Most enterprise questions that actually matter — "what is the impact of this new policy on revenue and customer satisfaction?", "how is the new welfare scheme affecting citizen outcomes across districts?", "are our employees more or less engaged after the return-to-office mandate?" — never get answered properly. Not because the data isn't there. The data is in 14 different systems: SQL warehouses, the ERP, Salesforce, HR systems, survey platforms, file shares, PDF policy documents. Answering well requires pulling all of it together, cross-referencing, controlling for confounds, and writing it up.
That work, in a legacy stack, takes months. An analyst opens a JIRA ticket. A developer integrates a new BI source. ETL gets written. Dashboards are wired up. Analysts compile, interpret, and write. By the time the report lands on the executive's desk, the policy has been in effect for two quarters and the question has shifted.
Citra Deep Analytics Chat collapses that timeline to hours — by replacing the human pipeline with a long-running agentic research engine that does heuristic deep search across every connected enterprise system over MCP.
Citra Has Three Chat Modes — Here's When to Use Which
Quick Chat
Upload a file (or a few) and ask. Best for one-off Q&A, document summarization, ad-hoc extraction. Seconds to first answer.
Enterprise Chat
Fast AI chat over your governed Vault and the web. Cited answers across departments. Ideal for daily knowledge work and grounded Q&A in seconds.
Deep Analytics Chat
Long-running, sandboxed agentic deep-research analyst. Heuristic investigation across every system. Decision-ready impact reports in hours.
Quick Chat and Enterprise Chat are real-time tools. Deep Analytics Chat is something different — it is an autonomous research process that runs for hours, plans its own investigation, and produces a finished report.
What Deep Analytics Chat Actually Does
You ask one complex business question. Behind the scenes, a long-running agent does the work of a full analyst team:
Plan
Decompose the question into sub-questions, hypotheses, and required evidence.
Discover
Identify which connected systems have relevant data via MCP — SQL, ERP, sales, HR, files.
Fetch
Auto-generate queries, pull rows, parse PDFs, sample documents, scrape allowed sources.
Analyze
Run heuristic deep search — segment, correlate, control for confounds, test sub-hypotheses.
Synthesize
Pull findings into a structured narrative with charts, tables, and citations to source rows and documents.
Deliver
Decision-ready report — exportable as a presentation, document, or dashboard.
The whole loop runs autonomously. You can close your laptop. When you come back, the report is ready, every claim is cited, and every chart is reproducible.
The shift in mental model
Stop thinking about "asking AI a question" and start thinking about tasking an AI analyst. Deep Analytics Chat is not a chatbot — it is a sandboxed research operative that takes a brief, runs the investigation, and ships a report.
Real Use Cases
Use Case 1 — Policy Impact on Company Performance
Did our new return-to-office policy actually improve performance?
What the agent does: Pulls HR data from Workday, attrition logs from the HRMS, eNPS survey results from a file share, output metrics (commits, tickets, deals closed) from Jira / Salesforce / GitLab, revenue per FTE from finance dashboards in SQL. Controls for seasonality and tenure mix. Cites every metric back to source rows.
Use Case 2 — Citizen Welfare Impact (Government)
Is the new welfare scheme actually improving citizen outcomes?
What the agent does: Runs queries on the disbursement SQL system, pulls school enrolment data from the education department's API, ingests health records from the public hospital data lake, parses 200+ field-officer PDFs for qualitative signal, runs differential analysis between pilot and control districts.
Use Case 3 — Employee Sentiment on a Policy Change
Are employees actually happy with the new performance-review framework?
What the agent does: Reads survey CSVs, ingests internal forum exports, parses anonymized 1:1 note PDFs, runs sentiment + theme extraction, segments by cohort, surfaces manager-level outliers, cites verbatim quotes (with consent flags respected).
Use Case 4 — Sales & ERP Cross-System Investigation
Why is gross margin in the southern region down 4 points QoQ?
What the agent does: Joins data from SAP (cost), Salesforce (discount approvals and deal notes), the freight TMS (transport cost per shipment), and partner contracts from a SharePoint folder. Decomposes the margin walk and ranks drivers.
Use Case 5 — Compliance & Risk Scan
Where are we exposed to the new regulation that drops next quarter?
What the agent does: Reads the regulation PDF, parses 1,400 vendor contracts from the contract management system, pulls data-flow inventory from the architecture wiki, cross-references customer regions from the CRM, ranks gaps.
Hours, Not Months — The Productivity Math
| Phase | Legacy Reporting Stack | Citra Deep Analytics Chat |
|---|---|---|
| Question intake & scoping | 1–2 weeks (analyst meetings, JIRA tickets) | Minutes (one prompt) |
| Data integration | 4–8 weeks (developer integrates BI sources, writes ETL) | Already done — MCP connectors are pre-attached |
| Dashboarding | 2–4 weeks (BI engineer builds reports) | Auto-generated inline charts & tables |
| Analysis & write-up | 2–6 weeks (analysts segment, interpret, write) | Hours (agent writes cited narrative) |
| Total wall-clock | 2–6 months | 2–8 hours |
| Estimated human hours saved per study | — | 400–1,200 analyst-hours |
The compounding effect is the real story. When a single impact study takes hours, executives stop rationing them. Suddenly every policy decision, every spend approval, every pricing change gets a rigorous, cited, agentic-research backed brief. Visibility into company performance and impact goes up by an order of magnitude, and decisions stop being driven by gut.
"We used to commission two impact studies a quarter. With Deep Analytics Chat we are running 30 a quarter and the leadership team finally has the visibility they always wanted." — anonymized customer, financial services
Why It Works — The Architecture in One Paragraph
Deep Analytics Chat is built on three Citra primitives. MCP & Dept Data Flow give the agent governed, scoped access to every enterprise system — SQL, ERP, sales, HR, file servers — without ever exfiltrating data. Long-running agentic orchestration lets a single conversation persist for hours, with checkpointing, retries, and audit logging across every tool call. Heuristic deep search means the agent doesn't just answer the question — it generates and tests sub-hypotheses, controls for confounds, cross-checks sources, and rewrites its plan when evidence shifts. The whole stack runs inside your sovereign Citra deployment on open-source models — air-gapped, on-prem, fully owned.
Frequently Asked Questions
What is Citra Deep Analytics Chat?
A long-running agentic research engine inside Citra. You ask a complex business question and an autonomous agent plans, fetches, analyzes, and writes a decision-ready report — pulling from every connected system via MCP.
How is it different from Quick Chat and Enterprise Chat?
Quick Chat is upload-and-ask. Enterprise Chat is real-time AI chat over governed data. Deep Analytics Chat is a long-running agentic deep-research analyst — it spends hours autonomously to produce reports that traditionally take weeks.
How does Citra connect to enterprise systems?
MCP and Dept Data Flow attach scoped, governed connectors to SQL, MongoDB, ERPs (SAP, Oracle, NetSuite), Salesforce, HubSpot, file servers, S3, REST APIs, and document stores. Full audit trail, zero data egress.
How long does a Deep Analytics report take?
Typically 2–8 hours of agentic runtime, replacing 2–6 months of human integration and analyst work and saving 400–1,200 analyst-hours per study.
Is it safe for regulated industries?
Yes. The agent runs entirely inside your sovereign deployment on open-source models. Every tool call, query, and reasoning step is logged. Data never leaves your perimeter — fit for banking, healthcare, government, and defense.