1
Dashboards take too long to build
Teams often need answers before a BI project can be scoped, modeled and shipped.
Early validation — shaping the first pilot cohort · Self-hosted · Provider-agnostic
Secure data access for AI agents and internal analytics assistants.
AgentDataGateway is a governed layer between internal databases and LLMs. Connect multiple data sources in the same conversation, so your AI agent can reason across systems instead of answering from one silo.
Multi-source chat
Multiple sources at launch
Docker-first
Data stays in your infra
6 LLM providers
Including local vLLM
Read-only runtime
agentdatagateway · internalPostgreSQL BigQuery Guardrails active
Read-only Query logged
One chat can combine context from multiple sources, making the agent smarter than single-database chat.
What caused revenue to drop last month?
AgentDataGateway
OpenAI · governed runtimeRevenue fell 8.4% month over month. The biggest driver was enterprise expansion revenue. Trial-to-paid conversion also dropped for workspaces created after May 14 — I would look at enterprise renewals and the mid-May signup flow first.
Analysis details
Analytics insight
Revenue delta −8.4%
Main driver Enterprise expansion
Confidence High
2 sources · 12,840 rows · 5 steps
Schema inspected Read-only query generated Query executed Answer summarized Audit log written
Read-only query
select segment,
sum(revenue_delta) as impact
from finance.monthly_revenue
where month = 'last_month'
group by segment
order by impact asc
limit 5;Audit log
Policyread-only ✓
Timeout15 s
Rows scanned12,840
ProviderOpenAI
Only read-only queries. No credentials exposed. Every answer is logged.
Ask anything across selected data sources…
Ask securely
The problem
Internal data is becoming agent-accessible before it is agent-safe.
Teams want faster answers than traditional BI workflows allow, but the current shortcuts create real risk.
2
Raw SQL access does not scale safely
Giving every AI tool credentials or broad SQL access creates policy, audit and reliability gaps.
3
Copying data into AI tools is unsafe
Sensitive internal context should not be pasted manually into third-party chat windows.
4
Agents need governed data access
MCP-style workflows, n8n agents and custom AI apps need controls before they touch company data.
The solution
A secure gateway between internal databases and AI agents.
AgentDataGateway is an AI-native analytics layer and controlled runtime for asking questions over company data. The powerful part is multi-source context: chat with different databases at the same time and give the agent enough context to produce much smarter answers.
Query guardrails Provider agnostic Audit trail
Do not expose raw credentials
Agents connect through the gateway, not directly through shared database passwords.
Control generated SQL
Use read-only users, limits, timeouts and future policy checks to keep query execution bounded.
Serve humans and agents
Support chat interfaces today while preparing for MCP-style workflows and internal tools.
Answer before dashboards
Get fast internal data exploration without claiming to replace every BI workflow.
How it works
Four steps from internal database to governed AI answer.
The validation focus is a practical workflow technical teams can pilot with one real source and one real provider.
Step 1
Connect a read-only data source
Start with PostgreSQL, then expand toward warehouses, document databases, graph databases and vectors.
Step 2
Choose your LLM provider
Use OpenAI, Anthropic, Gemini, Grok, local vLLM or a custom OpenAI-compatible endpoint.
Step 3
Ask questions in natural language
Select the sources the agent may access before the chat starts.
Step 4
Review answers, queries and logs
Inspect generated queries, responses, timeouts, limits and agent activity after every run.
Features
Built for technical teams validating agentic BI.
The landing page validates the demand. The product direction is a secure, auditable and provider-agnostic access layer for real internal workflows.
Secure data source connections
Encrypted credentials, source-level controls and read-only database users.
Chat with company data
A ChatGPT-like analytics experience for internal operational questions.
Multi-provider LLM support
Provider-agnostic by design, from hosted APIs to local models.
Query logging and audit trail
Track generated queries, responses, timing, limits and agent activity.
Self-hosted deployment
Docker-first deployment for teams that keep database traffic in their own environment.
Read-only access by design
Designed around safe defaults, query guardrails, timeouts and execution limits.
Agent-ready architecture
Expose governed data access to internal assistants and agentic workflows.
Future MCP and n8n support
A foundation for MCP-style tools, n8n agents and custom internal AI apps.
Data sources to validate
PostgreSQL firstMongoDBMySQLMariaDBBigQueryNeo4jCosmosDBVector databasesMore sources later
LLM providers to validate
OpenAIAnthropicGoogle GeminiGrokCustom OpenAI-compatible endpointsLocal and self-hosted models via vLLM or similar
Use cases
For teams already experimenting with LLMs, agents and internal data.
The strongest fit is technical teams that need answers from company data without giving every assistant direct database access.
Internal AI data assistant
AI-native analytics
Operational reporting
Agent access to databases
Data exploration without dashboards
Reducing ad-hoc SQL request queues
AI analytics for SaaS company data
Deployment
Self-hosted first, because internal data traffic is sensitive.
The validation question is not only whether teams want AI analytics. It is where they are willing to run it, and what security posture they require.
Recommended
Self-hosted Docker
Likely the first serious deployment model. Keep database traffic, credentials and logs inside your controlled environment.
Share preferenceLicensed on-prem
For companies that need custom security review, private networking, enterprise controls and license-key deployment.
Share preference PlannedFuture managed SaaS
A lighter option may come later, but many teams will prefer self-hosting for internal database traffic.
Share preferenceValidation and pilot intent
Help shape the first serious deployment path.
No fake pricing, no final packaging. The goal is to understand willingness to pay, pilot budgets and what security requirements would make this real.
Early Access
Join the waitlist
Join the validation list, share your stack and help shape the first PostgreSQL workflow.
Join waitlistSelf-hosted Pilot
Pilot interest
For technical teams that want to test one database, one provider and real internal questions.
Request pilotEnterprise / On-prem
Talk to us
For custom deployment, security review, private networking and strict internal governance needs.
Talk to usLead capture
Tell us what would make this worth testing.
Useful validation is specific: database type, preferred deployment, LLM provider, security posture and whether there is a real pilot budget.
What this will not ask for
Do not submit credentials, connection strings, API keys or sensitive company data. The form only captures interest and requirements.
Common questions
Straight answers for early technical evaluators.
This is an honest validation page for a product concept, not a finished dashboard or backend.
Is this a finished product?
No. AgentDataGateway is being validated as a serious B2B technical product. This landing page is designed to measure demand, pilot interest and buying intent before the full application is built.
Where does my data go?
The intended direction is controlled access to internal company data, with self-hosted Docker as the likely first serious deployment model. The goal is to avoid manually copying sensitive data into public AI tools.
Is this SaaS or self-hosted?
Self-hosted is the main deployment being validated because companies may not want database traffic routed through a third-party SaaS. A managed SaaS option may be considered later.
Which database is supported first?
PostgreSQL is the first priority. Other sources under consideration include MongoDB, MySQL, MariaDB, BigQuery, Neo4j, CosmosDB and vector databases.
Can I use my own LLM provider?
That is the plan. The concept is provider-agnostic, including OpenAI, Anthropic, Gemini, Grok, custom OpenAI-compatible endpoints and local models via vLLM or similar.
Is this a Power BI replacement?
Not exactly. It is positioned as a faster, AI-native alternative for teams that need answers from internal data before building dashboards, not as a complete replacement for mature BI workflows.
Can AI agents execute unsafe queries?
The intended design emphasizes read-only database users, guardrails, query audit logs, timeouts and limits. The goal is governed access, not uncontrolled SQL execution.
How do I join the pilot?
Use the form below and choose Technical pilot or Enterprise/on-prem. Include the data sources, LLM providers and deployment model you would need for a useful test.
Get started
Validate governed AI access to your internal data.
Request early access, or share enough technical context for a self-hosted pilot conversation.