context sent goes straight to the point.
Connect your data, build intelligent workflows, and deploy autonomous agents on private infrastructure. All in one ecosystem — designed for CTOs, CIOs, and software architects who need to turn AI into real productivity.
Why does MyDatAgent deliver more ROI than using LLMs solo? Because we use the best orchestration frameworks on the market to create agents that reason, use tools, and interact with each other.
Task delegation where multiple agents assume roles — Researcher + Writer + Reviewer — and collaborate toward a final goal.
The backbone of RAG and tool-calling workflows. We connect AI to the real world — banks, APIs, internal systems.
Direct communication between agents in the ecosystem. The SDR Agent talks to the BI Agent to qualify leads with predictive data in real time.
The visual bridge for workflow automation. The agent resolves a ticket → n8n updates Jira and fires a Slack message. Zero fragile code.
The result: we leave GPT for chat only. MyDatAgent is a productivity engine that operates your company's systems.
AI is only useful if it has access to the right data and systems. But how you connect AI defines not just its capability, but also your operating costs. MyDatAgent offers two complementary integration routes: the power of autonomy with MCP and the extreme efficiency with CLI. You choose the best architecture for each business scenario.
The gold standard for giving your AI Agents "hands and eyes". Instead of just generating text, AI actively interacts with systems, deciding which tool to use at the exact moment. When to use: conversational interactions, autonomous agent workflows (A2A), tasks where AI needs to reason about which tool to call next.
The DataOps Agent reads the error in Sentry, fetches the log in GitHub, and opens a PR with the fix.
The SDR Agent sends the message via Twilio and notifies the team on Slack when the meeting is booked.
The BI Agent runs direct queries on Snowflake and PostgreSQL to generate the financial report in real time.
The agent reads product documentation in Notion and converts it into a knowledge base (RAG).
The Lead Scraper Agent navigates the web, validates data, and looks up business IDs in real time.
Governance, deployment, and observability directly in your VPC.
Financial reconciliation and e-commerce metrics analysis automated by AI.
Legacy systems, proprietary ERPs, or internal APIs: our MDA Consulting delivers the connector ready.
+ Custom tools and on-demand workflows · open MCP protocol · new connectors published monthly
Not every task needs the heavy machinery of MCP tool interpretation. Repetitive tasks, batch ETL, report generation, and direct calls don't require AI to "think" about which tool to use. By connecting your systems directly via command line or scripts, you cut the overhead of tool processing.
You don't pay for AI to read unnecessary JSON integration schemas for that task. The context sent goes straight to the point.
AI doesn't generate complex function calls — it just returns the processed data or ready code. No cascading invocations.
At high volume, using CLI instead of MCP can cut token consumption by over 40% — drastically reducing your monthly costs.
$ mda llm summarize --input contracts/*.pdf --batch --model mda-2.1
→ 1,000 contracts processed in 18 min · 12.4M tokens saved vs MCP
$ mda llm refactor --lang python --repo ./monorepo --rule "type all functions"
→ 847 files updated · PR opened · 0 wasted function callsWhen to use CLI: background processing, automated reports, DevOps scripts, batch refactoring — any task where speed and token cost are critical.
In practice, companies need both routes. The MyDatAgent AI Ecosystem orchestrates MCP and CLI under the same endpoint — total flexibility, optimized costs.
The SDR Agent reads HubSpot, decides which lead to approach, and sends a personalized message via Slack (reasoning + action).
At end of day, a script pulls all responses in batch, sends to MDA LLM 2.1 to summarize, and generates the executive report for the VP of Sales (speed + savings).
Total flexibility. Optimized costs. No public API gives you this level of architectural control.
Don't put your company's data on a third-party's cloud. The MyDatAgent AI Ecosystem is built on the architecture CTOs trust.
We run models like Qwen 3.5-35B with FP8 quantization via vLLM. High performance, optimized memory, and predictable per-token costs.
Ingestion and fine-tuning on your company's knowledge. AI responds with the truth of your data, not hallucinations from the public internet.
Your data in private cloud in Brazil (dedicated VPC). We don't train public models with your information. Immutable logs, tenant isolation, encryption at rest and in transit.
Virtual key management, logs, and routing via LiteLLM. You know exactly what each agent is doing — and how much each agent is costing, in real time.
AI engineering without mystery: clear architecture, well-defined roles, and KPIs your board understands.
Databases (Postgres, MongoDB, Snowflake), CRMs (HubSpot), and internal sources (Notion, Google Drive) enter the ecosystem via MCP. Our DataOps Agent cleans, normalizes, and structures everything for consumption.
CrewAI and LangChain define what the agent does: which tools it accesses, validation criteria, how it interacts with other agents. Everything orchestrated with guardrails and human review where needed.
The agent interacts via WhatsApp, updates Jira, runs SQL queries, and reports everything on Slack. You just track KPIs and cost per execution in the governance dashboard.
Building a multi-agent architecture that connects Jira, Slack, Postgres, and private LLMs requires expertise in data engineering and AI.
If your product-focused team can't spend months configuring infrastructure, MDA Consulting is the answer. We map your stack, design the integration architecture (MCP), choose the ideal frameworks (LangChain, CrewAI), and deploy your complete agent fleet.